Support Streaming
Browse files- tokenization_qwen.py +16 -2
tokenization_qwen.py
CHANGED
@@ -27,6 +27,12 @@ logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken", "ttf": "SimSun.ttf"}
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PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
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ENDOFTEXT = "<|endoftext|>"
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@@ -147,6 +153,10 @@ class QWenTokenizer(PreTrainedTokenizer):
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self.box_end_id = self.special_tokens[self.box_end_tag]
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self.quad_start_id = self.special_tokens[self.quad_start_tag]
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self.quad_end_id = self.special_tokens[self.quad_end_tag]
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enc = tiktoken.Encoding(
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"Qwen",
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@@ -348,7 +358,11 @@ class QWenTokenizer(PreTrainedTokenizer):
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token_ids = _replace_closed_tag(token_ids, self.img_start_id, self.img_end_id, _decode_imgurl)
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if skip_special_tokens:
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-
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return self.tokenizer.decode(token_ids, errors=errors or self.errors)
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def to_list_format(self, text: str):
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@@ -515,7 +529,7 @@ class VisImage:
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class Visualizer:
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def __init__(self, img_rgb, metadata=None, scale=1.0):
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self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
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self.font_path =
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self.output = VisImage(self.img, scale=scale)
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self.cpu_device = torch.device("cpu")
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VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken", "ttf": "SimSun.ttf"}
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FONT_PATH = try_to_load_from_cache("Qwen/Qwen-VL-Chat", "SimSun.ttf")
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if FONT_PATH is None:
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if not os.path.exists("SimSun.ttf"):
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ttf = requests.get("https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/SimSun.ttf")
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open("SimSun.ttf", "wb").write(ttf.content)
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FONT_PATH = "SimSun.ttf"
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PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
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ENDOFTEXT = "<|endoftext|>"
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self.box_end_id = self.special_tokens[self.box_end_tag]
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self.quad_start_id = self.special_tokens[self.quad_start_tag]
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self.quad_end_id = self.special_tokens[self.quad_end_tag]
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self.image_special_tokens = set([
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self.ref_start_id, self.ref_end_id, self.box_start_id, self.box_end_id,
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self.quad_start_id, self.quad_end_id,
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])
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enc = tiktoken.Encoding(
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"Qwen",
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token_ids = _replace_closed_tag(token_ids, self.img_start_id, self.img_end_id, _decode_imgurl)
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if skip_special_tokens:
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if kwargs.get('keep_image_special', False):
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token_ids = [i for i in token_ids if i < self.eod_id
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or i in self.image_special_tokens]
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else:
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token_ids = [i for i in token_ids if i < self.eod_id]
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return self.tokenizer.decode(token_ids, errors=errors or self.errors)
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def to_list_format(self, text: str):
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class Visualizer:
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def __init__(self, img_rgb, metadata=None, scale=1.0):
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self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
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self.font_path = FONT_PATH
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self.output = VisImage(self.img, scale=scale)
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self.cpu_device = torch.device("cpu")
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