text
stringlengths 31
361k
| type
stringclasses 6
values | start
int64 125
418k
| end
int64 325
418k
| depth
int64 0
8
| filepath
stringclasses 103
values | parent_class
stringclasses 106
values |
|---|---|---|---|---|---|---|
class EvalResult:
"""
Flattened representation of individual evaluation results found in model-index of Model Cards.
For more information on the model-index spec, see https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1.
Args:
task_type (`str`):
The task identifier. Example: "image-classification".
dataset_type (`str`):
The dataset identifier. Example: "common_voice". Use dataset id from https://hf.co/datasets.
dataset_name (`str`):
A pretty name for the dataset. Example: "Common Voice (French)".
metric_type (`str`):
The metric identifier. Example: "wer". Use metric id from https://hf.co/metrics.
metric_value (`Any`):
The metric value. Example: 0.9 or "20.0 ± 1.2".
task_name (`str`, *optional*):
A pretty name for the task. Example: "Speech Recognition".
dataset_config (`str`, *optional*):
The name of the dataset configuration used in `load_dataset()`.
Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info:
https://hf.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
dataset_split (`str`, *optional*):
The split used in `load_dataset()`. Example: "test".
dataset_revision (`str`, *optional*):
The revision (AKA Git Sha) of the dataset used in `load_dataset()`.
Example: 5503434ddd753f426f4b38109466949a1217c2bb
dataset_args (`Dict[str, Any]`, *optional*):
The arguments passed during `Metric.compute()`. Example for `bleu`: `{"max_order": 4}`
metric_name (`str`, *optional*):
A pretty name for the metric. Example: "Test WER".
metric_config (`str`, *optional*):
The name of the metric configuration used in `load_metric()`.
Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
metric_args (`Dict[str, Any]`, *optional*):
The arguments passed during `Metric.compute()`. Example for `bleu`: max_order: 4
verified (`bool`, *optional*):
Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
verify_token (`str`, *optional*):
A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
source_name (`str`, *optional*):
The name of the source of the evaluation result. Example: "Open LLM Leaderboard".
source_url (`str`, *optional*):
The URL of the source of the evaluation result. Example: "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard".
"""
# Required
# The task identifier
# Example: automatic-speech-recognition
task_type: str
# The dataset identifier
# Example: common_voice. Use dataset id from https://hf.co/datasets
dataset_type: str
# A pretty name for the dataset.
# Example: Common Voice (French)
dataset_name: str
# The metric identifier
# Example: wer. Use metric id from https://hf.co/metrics
metric_type: str
# Value of the metric.
# Example: 20.0 or "20.0 ± 1.2"
metric_value: Any
# Optional
# A pretty name for the task.
# Example: Speech Recognition
task_name: Optional[str] = None
# The name of the dataset configuration used in `load_dataset()`.
# Example: fr in `load_dataset("common_voice", "fr")`.
# See the `datasets` docs for more info:
# https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
dataset_config: Optional[str] = None
# The split used in `load_dataset()`.
# Example: test
dataset_split: Optional[str] = None
# The revision (AKA Git Sha) of the dataset used in `load_dataset()`.
# Example: 5503434ddd753f426f4b38109466949a1217c2bb
dataset_revision: Optional[str] = None
# The arguments passed during `Metric.compute()`.
# Example for `bleu`: max_order: 4
dataset_args: Optional[Dict[str, Any]] = None
# A pretty name for the metric.
# Example: Test WER
metric_name: Optional[str] = None
# The name of the metric configuration used in `load_metric()`.
# Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
# See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
metric_config: Optional[str] = None
# The arguments passed during `Metric.compute()`.
# Example for `bleu`: max_order: 4
metric_args: Optional[Dict[str, Any]] = None
# Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
verified: Optional[bool] = None
# A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
verify_token: Optional[str] = None
# The name of the source of the evaluation result.
# Example: Open LLM Leaderboard
source_name: Optional[str] = None
# The URL of the source of the evaluation result.
# Example: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
source_url: Optional[str] = None
@property
def unique_identifier(self) -> tuple:
"""Returns a tuple that uniquely identifies this evaluation."""
return (
self.task_type,
self.dataset_type,
self.dataset_config,
self.dataset_split,
self.dataset_revision,
)
def is_equal_except_value(self, other: "EvalResult") -> bool:
"""
Return True if `self` and `other` describe exactly the same metric but with a
different value.
"""
for key, _ in self.__dict__.items():
if key == "metric_value":
continue
# For metrics computed by Hugging Face's evaluation service, `verify_token` is derived from `metric_value`,
# so we exclude it here in the comparison.
if key != "verify_token" and getattr(self, key) != getattr(other, key):
return False
return True
def __post_init__(self) -> None:
if self.source_name is not None and self.source_url is None:
raise ValueError("If `source_name` is provided, `source_url` must also be provided.") | class_definition | 248 | 7,185 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def unique_identifier(self) -> tuple:
"""Returns a tuple that uniquely identifies this evaluation."""
return (
self.task_type,
self.dataset_type,
self.dataset_config,
self.dataset_split,
self.dataset_revision,
) | function_definition | 6,067 | 6,362 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | EvalResult |
def is_equal_except_value(self, other: "EvalResult") -> bool:
"""
Return True if `self` and `other` describe exactly the same metric but with a
different value.
"""
for key, _ in self.__dict__.items():
if key == "metric_value":
continue
# For metrics computed by Hugging Face's evaluation service, `verify_token` is derived from `metric_value`,
# so we exclude it here in the comparison.
if key != "verify_token" and getattr(self, key) != getattr(other, key):
return False
return True | function_definition | 6,368 | 6,980 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | EvalResult |
for key, _ in self.__dict__.items():
if key == "metric_value":
continue
# For metrics computed by Hugging Face's evaluation service, `verify_token` is derived from `metric_value`,
# so we exclude it here in the comparison.
if key != "verify_token" and getattr(self, key) != getattr(other, key):
return False | for_statement | 6,573 | 6,960 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | EvalResult |
if key == "metric_value":
continue | if_statement | 6,622 | 6,672 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | EvalResult |
if key != "verify_token" and getattr(self, key) != getattr(other, key):
return False | if_statement | 6,860 | 6,960 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | EvalResult |
def __post_init__(self) -> None:
if self.source_name is not None and self.source_url is None:
raise ValueError("If `source_name` is provided, `source_url` must also be provided.") | function_definition | 6,986 | 7,185 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | EvalResult |
if self.source_name is not None and self.source_url is None:
raise ValueError("If `source_name` is provided, `source_url` must also be provided.") | if_statement | 7,027 | 7,185 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | EvalResult |
class CardData:
"""Structure containing metadata from a RepoCard.
[`CardData`] is the parent class of [`ModelCardData`] and [`DatasetCardData`].
Metadata can be exported as a dictionary or YAML. Export can be customized to alter the representation of the data
(example: flatten evaluation results). `CardData` behaves as a dictionary (can get, pop, set values) but do not
inherit from `dict` to allow this export step.
"""
def __init__(self, ignore_metadata_errors: bool = False, **kwargs):
self.__dict__.update(kwargs)
def to_dict(self):
"""Converts CardData to a dict.
Returns:
`dict`: CardData represented as a dictionary ready to be dumped to a YAML
block for inclusion in a README.md file.
"""
data_dict = copy.deepcopy(self.__dict__)
self._to_dict(data_dict)
return {key: value for key, value in data_dict.items() if value is not None}
def _to_dict(self, data_dict):
"""Use this method in child classes to alter the dict representation of the data. Alter the dict in-place.
Args:
data_dict (`dict`): The raw dict representation of the card data.
"""
pass
def to_yaml(self, line_break=None, original_order: Optional[List[str]] = None) -> str:
"""Dumps CardData to a YAML block for inclusion in a README.md file.
Args:
line_break (str, *optional*):
The line break to use when dumping to yaml.
Returns:
`str`: CardData represented as a YAML block.
"""
if original_order:
self.__dict__ = {
k: self.__dict__[k]
for k in original_order + list(set(self.__dict__.keys()) - set(original_order))
if k in self.__dict__
}
return yaml_dump(self.to_dict(), sort_keys=False, line_break=line_break).strip()
def __repr__(self):
return repr(self.__dict__)
def __str__(self):
return self.to_yaml()
def get(self, key: str, default: Any = None) -> Any:
"""Get value for a given metadata key."""
return self.__dict__.get(key, default)
def pop(self, key: str, default: Any = None) -> Any:
"""Pop value for a given metadata key."""
return self.__dict__.pop(key, default)
def __getitem__(self, key: str) -> Any:
"""Get value for a given metadata key."""
return self.__dict__[key]
def __setitem__(self, key: str, value: Any) -> None:
"""Set value for a given metadata key."""
self.__dict__[key] = value
def __contains__(self, key: str) -> bool:
"""Check if a given metadata key is set."""
return key in self.__dict__
def __len__(self) -> int:
"""Return the number of metadata keys set."""
return len(self.__dict__) | class_definition | 7,199 | 10,080 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def __init__(self, ignore_metadata_errors: bool = False, **kwargs):
self.__dict__.update(kwargs) | function_definition | 7,653 | 7,757 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def to_dict(self):
"""Converts CardData to a dict.
Returns:
`dict`: CardData represented as a dictionary ready to be dumped to a YAML
block for inclusion in a README.md file.
"""
data_dict = copy.deepcopy(self.__dict__)
self._to_dict(data_dict)
return {key: value for key, value in data_dict.items() if value is not None} | function_definition | 7,763 | 8,158 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def _to_dict(self, data_dict):
"""Use this method in child classes to alter the dict representation of the data. Alter the dict in-place.
Args:
data_dict (`dict`): The raw dict representation of the card data.
"""
pass | function_definition | 8,164 | 8,427 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def to_yaml(self, line_break=None, original_order: Optional[List[str]] = None) -> str:
"""Dumps CardData to a YAML block for inclusion in a README.md file.
Args:
line_break (str, *optional*):
The line break to use when dumping to yaml.
Returns:
`str`: CardData represented as a YAML block.
"""
if original_order:
self.__dict__ = {
k: self.__dict__[k]
for k in original_order + list(set(self.__dict__.keys()) - set(original_order))
if k in self.__dict__
}
return yaml_dump(self.to_dict(), sort_keys=False, line_break=line_break).strip() | function_definition | 8,433 | 9,130 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
if original_order:
self.__dict__ = {
k: self.__dict__[k]
for k in original_order + list(set(self.__dict__.keys()) - set(original_order))
if k in self.__dict__
} | if_statement | 8,809 | 9,041 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def __repr__(self):
return repr(self.__dict__) | function_definition | 9,136 | 9,190 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def __str__(self):
return self.to_yaml() | function_definition | 9,196 | 9,244 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def get(self, key: str, default: Any = None) -> Any:
"""Get value for a given metadata key."""
return self.__dict__.get(key, default) | function_definition | 9,250 | 9,399 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def pop(self, key: str, default: Any = None) -> Any:
"""Pop value for a given metadata key."""
return self.__dict__.pop(key, default) | function_definition | 9,405 | 9,554 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def __getitem__(self, key: str) -> Any:
"""Get value for a given metadata key."""
return self.__dict__[key] | function_definition | 9,560 | 9,683 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def __setitem__(self, key: str, value: Any) -> None:
"""Set value for a given metadata key."""
self.__dict__[key] = value | function_definition | 9,689 | 9,826 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def __contains__(self, key: str) -> bool:
"""Check if a given metadata key is set."""
return key in self.__dict__ | function_definition | 9,832 | 9,961 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
def __len__(self) -> int:
"""Return the number of metadata keys set."""
return len(self.__dict__) | function_definition | 9,967 | 10,080 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | CardData |
class ModelCardData(CardData):
"""Model Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
Args:
base_model (`str` or `List[str]`, *optional*):
The identifier of the base model from which the model derives. This is applicable for example if your model is a
fine-tune or adapter of an existing model. The value must be the ID of a model on the Hub (or a list of IDs
if your model derives from multiple models). Defaults to None.
datasets (`Union[str, List[str]]`, *optional*):
Dataset or list of datasets that were used to train this model. Should be a dataset ID
found on https://hf.co/datasets. Defaults to None.
eval_results (`Union[List[EvalResult], EvalResult]`, *optional*):
List of `huggingface_hub.EvalResult` that define evaluation results of the model. If provided,
`model_name` is used to as a name on PapersWithCode's leaderboards. Defaults to `None`.
language (`Union[str, List[str]]`, *optional*):
Language of model's training data or metadata. It must be an ISO 639-1, 639-2 or
639-3 code (two/three letters), or a special value like "code", "multilingual". Defaults to `None`.
library_name (`str`, *optional*):
Name of library used by this model. Example: keras or any library from
https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts.
Defaults to None.
license (`str`, *optional*):
License of this model. Example: apache-2.0 or any license from
https://huggingface.co/docs/hub/repositories-licenses. Defaults to None.
license_name (`str`, *optional*):
Name of the license of this model. Defaults to None. To be used in conjunction with `license_link`.
Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a name. In that case, use `license` instead.
license_link (`str`, *optional*):
Link to the license of this model. Defaults to None. To be used in conjunction with `license_name`.
Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a link. In that case, use `license` instead.
metrics (`List[str]`, *optional*):
List of metrics used to evaluate this model. Should be a metric name that can be found
at https://hf.co/metrics. Example: 'accuracy'. Defaults to None.
model_name (`str`, *optional*):
A name for this model. It is used along with
`eval_results` to construct the `model-index` within the card's metadata. The name
you supply here is what will be used on PapersWithCode's leaderboards. If None is provided
then the repo name is used as a default. Defaults to None.
pipeline_tag (`str`, *optional*):
The pipeline tag associated with the model. Example: "text-classification".
tags (`List[str]`, *optional*):
List of tags to add to your model that can be used when filtering on the Hugging
Face Hub. Defaults to None.
ignore_metadata_errors (`str`):
If True, errors while parsing the metadata section will be ignored. Some information might be lost during
the process. Use it at your own risk.
kwargs (`dict`, *optional*):
Additional metadata that will be added to the model card. Defaults to None.
Example:
```python
>>> from huggingface_hub import ModelCardData
>>> card_data = ModelCardData(
... language="en",
... license="mit",
... library_name="timm",
... tags=['image-classification', 'resnet'],
... )
>>> card_data.to_dict()
{'language': 'en', 'license': 'mit', 'library_name': 'timm', 'tags': ['image-classification', 'resnet']}
```
"""
def __init__(
self,
*,
base_model: Optional[Union[str, List[str]]] = None,
datasets: Optional[Union[str, List[str]]] = None,
eval_results: Optional[List[EvalResult]] = None,
language: Optional[Union[str, List[str]]] = None,
library_name: Optional[str] = None,
license: Optional[str] = None,
license_name: Optional[str] = None,
license_link: Optional[str] = None,
metrics: Optional[List[str]] = None,
model_name: Optional[str] = None,
pipeline_tag: Optional[str] = None,
tags: Optional[List[str]] = None,
ignore_metadata_errors: bool = False,
**kwargs,
):
self.base_model = base_model
self.datasets = datasets
self.eval_results = eval_results
self.language = language
self.library_name = library_name
self.license = license
self.license_name = license_name
self.license_link = license_link
self.metrics = metrics
self.model_name = model_name
self.pipeline_tag = pipeline_tag
self.tags = _to_unique_list(tags)
model_index = kwargs.pop("model-index", None)
if model_index:
try:
model_name, eval_results = model_index_to_eval_results(model_index)
self.model_name = model_name
self.eval_results = eval_results
except (KeyError, TypeError) as error:
if ignore_metadata_errors:
logger.warning("Invalid model-index. Not loading eval results into CardData.")
else:
raise ValueError(
f"Invalid `model_index` in metadata cannot be parsed: {error.__class__} {error}. Pass"
" `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning:"
" some information will be lost. Use it at your own risk."
)
super().__init__(**kwargs)
if self.eval_results:
if isinstance(self.eval_results, EvalResult):
self.eval_results = [self.eval_results]
if self.model_name is None:
raise ValueError("Passing `eval_results` requires `model_name` to be set.")
def _to_dict(self, data_dict):
"""Format the internal data dict. In this case, we convert eval results to a valid model index"""
if self.eval_results is not None:
data_dict["model-index"] = eval_results_to_model_index(self.model_name, self.eval_results)
del data_dict["eval_results"], data_dict["model_name"] | class_definition | 10,083 | 16,711 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def __init__(
self,
*,
base_model: Optional[Union[str, List[str]]] = None,
datasets: Optional[Union[str, List[str]]] = None,
eval_results: Optional[List[EvalResult]] = None,
language: Optional[Union[str, List[str]]] = None,
library_name: Optional[str] = None,
license: Optional[str] = None,
license_name: Optional[str] = None,
license_link: Optional[str] = None,
metrics: Optional[List[str]] = None,
model_name: Optional[str] = None,
pipeline_tag: Optional[str] = None,
tags: Optional[List[str]] = None,
ignore_metadata_errors: bool = False,
**kwargs,
):
self.base_model = base_model
self.datasets = datasets
self.eval_results = eval_results
self.language = language
self.library_name = library_name
self.license = license
self.license_name = license_name
self.license_link = license_link
self.metrics = metrics
self.model_name = model_name
self.pipeline_tag = pipeline_tag
self.tags = _to_unique_list(tags)
model_index = kwargs.pop("model-index", None)
if model_index:
try:
model_name, eval_results = model_index_to_eval_results(model_index)
self.model_name = model_name
self.eval_results = eval_results
except (KeyError, TypeError) as error:
if ignore_metadata_errors:
logger.warning("Invalid model-index. Not loading eval results into CardData.")
else:
raise ValueError(
f"Invalid `model_index` in metadata cannot be parsed: {error.__class__} {error}. Pass"
" `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning:"
" some information will be lost. Use it at your own risk."
)
super().__init__(**kwargs)
if self.eval_results:
if isinstance(self.eval_results, EvalResult):
self.eval_results = [self.eval_results]
if self.model_name is None:
raise ValueError("Passing `eval_results` requires `model_name` to be set.") | function_definition | 14,051 | 16,357 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
if model_index:
try:
model_name, eval_results = model_index_to_eval_results(model_index)
self.model_name = model_name
self.eval_results = eval_results
except (KeyError, TypeError) as error:
if ignore_metadata_errors:
logger.warning("Invalid model-index. Not loading eval results into CardData.")
else:
raise ValueError(
f"Invalid `model_index` in metadata cannot be parsed: {error.__class__} {error}. Pass"
" `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning:"
" some information will be lost. Use it at your own risk."
) | if_statement | 15,250 | 16,044 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
try:
model_name, eval_results = model_index_to_eval_results(model_index)
self.model_name = model_name
self.eval_results = eval_results
except (KeyError, TypeError) as error:
if ignore_metadata_errors:
logger.warning("Invalid model-index. Not loading eval results into CardData.")
else:
raise ValueError(
f"Invalid `model_index` in metadata cannot be parsed: {error.__class__} {error}. Pass"
" `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning:"
" some information will be lost. Use it at your own risk."
) | try_statement | 15,278 | 16,044 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
if ignore_metadata_errors:
logger.warning("Invalid model-index. Not loading eval results into CardData.")
else:
raise ValueError(
f"Invalid `model_index` in metadata cannot be parsed: {error.__class__} {error}. Pass"
" `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning:"
" some information will be lost. Use it at your own risk."
) | if_statement | 15,528 | 16,044 | 4 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
if self.eval_results:
if isinstance(self.eval_results, EvalResult):
self.eval_results = [self.eval_results]
if self.model_name is None:
raise ValueError("Passing `eval_results` requires `model_name` to be set.") | if_statement | 16,090 | 16,357 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
if isinstance(self.eval_results, EvalResult):
self.eval_results = [self.eval_results] | if_statement | 16,124 | 16,225 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
if self.model_name is None:
raise ValueError("Passing `eval_results` requires `model_name` to be set.") | if_statement | 16,238 | 16,357 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
def _to_dict(self, data_dict):
"""Format the internal data dict. In this case, we convert eval results to a valid model index"""
if self.eval_results is not None:
data_dict["model-index"] = eval_results_to_model_index(self.model_name, self.eval_results)
del data_dict["eval_results"], data_dict["model_name"] | function_definition | 16,363 | 16,711 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
if self.eval_results is not None:
data_dict["model-index"] = eval_results_to_model_index(self.model_name, self.eval_results)
del data_dict["eval_results"], data_dict["model_name"] | if_statement | 16,508 | 16,711 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | ModelCardData |
class DatasetCardData(CardData):
"""Dataset Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
Args:
language (`List[str]`, *optional*):
Language of dataset's data or metadata. It must be an ISO 639-1, 639-2 or
639-3 code (two/three letters), or a special value like "code", "multilingual".
license (`Union[str, List[str]]`, *optional*):
License(s) of this dataset. Example: apache-2.0 or any license from
https://huggingface.co/docs/hub/repositories-licenses.
annotations_creators (`Union[str, List[str]]`, *optional*):
How the annotations for the dataset were created.
Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'no-annotation', 'other'.
language_creators (`Union[str, List[str]]`, *optional*):
How the text-based data in the dataset was created.
Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'other'
multilinguality (`Union[str, List[str]]`, *optional*):
Whether the dataset is multilingual.
Options are: 'monolingual', 'multilingual', 'translation', 'other'.
size_categories (`Union[str, List[str]]`, *optional*):
The number of examples in the dataset. Options are: 'n<1K', '1K<n<10K', '10K<n<100K',
'100K<n<1M', '1M<n<10M', '10M<n<100M', '100M<n<1B', '1B<n<10B', '10B<n<100B', '100B<n<1T', 'n>1T', and 'other'.
source_datasets (`List[str]]`, *optional*):
Indicates whether the dataset is an original dataset or extended from another existing dataset.
Options are: 'original' and 'extended'.
task_categories (`Union[str, List[str]]`, *optional*):
What categories of task does the dataset support?
task_ids (`Union[str, List[str]]`, *optional*):
What specific tasks does the dataset support?
paperswithcode_id (`str`, *optional*):
ID of the dataset on PapersWithCode.
pretty_name (`str`, *optional*):
A more human-readable name for the dataset. (ex. "Cats vs. Dogs")
train_eval_index (`Dict`, *optional*):
A dictionary that describes the necessary spec for doing evaluation on the Hub.
If not provided, it will be gathered from the 'train-eval-index' key of the kwargs.
config_names (`Union[str, List[str]]`, *optional*):
A list of the available dataset configs for the dataset.
"""
def __init__(
self,
*,
language: Optional[Union[str, List[str]]] = None,
license: Optional[Union[str, List[str]]] = None,
annotations_creators: Optional[Union[str, List[str]]] = None,
language_creators: Optional[Union[str, List[str]]] = None,
multilinguality: Optional[Union[str, List[str]]] = None,
size_categories: Optional[Union[str, List[str]]] = None,
source_datasets: Optional[List[str]] = None,
task_categories: Optional[Union[str, List[str]]] = None,
task_ids: Optional[Union[str, List[str]]] = None,
paperswithcode_id: Optional[str] = None,
pretty_name: Optional[str] = None,
train_eval_index: Optional[Dict] = None,
config_names: Optional[Union[str, List[str]]] = None,
ignore_metadata_errors: bool = False,
**kwargs,
):
self.annotations_creators = annotations_creators
self.language_creators = language_creators
self.language = language
self.license = license
self.multilinguality = multilinguality
self.size_categories = size_categories
self.source_datasets = source_datasets
self.task_categories = task_categories
self.task_ids = task_ids
self.paperswithcode_id = paperswithcode_id
self.pretty_name = pretty_name
self.config_names = config_names
# TODO - maybe handle this similarly to EvalResult?
self.train_eval_index = train_eval_index or kwargs.pop("train-eval-index", None)
super().__init__(**kwargs)
def _to_dict(self, data_dict):
data_dict["train-eval-index"] = data_dict.pop("train_eval_index") | class_definition | 16,714 | 20,971 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def __init__(
self,
*,
language: Optional[Union[str, List[str]]] = None,
license: Optional[Union[str, List[str]]] = None,
annotations_creators: Optional[Union[str, List[str]]] = None,
language_creators: Optional[Union[str, List[str]]] = None,
multilinguality: Optional[Union[str, List[str]]] = None,
size_categories: Optional[Union[str, List[str]]] = None,
source_datasets: Optional[List[str]] = None,
task_categories: Optional[Union[str, List[str]]] = None,
task_ids: Optional[Union[str, List[str]]] = None,
paperswithcode_id: Optional[str] = None,
pretty_name: Optional[str] = None,
train_eval_index: Optional[Dict] = None,
config_names: Optional[Union[str, List[str]]] = None,
ignore_metadata_errors: bool = False,
**kwargs,
):
self.annotations_creators = annotations_creators
self.language_creators = language_creators
self.language = language
self.license = license
self.multilinguality = multilinguality
self.size_categories = size_categories
self.source_datasets = source_datasets
self.task_categories = task_categories
self.task_ids = task_ids
self.paperswithcode_id = paperswithcode_id
self.pretty_name = pretty_name
self.config_names = config_names
# TODO - maybe handle this similarly to EvalResult?
self.train_eval_index = train_eval_index or kwargs.pop("train-eval-index", None)
super().__init__(**kwargs) | function_definition | 19,282 | 20,861 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | DatasetCardData |
def _to_dict(self, data_dict):
data_dict["train-eval-index"] = data_dict.pop("train_eval_index") | function_definition | 20,867 | 20,971 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | DatasetCardData |
class SpaceCardData(CardData):
"""Space Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
To get an exhaustive reference of Spaces configuration, please visit https://huggingface.co/docs/hub/spaces-config-reference#spaces-configuration-reference.
Args:
title (`str`, *optional*)
Title of the Space.
sdk (`str`, *optional*)
SDK of the Space (one of `gradio`, `streamlit`, `docker`, or `static`).
sdk_version (`str`, *optional*)
Version of the used SDK (if Gradio/Streamlit sdk).
python_version (`str`, *optional*)
Python version used in the Space (if Gradio/Streamlit sdk).
app_file (`str`, *optional*)
Path to your main application file (which contains either gradio or streamlit Python code, or static html code).
Path is relative to the root of the repository.
app_port (`str`, *optional*)
Port on which your application is running. Used only if sdk is `docker`.
license (`str`, *optional*)
License of this model. Example: apache-2.0 or any license from
https://huggingface.co/docs/hub/repositories-licenses.
duplicated_from (`str`, *optional*)
ID of the original Space if this is a duplicated Space.
models (List[`str`], *optional*)
List of models related to this Space. Should be a dataset ID found on https://hf.co/models.
datasets (`List[str]`, *optional*)
List of datasets related to this Space. Should be a dataset ID found on https://hf.co/datasets.
tags (`List[str]`, *optional*)
List of tags to add to your Space that can be used when filtering on the Hub.
ignore_metadata_errors (`str`):
If True, errors while parsing the metadata section will be ignored. Some information might be lost during
the process. Use it at your own risk.
kwargs (`dict`, *optional*):
Additional metadata that will be added to the space card.
Example:
```python
>>> from huggingface_hub import SpaceCardData
>>> card_data = SpaceCardData(
... title="Dreambooth Training",
... license="mit",
... sdk="gradio",
... duplicated_from="multimodalart/dreambooth-training"
... )
>>> card_data.to_dict()
{'title': 'Dreambooth Training', 'sdk': 'gradio', 'license': 'mit', 'duplicated_from': 'multimodalart/dreambooth-training'}
```
"""
def __init__(
self,
*,
title: Optional[str] = None,
sdk: Optional[str] = None,
sdk_version: Optional[str] = None,
python_version: Optional[str] = None,
app_file: Optional[str] = None,
app_port: Optional[int] = None,
license: Optional[str] = None,
duplicated_from: Optional[str] = None,
models: Optional[List[str]] = None,
datasets: Optional[List[str]] = None,
tags: Optional[List[str]] = None,
ignore_metadata_errors: bool = False,
**kwargs,
):
self.title = title
self.sdk = sdk
self.sdk_version = sdk_version
self.python_version = python_version
self.app_file = app_file
self.app_port = app_port
self.license = license
self.duplicated_from = duplicated_from
self.models = models
self.datasets = datasets
self.tags = _to_unique_list(tags)
super().__init__(**kwargs) | class_definition | 20,974 | 24,542 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def __init__(
self,
*,
title: Optional[str] = None,
sdk: Optional[str] = None,
sdk_version: Optional[str] = None,
python_version: Optional[str] = None,
app_file: Optional[str] = None,
app_port: Optional[int] = None,
license: Optional[str] = None,
duplicated_from: Optional[str] = None,
models: Optional[List[str]] = None,
datasets: Optional[List[str]] = None,
tags: Optional[List[str]] = None,
ignore_metadata_errors: bool = False,
**kwargs,
):
self.title = title
self.sdk = sdk
self.sdk_version = sdk_version
self.python_version = python_version
self.app_file = app_file
self.app_port = app_port
self.license = license
self.duplicated_from = duplicated_from
self.models = models
self.datasets = datasets
self.tags = _to_unique_list(tags)
super().__init__(**kwargs) | function_definition | 23,557 | 24,542 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | SpaceCardData |
def model_index_to_eval_results(model_index: List[Dict[str, Any]]) -> Tuple[str, List[EvalResult]]:
"""Takes in a model index and returns the model name and a list of `huggingface_hub.EvalResult` objects.
A detailed spec of the model index can be found here:
https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
Args:
model_index (`List[Dict[str, Any]]`):
A model index data structure, likely coming from a README.md file on the
Hugging Face Hub.
Returns:
model_name (`str`):
The name of the model as found in the model index. This is used as the
identifier for the model on leaderboards like PapersWithCode.
eval_results (`List[EvalResult]`):
A list of `huggingface_hub.EvalResult` objects containing the metrics
reported in the provided model_index.
Example:
```python
>>> from huggingface_hub.repocard_data import model_index_to_eval_results
>>> # Define a minimal model index
>>> model_index = [
... {
... "name": "my-cool-model",
... "results": [
... {
... "task": {
... "type": "image-classification"
... },
... "dataset": {
... "type": "beans",
... "name": "Beans"
... },
... "metrics": [
... {
... "type": "accuracy",
... "value": 0.9
... }
... ]
... }
... ]
... }
... ]
>>> model_name, eval_results = model_index_to_eval_results(model_index)
>>> model_name
'my-cool-model'
>>> eval_results[0].task_type
'image-classification'
>>> eval_results[0].metric_type
'accuracy'
```
"""
eval_results = []
for elem in model_index:
name = elem["name"]
results = elem["results"]
for result in results:
task_type = result["task"]["type"]
task_name = result["task"].get("name")
dataset_type = result["dataset"]["type"]
dataset_name = result["dataset"]["name"]
dataset_config = result["dataset"].get("config")
dataset_split = result["dataset"].get("split")
dataset_revision = result["dataset"].get("revision")
dataset_args = result["dataset"].get("args")
source_name = result.get("source", {}).get("name")
source_url = result.get("source", {}).get("url")
for metric in result["metrics"]:
metric_type = metric["type"]
metric_value = metric["value"]
metric_name = metric.get("name")
metric_args = metric.get("args")
metric_config = metric.get("config")
verified = metric.get("verified")
verify_token = metric.get("verifyToken")
eval_result = EvalResult(
task_type=task_type, # Required
dataset_type=dataset_type, # Required
dataset_name=dataset_name, # Required
metric_type=metric_type, # Required
metric_value=metric_value, # Required
task_name=task_name,
dataset_config=dataset_config,
dataset_split=dataset_split,
dataset_revision=dataset_revision,
dataset_args=dataset_args,
metric_name=metric_name,
metric_args=metric_args,
metric_config=metric_config,
verified=verified,
verify_token=verify_token,
source_name=source_name,
source_url=source_url,
)
eval_results.append(eval_result)
return name, eval_results | function_definition | 24,545 | 28,735 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
for elem in model_index:
name = elem["name"]
results = elem["results"]
for result in results:
task_type = result["task"]["type"]
task_name = result["task"].get("name")
dataset_type = result["dataset"]["type"]
dataset_name = result["dataset"]["name"]
dataset_config = result["dataset"].get("config")
dataset_split = result["dataset"].get("split")
dataset_revision = result["dataset"].get("revision")
dataset_args = result["dataset"].get("args")
source_name = result.get("source", {}).get("name")
source_url = result.get("source", {}).get("url")
for metric in result["metrics"]:
metric_type = metric["type"]
metric_value = metric["value"]
metric_name = metric.get("name")
metric_args = metric.get("args")
metric_config = metric.get("config")
verified = metric.get("verified")
verify_token = metric.get("verifyToken")
eval_result = EvalResult(
task_type=task_type, # Required
dataset_type=dataset_type, # Required
dataset_name=dataset_name, # Required
metric_type=metric_type, # Required
metric_value=metric_value, # Required
task_name=task_name,
dataset_config=dataset_config,
dataset_split=dataset_split,
dataset_revision=dataset_revision,
dataset_args=dataset_args,
metric_name=metric_name,
metric_args=metric_args,
metric_config=metric_config,
verified=verified,
verify_token=verify_token,
source_name=source_name,
source_url=source_url,
)
eval_results.append(eval_result) | for_statement | 26,669 | 28,705 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
for result in results:
task_type = result["task"]["type"]
task_name = result["task"].get("name")
dataset_type = result["dataset"]["type"]
dataset_name = result["dataset"]["name"]
dataset_config = result["dataset"].get("config")
dataset_split = result["dataset"].get("split")
dataset_revision = result["dataset"].get("revision")
dataset_args = result["dataset"].get("args")
source_name = result.get("source", {}).get("name")
source_url = result.get("source", {}).get("url")
for metric in result["metrics"]:
metric_type = metric["type"]
metric_value = metric["value"]
metric_name = metric.get("name")
metric_args = metric.get("args")
metric_config = metric.get("config")
verified = metric.get("verified")
verify_token = metric.get("verifyToken")
eval_result = EvalResult(
task_type=task_type, # Required
dataset_type=dataset_type, # Required
dataset_name=dataset_name, # Required
metric_type=metric_type, # Required
metric_value=metric_value, # Required
task_name=task_name,
dataset_config=dataset_config,
dataset_split=dataset_split,
dataset_revision=dataset_revision,
dataset_args=dataset_args,
metric_name=metric_name,
metric_args=metric_args,
metric_config=metric_config,
verified=verified,
verify_token=verify_token,
source_name=source_name,
source_url=source_url,
)
eval_results.append(eval_result) | for_statement | 26,764 | 28,705 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
for metric in result["metrics"]:
metric_type = metric["type"]
metric_value = metric["value"]
metric_name = metric.get("name")
metric_args = metric.get("args")
metric_config = metric.get("config")
verified = metric.get("verified")
verify_token = metric.get("verifyToken")
eval_result = EvalResult(
task_type=task_type, # Required
dataset_type=dataset_type, # Required
dataset_name=dataset_name, # Required
metric_type=metric_type, # Required
metric_value=metric_value, # Required
task_name=task_name,
dataset_config=dataset_config,
dataset_split=dataset_split,
dataset_revision=dataset_revision,
dataset_args=dataset_args,
metric_name=metric_name,
metric_args=metric_args,
metric_config=metric_config,
verified=verified,
verify_token=verify_token,
source_name=source_name,
source_url=source_url,
)
eval_results.append(eval_result) | for_statement | 27,370 | 28,705 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def _remove_none(obj):
"""
Recursively remove `None` values from a dict. Borrowed from: https://stackoverflow.com/a/20558778
"""
if isinstance(obj, (list, tuple, set)):
return type(obj)(_remove_none(x) for x in obj if x is not None)
elif isinstance(obj, dict):
return type(obj)((_remove_none(k), _remove_none(v)) for k, v in obj.items() if k is not None and v is not None)
else:
return obj | function_definition | 28,738 | 29,175 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
if isinstance(obj, (list, tuple, set)):
return type(obj)(_remove_none(x) for x in obj if x is not None)
elif isinstance(obj, dict):
return type(obj)((_remove_none(k), _remove_none(v)) for k, v in obj.items() if k is not None and v is not None)
else:
return obj | if_statement | 28,883 | 29,175 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def eval_results_to_model_index(model_name: str, eval_results: List[EvalResult]) -> List[Dict[str, Any]]:
"""Takes in given model name and list of `huggingface_hub.EvalResult` and returns a
valid model-index that will be compatible with the format expected by the
Hugging Face Hub.
Args:
model_name (`str`):
Name of the model (ex. "my-cool-model"). This is used as the identifier
for the model on leaderboards like PapersWithCode.
eval_results (`List[EvalResult]`):
List of `huggingface_hub.EvalResult` objects containing the metrics to be
reported in the model-index.
Returns:
model_index (`List[Dict[str, Any]]`): The eval_results converted to a model-index.
Example:
```python
>>> from huggingface_hub.repocard_data import eval_results_to_model_index, EvalResult
>>> # Define minimal eval_results
>>> eval_results = [
... EvalResult(
... task_type="image-classification", # Required
... dataset_type="beans", # Required
... dataset_name="Beans", # Required
... metric_type="accuracy", # Required
... metric_value=0.9, # Required
... )
... ]
>>> eval_results_to_model_index("my-cool-model", eval_results)
[{'name': 'my-cool-model', 'results': [{'task': {'type': 'image-classification'}, 'dataset': {'name': 'Beans', 'type': 'beans'}, 'metrics': [{'type': 'accuracy', 'value': 0.9}]}]}]
```
"""
# Metrics are reported on a unique task-and-dataset basis.
# Here, we make a map of those pairs and the associated EvalResults.
task_and_ds_types_map: Dict[Any, List[EvalResult]] = defaultdict(list)
for eval_result in eval_results:
task_and_ds_types_map[eval_result.unique_identifier].append(eval_result)
# Use the map from above to generate the model index data.
model_index_data = []
for results in task_and_ds_types_map.values():
# All items from `results` share same metadata
sample_result = results[0]
data = {
"task": {
"type": sample_result.task_type,
"name": sample_result.task_name,
},
"dataset": {
"name": sample_result.dataset_name,
"type": sample_result.dataset_type,
"config": sample_result.dataset_config,
"split": sample_result.dataset_split,
"revision": sample_result.dataset_revision,
"args": sample_result.dataset_args,
},
"metrics": [
{
"type": result.metric_type,
"value": result.metric_value,
"name": result.metric_name,
"config": result.metric_config,
"args": result.metric_args,
"verified": result.verified,
"verifyToken": result.verify_token,
}
for result in results
],
}
if sample_result.source_url is not None:
source = {
"url": sample_result.source_url,
}
if sample_result.source_name is not None:
source["name"] = sample_result.source_name
data["source"] = source
model_index_data.append(data)
# TODO - Check if there cases where this list is longer than one?
# Finally, the model index itself is list of dicts.
model_index = [
{
"name": model_name,
"results": model_index_data,
}
]
return _remove_none(model_index) | function_definition | 29,178 | 32,909 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
for eval_result in eval_results:
task_and_ds_types_map[eval_result.unique_identifier].append(eval_result) | for_statement | 30,967 | 31,080 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
for results in task_and_ds_types_map.values():
# All items from `results` share same metadata
sample_result = results[0]
data = {
"task": {
"type": sample_result.task_type,
"name": sample_result.task_name,
},
"dataset": {
"name": sample_result.dataset_name,
"type": sample_result.dataset_type,
"config": sample_result.dataset_config,
"split": sample_result.dataset_split,
"revision": sample_result.dataset_revision,
"args": sample_result.dataset_args,
},
"metrics": [
{
"type": result.metric_type,
"value": result.metric_value,
"name": result.metric_name,
"config": result.metric_config,
"args": result.metric_args,
"verified": result.verified,
"verifyToken": result.verify_token,
}
for result in results
],
}
if sample_result.source_url is not None:
source = {
"url": sample_result.source_url,
}
if sample_result.source_name is not None:
source["name"] = sample_result.source_name
data["source"] = source
model_index_data.append(data) | for_statement | 31,175 | 32,626 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
if sample_result.source_url is not None:
source = {
"url": sample_result.source_url,
}
if sample_result.source_name is not None:
source["name"] = sample_result.source_name
data["source"] = source | if_statement | 32,313 | 32,588 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
if sample_result.source_name is not None:
source["name"] = sample_result.source_name | if_statement | 32,452 | 32,552 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
def _to_unique_list(tags: Optional[List[str]]) -> Optional[List[str]]:
if tags is None:
return tags
unique_tags = [] # make tags unique + keep order explicitly
for tag in tags:
if tag not in unique_tags:
unique_tags.append(tag)
return unique_tags | function_definition | 32,912 | 33,203 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
if tags is None:
return tags | if_statement | 32,987 | 33,023 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
for tag in tags:
if tag not in unique_tags:
unique_tags.append(tag) | for_statement | 33,093 | 33,180 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
if tag not in unique_tags:
unique_tags.append(tag) | if_statement | 33,118 | 33,180 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/repocard_data.py | null |
if is_pydantic_available():
from pydantic import BaseModel
else:
# Define a dummy BaseModel to avoid import errors when pydantic is not installed
# Import error will be raised when trying to use the class
class BaseModel: # type: ignore [no-redef]
def __init__(self, *args, **kwargs) -> None:
raise ImportError(
"You must have `pydantic` installed to use `WebhookPayload`. This is an optional dependency that"
" should be installed separately. Please run `pip install --upgrade pydantic` and retry."
) | if_statement | 764 | 1,347 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class BaseModel: # type: ignore [no-redef]
def __init__(self, *args, **kwargs) -> None:
raise ImportError(
"You must have `pydantic` installed to use `WebhookPayload`. This is an optional dependency that"
" should be installed separately. Please run `pip install --upgrade pydantic` and retry."
) | class_definition | 986 | 1,347 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
def __init__(self, *args, **kwargs) -> None:
raise ImportError(
"You must have `pydantic` installed to use `WebhookPayload`. This is an optional dependency that"
" should be installed separately. Please run `pip install --upgrade pydantic` and retry."
) | function_definition | 1,038 | 1,347 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | BaseModel |
class ObjectId(BaseModel):
id: str | class_definition | 1,991 | 2,029 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadUrl(BaseModel):
web: str
api: Optional[str] = None | class_definition | 2,032 | 2,110 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadMovedTo(BaseModel):
name: str
owner: ObjectId | class_definition | 2,113 | 2,186 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadWebhook(ObjectId):
version: SupportedWebhookVersion | class_definition | 2,189 | 2,264 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadEvent(BaseModel):
action: WebhookEvent_T
scope: str | class_definition | 2,267 | 2,346 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadDiscussionChanges(BaseModel):
base: str
mergeCommitId: Optional[str] = None | class_definition | 2,349 | 2,452 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadComment(ObjectId):
author: ObjectId
hidden: bool
content: Optional[str] = None
url: WebhookPayloadUrl | class_definition | 2,455 | 2,592 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadDiscussion(ObjectId):
num: int
author: ObjectId
url: WebhookPayloadUrl
title: str
isPullRequest: bool
status: DiscussionStatus_T
changes: Optional[WebhookPayloadDiscussionChanges] = None
pinned: Optional[bool] = None | class_definition | 2,595 | 2,863 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadRepo(ObjectId):
owner: ObjectId
head_sha: Optional[str] = None
name: str
private: bool
subdomain: Optional[str] = None
tags: Optional[List[str]] = None
type: Literal["dataset", "model", "space"]
url: WebhookPayloadUrl | class_definition | 2,866 | 3,135 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayloadUpdatedRef(BaseModel):
ref: str
oldSha: Optional[str] = None
newSha: Optional[str] = None | class_definition | 3,138 | 3,259 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
class WebhookPayload(BaseModel):
event: WebhookPayloadEvent
repo: WebhookPayloadRepo
discussion: Optional[WebhookPayloadDiscussion] = None
comment: Optional[WebhookPayloadComment] = None
webhook: WebhookPayloadWebhook
movedTo: Optional[WebhookPayloadMovedTo] = None
updatedRefs: Optional[List[WebhookPayloadUpdatedRef]] = None | class_definition | 3,262 | 3,616 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/_webhooks_payload.py | null |
if TYPE_CHECKING:
from _typeshed import DataclassInstance | if_statement | 664 | 725 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | null |
if is_torch_available():
import torch # type: ignore | if_statement | 727 | 784 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | null |
if is_safetensors_available():
import safetensors
from safetensors.torch import load_model as load_model_as_safetensor
from safetensors.torch import save_model as save_model_as_safetensor | if_statement | 786 | 985 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | null |
class MixinInfo:
model_card_template: str
model_card_data: ModelCardData
repo_url: Optional[str] = None
docs_url: Optional[str] = None | class_definition | 1,902 | 2,052 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | null |
class ModelHubMixin:
"""
A generic mixin to integrate ANY machine learning framework with the Hub.
To integrate your framework, your model class must inherit from this class. Custom logic for saving/loading models
have to be overwritten in [`_from_pretrained`] and [`_save_pretrained`]. [`PyTorchModelHubMixin`] is a good example
of mixin integration with the Hub. Check out our [integration guide](../guides/integrations) for more instructions.
When inheriting from [`ModelHubMixin`], you can define class-level attributes. These attributes are not passed to
`__init__` but to the class definition itself. This is useful to define metadata about the library integrating
[`ModelHubMixin`].
For more details on how to integrate the mixin with your library, checkout the [integration guide](../guides/integrations).
Args:
repo_url (`str`, *optional*):
URL of the library repository. Used to generate model card.
docs_url (`str`, *optional*):
URL of the library documentation. Used to generate model card.
model_card_template (`str`, *optional*):
Template of the model card. Used to generate model card. Defaults to a generic template.
language (`str` or `List[str]`, *optional*):
Language supported by the library. Used to generate model card.
library_name (`str`, *optional*):
Name of the library integrating ModelHubMixin. Used to generate model card.
license (`str`, *optional*):
License of the library integrating ModelHubMixin. Used to generate model card.
E.g: "apache-2.0"
license_name (`str`, *optional*):
Name of the library integrating ModelHubMixin. Used to generate model card.
Only used if `license` is set to `other`.
E.g: "coqui-public-model-license".
license_link (`str`, *optional*):
URL to the license of the library integrating ModelHubMixin. Used to generate model card.
Only used if `license` is set to `other` and `license_name` is set.
E.g: "https://coqui.ai/cpml".
pipeline_tag (`str`, *optional*):
Tag of the pipeline. Used to generate model card. E.g. "text-classification".
tags (`List[str]`, *optional*):
Tags to be added to the model card. Used to generate model card. E.g. ["x-custom-tag", "arxiv:2304.12244"]
coders (`Dict[Type, Tuple[Callable, Callable]]`, *optional*):
Dictionary of custom types and their encoders/decoders. Used to encode/decode arguments that are not
jsonable by default. E.g dataclasses, argparse.Namespace, OmegaConf, etc.
Example:
```python
>>> from huggingface_hub import ModelHubMixin
# Inherit from ModelHubMixin
>>> class MyCustomModel(
... ModelHubMixin,
... library_name="my-library",
... tags=["x-custom-tag", "arxiv:2304.12244"],
... repo_url="https://github.com/huggingface/my-cool-library",
... docs_url="https://huggingface.co/docs/my-cool-library",
... # ^ optional metadata to generate model card
... ):
... def __init__(self, size: int = 512, device: str = "cpu"):
... # define how to initialize your model
... super().__init__()
... ...
...
... def _save_pretrained(self, save_directory: Path) -> None:
... # define how to serialize your model
... ...
...
... @classmethod
... def from_pretrained(
... cls: Type[T],
... pretrained_model_name_or_path: Union[str, Path],
... *,
... force_download: bool = False,
... resume_download: Optional[bool] = None,
... proxies: Optional[Dict] = None,
... token: Optional[Union[str, bool]] = None,
... cache_dir: Optional[Union[str, Path]] = None,
... local_files_only: bool = False,
... revision: Optional[str] = None,
... **model_kwargs,
... ) -> T:
... # define how to deserialize your model
... ...
>>> model = MyCustomModel(size=256, device="gpu")
# Save model weights to local directory
>>> model.save_pretrained("my-awesome-model")
# Push model weights to the Hub
>>> model.push_to_hub("my-awesome-model")
# Download and initialize weights from the Hub
>>> reloaded_model = MyCustomModel.from_pretrained("username/my-awesome-model")
>>> reloaded_model.size
256
# Model card has been correctly populated
>>> from huggingface_hub import ModelCard
>>> card = ModelCard.load("username/my-awesome-model")
>>> card.data.tags
["x-custom-tag", "pytorch_model_hub_mixin", "model_hub_mixin"]
>>> card.data.library_name
"my-library"
```
"""
_hub_mixin_config: Optional[Union[dict, "DataclassInstance"]] = None
# ^ optional config attribute automatically set in `from_pretrained`
_hub_mixin_info: MixinInfo
# ^ information about the library integrating ModelHubMixin (used to generate model card)
_hub_mixin_inject_config: bool # whether `_from_pretrained` expects `config` or not
_hub_mixin_init_parameters: Dict[str, inspect.Parameter] # __init__ parameters
_hub_mixin_jsonable_default_values: Dict[str, Any] # default values for __init__ parameters
_hub_mixin_jsonable_custom_types: Tuple[Type, ...] # custom types that can be encoded/decoded
_hub_mixin_coders: Dict[Type, CODER_T] # encoders/decoders for custom types
# ^ internal values to handle config
def __init_subclass__(
cls,
*,
# Generic info for model card
repo_url: Optional[str] = None,
docs_url: Optional[str] = None,
# Model card template
model_card_template: str = DEFAULT_MODEL_CARD,
# Model card metadata
language: Optional[List[str]] = None,
library_name: Optional[str] = None,
license: Optional[str] = None,
license_name: Optional[str] = None,
license_link: Optional[str] = None,
pipeline_tag: Optional[str] = None,
tags: Optional[List[str]] = None,
# How to encode/decode arguments with custom type into a JSON config?
coders: Optional[
Dict[Type, CODER_T]
# Key is a type.
# Value is a tuple (encoder, decoder).
# Example: {MyCustomType: (lambda x: x.value, lambda data: MyCustomType(data))}
] = None,
) -> None:
"""Inspect __init__ signature only once when subclassing + handle modelcard."""
super().__init_subclass__()
# Will be reused when creating modelcard
tags = tags or []
tags.append("model_hub_mixin")
# Initialize MixinInfo if not existent
info = MixinInfo(model_card_template=model_card_template, model_card_data=ModelCardData())
# If parent class has a MixinInfo, inherit from it as a copy
if hasattr(cls, "_hub_mixin_info"):
# Inherit model card template from parent class if not explicitly set
if model_card_template == DEFAULT_MODEL_CARD:
info.model_card_template = cls._hub_mixin_info.model_card_template
# Inherit from parent model card data
info.model_card_data = ModelCardData(**cls._hub_mixin_info.model_card_data.to_dict())
# Inherit other info
info.docs_url = cls._hub_mixin_info.docs_url
info.repo_url = cls._hub_mixin_info.repo_url
cls._hub_mixin_info = info
# Update MixinInfo with metadata
if model_card_template is not None and model_card_template != DEFAULT_MODEL_CARD:
info.model_card_template = model_card_template
if repo_url is not None:
info.repo_url = repo_url
if docs_url is not None:
info.docs_url = docs_url
if language is not None:
info.model_card_data.language = language
if library_name is not None:
info.model_card_data.library_name = library_name
if license is not None:
info.model_card_data.license = license
if license_name is not None:
info.model_card_data.license_name = license_name
if license_link is not None:
info.model_card_data.license_link = license_link
if pipeline_tag is not None:
info.model_card_data.pipeline_tag = pipeline_tag
if tags is not None:
if info.model_card_data.tags is not None:
info.model_card_data.tags.extend(tags)
else:
info.model_card_data.tags = tags
info.model_card_data.tags = sorted(set(info.model_card_data.tags))
# Handle encoders/decoders for args
cls._hub_mixin_coders = coders or {}
cls._hub_mixin_jsonable_custom_types = tuple(cls._hub_mixin_coders.keys())
# Inspect __init__ signature to handle config
cls._hub_mixin_init_parameters = dict(inspect.signature(cls.__init__).parameters)
cls._hub_mixin_jsonable_default_values = {
param.name: cls._encode_arg(param.default)
for param in cls._hub_mixin_init_parameters.values()
if param.default is not inspect.Parameter.empty and cls._is_jsonable(param.default)
}
cls._hub_mixin_inject_config = "config" in inspect.signature(cls._from_pretrained).parameters
def __new__(cls: Type[T], *args, **kwargs) -> T:
"""Create a new instance of the class and handle config.
3 cases:
- If `self._hub_mixin_config` is already set, do nothing.
- If `config` is passed as a dataclass, set it as `self._hub_mixin_config`.
- Otherwise, build `self._hub_mixin_config` from default values and passed values.
"""
instance = super().__new__(cls)
# If `config` is already set, return early
if instance._hub_mixin_config is not None:
return instance
# Infer passed values
passed_values = {
**{
key: value
for key, value in zip(
# [1:] to skip `self` parameter
list(cls._hub_mixin_init_parameters)[1:],
args,
)
},
**kwargs,
}
# If config passed as dataclass => set it and return early
if is_dataclass(passed_values.get("config")):
instance._hub_mixin_config = passed_values["config"]
return instance
# Otherwise, build config from default + passed values
init_config = {
# default values
**cls._hub_mixin_jsonable_default_values,
# passed values
**{
key: cls._encode_arg(value) # Encode custom types as jsonable value
for key, value in passed_values.items()
if instance._is_jsonable(value) # Only if jsonable or we have a custom encoder
},
}
passed_config = init_config.pop("config", {})
# Populate `init_config` with provided config
if isinstance(passed_config, dict):
init_config.update(passed_config)
# Set `config` attribute and return
if init_config != {}:
instance._hub_mixin_config = init_config
return instance
@classmethod
def _is_jsonable(cls, value: Any) -> bool:
"""Check if a value is JSON serializable."""
if isinstance(value, cls._hub_mixin_jsonable_custom_types):
return True
return is_jsonable(value)
@classmethod
def _encode_arg(cls, arg: Any) -> Any:
"""Encode an argument into a JSON serializable format."""
for type_, (encoder, _) in cls._hub_mixin_coders.items():
if isinstance(arg, type_):
if arg is None:
return None
return encoder(arg)
return arg
@classmethod
def _decode_arg(cls, expected_type: Type[ARGS_T], value: Any) -> Optional[ARGS_T]:
"""Decode a JSON serializable value into an argument."""
if is_simple_optional_type(expected_type):
if value is None:
return None
expected_type = unwrap_simple_optional_type(expected_type)
# Dataclass => handle it
if is_dataclass(expected_type):
return _load_dataclass(expected_type, value) # type: ignore[return-value]
# Otherwise => check custom decoders
for type_, (_, decoder) in cls._hub_mixin_coders.items():
if inspect.isclass(expected_type) and issubclass(expected_type, type_):
return decoder(value)
# Otherwise => don't decode
return value
def save_pretrained(
self,
save_directory: Union[str, Path],
*,
config: Optional[Union[dict, "DataclassInstance"]] = None,
repo_id: Optional[str] = None,
push_to_hub: bool = False,
model_card_kwargs: Optional[Dict[str, Any]] = None,
**push_to_hub_kwargs,
) -> Optional[str]:
"""
Save weights in local directory.
Args:
save_directory (`str` or `Path`):
Path to directory in which the model weights and configuration will be saved.
config (`dict` or `DataclassInstance`, *optional*):
Model configuration specified as a key/value dictionary or a dataclass instance.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Huggingface Hub after saving it.
repo_id (`str`, *optional*):
ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to the folder name if
not provided.
model_card_kwargs (`Dict[str, Any]`, *optional*):
Additional arguments passed to the model card template to customize the model card.
push_to_hub_kwargs:
Additional key word arguments passed along to the [`~ModelHubMixin.push_to_hub`] method.
Returns:
`str` or `None`: url of the commit on the Hub if `push_to_hub=True`, `None` otherwise.
"""
save_directory = Path(save_directory)
save_directory.mkdir(parents=True, exist_ok=True)
# Remove config.json if already exists. After `_save_pretrained` we don't want to overwrite config.json
# as it might have been saved by the custom `_save_pretrained` already. However we do want to overwrite
# an existing config.json if it was not saved by `_save_pretrained`.
config_path = save_directory / constants.CONFIG_NAME
config_path.unlink(missing_ok=True)
# save model weights/files (framework-specific)
self._save_pretrained(save_directory)
# save config (if provided and if not serialized yet in `_save_pretrained`)
if config is None:
config = self._hub_mixin_config
if config is not None:
if is_dataclass(config):
config = asdict(config) # type: ignore[arg-type]
if not config_path.exists():
config_str = json.dumps(config, sort_keys=True, indent=2)
config_path.write_text(config_str)
# save model card
model_card_path = save_directory / "README.md"
model_card_kwargs = model_card_kwargs if model_card_kwargs is not None else {}
if not model_card_path.exists(): # do not overwrite if already exists
self.generate_model_card(**model_card_kwargs).save(save_directory / "README.md")
# push to the Hub if required
if push_to_hub:
kwargs = push_to_hub_kwargs.copy() # soft-copy to avoid mutating input
if config is not None: # kwarg for `push_to_hub`
kwargs["config"] = config
if repo_id is None:
repo_id = save_directory.name # Defaults to `save_directory` name
return self.push_to_hub(repo_id=repo_id, model_card_kwargs=model_card_kwargs, **kwargs)
return None
def _save_pretrained(self, save_directory: Path) -> None:
"""
Overwrite this method in subclass to define how to save your model.
Check out our [integration guide](../guides/integrations) for instructions.
Args:
save_directory (`str` or `Path`):
Path to directory in which the model weights and configuration will be saved.
"""
raise NotImplementedError
@classmethod
@validate_hf_hub_args
def from_pretrained(
cls: Type[T],
pretrained_model_name_or_path: Union[str, Path],
*,
force_download: bool = False,
resume_download: Optional[bool] = None,
proxies: Optional[Dict] = None,
token: Optional[Union[str, bool]] = None,
cache_dir: Optional[Union[str, Path]] = None,
local_files_only: bool = False,
revision: Optional[str] = None,
**model_kwargs,
) -> T:
"""
Download a model from the Huggingface Hub and instantiate it.
Args:
pretrained_model_name_or_path (`str`, `Path`):
- Either the `model_id` (string) of a model hosted on the Hub, e.g. `bigscience/bloom`.
- Or a path to a `directory` containing model weights saved using
[`~transformers.PreTrainedModel.save_pretrained`], e.g., `../path/to/my_model_directory/`.
revision (`str`, *optional*):
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
Defaults to the latest commit on `main` branch.
force_download (`bool`, *optional*, defaults to `False`):
Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on every request.
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running `huggingface-cli login`.
cache_dir (`str`, `Path`, *optional*):
Path to the folder where cached files are stored.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, avoid downloading the file and return the path to the local cached file if it exists.
model_kwargs (`Dict`, *optional*):
Additional kwargs to pass to the model during initialization.
"""
model_id = str(pretrained_model_name_or_path)
config_file: Optional[str] = None
if os.path.isdir(model_id):
if constants.CONFIG_NAME in os.listdir(model_id):
config_file = os.path.join(model_id, constants.CONFIG_NAME)
else:
logger.warning(f"{constants.CONFIG_NAME} not found in {Path(model_id).resolve()}")
else:
try:
config_file = hf_hub_download(
repo_id=model_id,
filename=constants.CONFIG_NAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
token=token,
local_files_only=local_files_only,
)
except HfHubHTTPError as e:
logger.info(f"{constants.CONFIG_NAME} not found on the HuggingFace Hub: {str(e)}")
# Read config
config = None
if config_file is not None:
with open(config_file, "r", encoding="utf-8") as f:
config = json.load(f)
# Decode custom types in config
for key, value in config.items():
if key in cls._hub_mixin_init_parameters:
expected_type = cls._hub_mixin_init_parameters[key].annotation
if expected_type is not inspect.Parameter.empty:
config[key] = cls._decode_arg(expected_type, value)
# Populate model_kwargs from config
for param in cls._hub_mixin_init_parameters.values():
if param.name not in model_kwargs and param.name in config:
model_kwargs[param.name] = config[param.name]
# Check if `config` argument was passed at init
if "config" in cls._hub_mixin_init_parameters and "config" not in model_kwargs:
# Decode `config` argument if it was passed
config_annotation = cls._hub_mixin_init_parameters["config"].annotation
config = cls._decode_arg(config_annotation, config)
# Forward config to model initialization
model_kwargs["config"] = config
# Inject config if `**kwargs` are expected
if is_dataclass(cls):
for key in cls.__dataclass_fields__:
if key not in model_kwargs and key in config:
model_kwargs[key] = config[key]
elif any(param.kind == inspect.Parameter.VAR_KEYWORD for param in cls._hub_mixin_init_parameters.values()):
for key, value in config.items():
if key not in model_kwargs:
model_kwargs[key] = value
# Finally, also inject if `_from_pretrained` expects it
if cls._hub_mixin_inject_config and "config" not in model_kwargs:
model_kwargs["config"] = config
instance = cls._from_pretrained(
model_id=str(model_id),
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
**model_kwargs,
)
# Implicitly set the config as instance attribute if not already set by the class
# This way `config` will be available when calling `save_pretrained` or `push_to_hub`.
if config is not None and (getattr(instance, "_hub_mixin_config", None) in (None, {})):
instance._hub_mixin_config = config
return instance
@classmethod
def _from_pretrained(
cls: Type[T],
*,
model_id: str,
revision: Optional[str],
cache_dir: Optional[Union[str, Path]],
force_download: bool,
proxies: Optional[Dict],
resume_download: Optional[bool],
local_files_only: bool,
token: Optional[Union[str, bool]],
**model_kwargs,
) -> T:
"""Overwrite this method in subclass to define how to load your model from pretrained.
Use [`hf_hub_download`] or [`snapshot_download`] to download files from the Hub before loading them. Most
args taken as input can be directly passed to those 2 methods. If needed, you can add more arguments to this
method using "model_kwargs". For example [`PyTorchModelHubMixin._from_pretrained`] takes as input a `map_location`
parameter to set on which device the model should be loaded.
Check out our [integration guide](../guides/integrations) for more instructions.
Args:
model_id (`str`):
ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
revision (`str`, *optional*):
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
latest commit on `main` branch.
force_download (`bool`, *optional*, defaults to `False`):
Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint (e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`).
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running `huggingface-cli login`.
cache_dir (`str`, `Path`, *optional*):
Path to the folder where cached files are stored.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, avoid downloading the file and return the path to the local cached file if it exists.
model_kwargs:
Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
"""
raise NotImplementedError
@validate_hf_hub_args
def push_to_hub(
self,
repo_id: str,
*,
config: Optional[Union[dict, "DataclassInstance"]] = None,
commit_message: str = "Push model using huggingface_hub.",
private: Optional[bool] = None,
token: Optional[str] = None,
branch: Optional[str] = None,
create_pr: Optional[bool] = None,
allow_patterns: Optional[Union[List[str], str]] = None,
ignore_patterns: Optional[Union[List[str], str]] = None,
delete_patterns: Optional[Union[List[str], str]] = None,
model_card_kwargs: Optional[Dict[str, Any]] = None,
) -> str:
"""
Upload model checkpoint to the Hub.
Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
`delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
details.
Args:
repo_id (`str`):
ID of the repository to push to (example: `"username/my-model"`).
config (`dict` or `DataclassInstance`, *optional*):
Model configuration specified as a key/value dictionary or a dataclass instance.
commit_message (`str`, *optional*):
Message to commit while pushing.
private (`bool`, *optional*):
Whether the repository created should be private.
If `None` (default), the repo will be public unless the organization's default is private.
token (`str`, *optional*):
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running `huggingface-cli login`.
branch (`str`, *optional*):
The git branch on which to push the model. This defaults to `"main"`.
create_pr (`boolean`, *optional*):
Whether or not to create a Pull Request from `branch` with that commit. Defaults to `False`.
allow_patterns (`List[str]` or `str`, *optional*):
If provided, only files matching at least one pattern are pushed.
ignore_patterns (`List[str]` or `str`, *optional*):
If provided, files matching any of the patterns are not pushed.
delete_patterns (`List[str]` or `str`, *optional*):
If provided, remote files matching any of the patterns will be deleted from the repo.
model_card_kwargs (`Dict[str, Any]`, *optional*):
Additional arguments passed to the model card template to customize the model card.
Returns:
The url of the commit of your model in the given repository.
"""
api = HfApi(token=token)
repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id
# Push the files to the repo in a single commit
with SoftTemporaryDirectory() as tmp:
saved_path = Path(tmp) / repo_id
self.save_pretrained(saved_path, config=config, model_card_kwargs=model_card_kwargs)
return api.upload_folder(
repo_id=repo_id,
repo_type="model",
folder_path=saved_path,
commit_message=commit_message,
revision=branch,
create_pr=create_pr,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
delete_patterns=delete_patterns,
)
def generate_model_card(self, *args, **kwargs) -> ModelCard:
card = ModelCard.from_template(
card_data=self._hub_mixin_info.model_card_data,
template_str=self._hub_mixin_info.model_card_template,
repo_url=self._hub_mixin_info.repo_url,
docs_url=self._hub_mixin_info.docs_url,
**kwargs,
)
return card | class_definition | 2,055 | 31,281 | 0 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | null |
def __init_subclass__(
cls,
*,
# Generic info for model card
repo_url: Optional[str] = None,
docs_url: Optional[str] = None,
# Model card template
model_card_template: str = DEFAULT_MODEL_CARD,
# Model card metadata
language: Optional[List[str]] = None,
library_name: Optional[str] = None,
license: Optional[str] = None,
license_name: Optional[str] = None,
license_link: Optional[str] = None,
pipeline_tag: Optional[str] = None,
tags: Optional[List[str]] = None,
# How to encode/decode arguments with custom type into a JSON config?
coders: Optional[
Dict[Type, CODER_T]
# Key is a type.
# Value is a tuple (encoder, decoder).
# Example: {MyCustomType: (lambda x: x.value, lambda data: MyCustomType(data))}
] = None,
) -> None:
"""Inspect __init__ signature only once when subclassing + handle modelcard."""
super().__init_subclass__()
# Will be reused when creating modelcard
tags = tags or []
tags.append("model_hub_mixin")
# Initialize MixinInfo if not existent
info = MixinInfo(model_card_template=model_card_template, model_card_data=ModelCardData())
# If parent class has a MixinInfo, inherit from it as a copy
if hasattr(cls, "_hub_mixin_info"):
# Inherit model card template from parent class if not explicitly set
if model_card_template == DEFAULT_MODEL_CARD:
info.model_card_template = cls._hub_mixin_info.model_card_template
# Inherit from parent model card data
info.model_card_data = ModelCardData(**cls._hub_mixin_info.model_card_data.to_dict())
# Inherit other info
info.docs_url = cls._hub_mixin_info.docs_url
info.repo_url = cls._hub_mixin_info.repo_url
cls._hub_mixin_info = info
# Update MixinInfo with metadata
if model_card_template is not None and model_card_template != DEFAULT_MODEL_CARD:
info.model_card_template = model_card_template
if repo_url is not None:
info.repo_url = repo_url
if docs_url is not None:
info.docs_url = docs_url
if language is not None:
info.model_card_data.language = language
if library_name is not None:
info.model_card_data.library_name = library_name
if license is not None:
info.model_card_data.license = license
if license_name is not None:
info.model_card_data.license_name = license_name
if license_link is not None:
info.model_card_data.license_link = license_link
if pipeline_tag is not None:
info.model_card_data.pipeline_tag = pipeline_tag
if tags is not None:
if info.model_card_data.tags is not None:
info.model_card_data.tags.extend(tags)
else:
info.model_card_data.tags = tags
info.model_card_data.tags = sorted(set(info.model_card_data.tags))
# Handle encoders/decoders for args
cls._hub_mixin_coders = coders or {}
cls._hub_mixin_jsonable_custom_types = tuple(cls._hub_mixin_coders.keys())
# Inspect __init__ signature to handle config
cls._hub_mixin_init_parameters = dict(inspect.signature(cls.__init__).parameters)
cls._hub_mixin_jsonable_default_values = {
param.name: cls._encode_arg(param.default)
for param in cls._hub_mixin_init_parameters.values()
if param.default is not inspect.Parameter.empty and cls._is_jsonable(param.default)
}
cls._hub_mixin_inject_config = "config" in inspect.signature(cls._from_pretrained).parameters | function_definition | 7,747 | 11,595 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if hasattr(cls, "_hub_mixin_info"):
# Inherit model card template from parent class if not explicitly set
if model_card_template == DEFAULT_MODEL_CARD:
info.model_card_template = cls._hub_mixin_info.model_card_template
# Inherit from parent model card data
info.model_card_data = ModelCardData(**cls._hub_mixin_info.model_card_data.to_dict())
# Inherit other info
info.docs_url = cls._hub_mixin_info.docs_url
info.repo_url = cls._hub_mixin_info.repo_url | if_statement | 9,135 | 9,690 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if model_card_template == DEFAULT_MODEL_CARD:
info.model_card_template = cls._hub_mixin_info.model_card_template | if_statement | 9,265 | 9,393 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if model_card_template is not None and model_card_template != DEFAULT_MODEL_CARD:
info.model_card_template = model_card_template | if_statement | 9,776 | 9,916 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if repo_url is not None:
info.repo_url = repo_url | if_statement | 9,925 | 9,986 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if docs_url is not None:
info.docs_url = docs_url | if_statement | 9,995 | 10,056 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if language is not None:
info.model_card_data.language = language | if_statement | 10,065 | 10,142 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if library_name is not None:
info.model_card_data.library_name = library_name | if_statement | 10,151 | 10,240 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if license is not None:
info.model_card_data.license = license | if_statement | 10,249 | 10,323 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if license_name is not None:
info.model_card_data.license_name = license_name | if_statement | 10,332 | 10,421 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if license_link is not None:
info.model_card_data.license_link = license_link | if_statement | 10,430 | 10,519 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if pipeline_tag is not None:
info.model_card_data.pipeline_tag = pipeline_tag | if_statement | 10,528 | 10,617 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if tags is not None:
if info.model_card_data.tags is not None:
info.model_card_data.tags.extend(tags)
else:
info.model_card_data.tags = tags | if_statement | 10,626 | 10,822 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if info.model_card_data.tags is not None:
info.model_card_data.tags.extend(tags)
else:
info.model_card_data.tags = tags | if_statement | 10,659 | 10,822 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
def __new__(cls: Type[T], *args, **kwargs) -> T:
"""Create a new instance of the class and handle config.
3 cases:
- If `self._hub_mixin_config` is already set, do nothing.
- If `config` is passed as a dataclass, set it as `self._hub_mixin_config`.
- Otherwise, build `self._hub_mixin_config` from default values and passed values.
"""
instance = super().__new__(cls)
# If `config` is already set, return early
if instance._hub_mixin_config is not None:
return instance
# Infer passed values
passed_values = {
**{
key: value
for key, value in zip(
# [1:] to skip `self` parameter
list(cls._hub_mixin_init_parameters)[1:],
args,
)
},
**kwargs,
}
# If config passed as dataclass => set it and return early
if is_dataclass(passed_values.get("config")):
instance._hub_mixin_config = passed_values["config"]
return instance
# Otherwise, build config from default + passed values
init_config = {
# default values
**cls._hub_mixin_jsonable_default_values,
# passed values
**{
key: cls._encode_arg(value) # Encode custom types as jsonable value
for key, value in passed_values.items()
if instance._is_jsonable(value) # Only if jsonable or we have a custom encoder
},
}
passed_config = init_config.pop("config", {})
# Populate `init_config` with provided config
if isinstance(passed_config, dict):
init_config.update(passed_config)
# Set `config` attribute and return
if init_config != {}:
instance._hub_mixin_config = init_config
return instance | function_definition | 11,601 | 13,543 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if instance._hub_mixin_config is not None:
return instance | if_statement | 12,086 | 12,156 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if is_dataclass(passed_values.get("config")):
instance._hub_mixin_config = passed_values["config"]
return instance | if_statement | 12,577 | 12,715 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if isinstance(passed_config, dict):
init_config.update(passed_config) | if_statement | 13,310 | 13,391 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if init_config != {}:
instance._hub_mixin_config = init_config | if_statement | 13,445 | 13,519 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
def _is_jsonable(cls, value: Any) -> bool:
"""Check if a value is JSON serializable."""
if isinstance(value, cls._hub_mixin_jsonable_custom_types):
return True
return is_jsonable(value) | function_definition | 13,566 | 13,787 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if isinstance(value, cls._hub_mixin_jsonable_custom_types):
return True | if_statement | 13,670 | 13,753 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
def _encode_arg(cls, arg: Any) -> Any:
"""Encode an argument into a JSON serializable format."""
for type_, (encoder, _) in cls._hub_mixin_coders.items():
if isinstance(arg, type_):
if arg is None:
return None
return encoder(arg)
return arg | function_definition | 13,810 | 14,138 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
for type_, (encoder, _) in cls._hub_mixin_coders.items():
if isinstance(arg, type_):
if arg is None:
return None
return encoder(arg) | for_statement | 13,923 | 14,119 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if isinstance(arg, type_):
if arg is None:
return None
return encoder(arg) | if_statement | 13,993 | 14,119 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if arg is None:
return None | if_statement | 14,036 | 14,083 | 4 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
def _decode_arg(cls, expected_type: Type[ARGS_T], value: Any) -> Optional[ARGS_T]:
"""Decode a JSON serializable value into an argument."""
if is_simple_optional_type(expected_type):
if value is None:
return None
expected_type = unwrap_simple_optional_type(expected_type)
# Dataclass => handle it
if is_dataclass(expected_type):
return _load_dataclass(expected_type, value) # type: ignore[return-value]
# Otherwise => check custom decoders
for type_, (_, decoder) in cls._hub_mixin_coders.items():
if inspect.isclass(expected_type) and issubclass(expected_type, type_):
return decoder(value)
# Otherwise => don't decode
return value | function_definition | 14,161 | 14,938 | 1 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if is_simple_optional_type(expected_type):
if value is None:
return None
expected_type = unwrap_simple_optional_type(expected_type) | if_statement | 14,317 | 14,488 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if value is None:
return None | if_statement | 14,372 | 14,417 | 3 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
if is_dataclass(expected_type):
return _load_dataclass(expected_type, value) # type: ignore[return-value] | if_statement | 14,530 | 14,648 | 2 | /Users/nielsrogge/Documents/python_projecten/huggingface_hub/src/huggingface_hub/hub_mixin.py | ModelHubMixin |
End of preview. Expand
in Dataset Viewer.
README.md exists but content is empty.
- Downloads last month
- 12