





import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__lowerCamelCase :  Any										=  """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase :  Optional[Any]										=  direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__lowerCamelCase :  List[Any]										=  re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__lowerCamelCase :  Optional[int]										=  re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCamelCase :  List[str]										=  re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCamelCase :  List[Any]										=  [
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def 	A_       (  _lowerCAmelCase  )		->				Tuple:
 UpperCamelCase			:      Any 			=     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,     __lowercase  )
 return [m.group(0  ) for m in matches]



def 	A_       (  )		->				Any:
 UpperCamelCase			:      Union[str, Any] 			=     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 UpperCamelCase			:      Dict 			=     {
     config.replace("Config"							,     ""  ): model_type for model_type, config in config_maping_names.items()
 }

 # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
 UpperCamelCase			:      List[Any] 			=     collections.defaultdict(__lowercase  )
 UpperCamelCase			:      Optional[int] 			=     collections.defaultdict(__lowercase  )
 UpperCamelCase			:      Tuple 			=     collections.defaultdict(__lowercase  )

 # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
 for attr_name in dir(__lowercase  ):
  UpperCamelCase			:      Tuple 			=     None
  if _re_tf_models.match(__lowercase  ) is not None:
   UpperCamelCase			:      List[str] 			=     tf_models
   UpperCamelCase			:      Any 			=     _re_tf_models.match(__lowercase  ).groups()[0]
  elif _re_flax_models.match(__lowercase  ) is not None:
   UpperCamelCase			:      Tuple 			=     flax_models
   UpperCamelCase			:      int 			=     _re_flax_models.match(__lowercase  ).groups()[0]
  elif _re_pt_models.match(__lowercase  ) is not None:
   UpperCamelCase			:      Tuple 			=     pt_models
   UpperCamelCase			:      Tuple 			=     _re_pt_models.match(__lowercase  ).groups()[0]

  if lookup_dict is not None:
   while len(__lowercase  ) > 0:
    if attr_name in model_prefix_to_model_type:
     UpperCamelCase			:      Any 			=     True
     break
    # Try again after removing the last word in the name
    UpperCamelCase			:      Dict 			=     ''.join(camel_case_split(__lowercase  )[:-1]  )

 UpperCamelCase			:      Any 			=     set(list(pt_models.keys()  ) + list(tf_models.keys()  ) + list(flax_models.keys()  )  )
 UpperCamelCase			:      Tuple 			=     list(__lowercase  )
 all_models.sort()

 UpperCamelCase			:      str 			=     {'model_type': all_models}
 UpperCamelCase			:      Optional[int] 			=     [pt_models[t] for t in all_models]
 UpperCamelCase			:      str 			=     [tf_models[t] for t in all_models]
 UpperCamelCase			:      List[Any] 			=     [flax_models[t] for t in all_models]

 # Now let's use the auto-mapping names to make sure
 UpperCamelCase			:      List[Any] 			=     {}
 for t in all_models:
  if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
   UpperCamelCase			:      List[Any] 			=     'AutoProcessor'
  elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
   UpperCamelCase			:      Optional[int] 			=     'AutoTokenizer'
  elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
   UpperCamelCase			:      List[str] 			=     'AutoFeatureExtractor'
  else:
   # Default to AutoTokenizer if a model has nothing, for backward compatibility.
   UpperCamelCase			:      Dict 			=     'AutoTokenizer'

 UpperCamelCase			:      List[Any] 			=     [processors[t] for t in all_models]

 return pd.DataFrame(__lowercase  )



def 	A_       (  _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     [
     transformers_module.models.auto.modeling_auto,
     transformers_module.models.auto.modeling_tf_auto,
     transformers_module.models.auto.modeling_flax_auto,
 ]
 for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
  UpperCamelCase			:      Any 			=     [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
  UpperCamelCase			:      Optional[Any] 			=     [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
  # Loop through all three frameworks
  for module, cls, mapping in zip(__lowercase							,     __lowercase							,     __lowercase  ):
   # The type of pipeline may not exist in this framework
   if not hasattr(__lowercase							,     __lowercase  ):
    continue
   # First extract all model_names
   UpperCamelCase			:      Optional[int] 			=     []
   for name in getattr(__lowercase							,     __lowercase  ).values():
    if isinstance(__lowercase							,     __lowercase  ):
     model_names.append(__lowercase  )
    else:
     model_names.extend(list(__lowercase  )  )

            # Add pipeline tag and auto model class for those models
   table.update({model_name: (pipeline_tag, cls) for model_name in model_names}  )

 return table



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Any 			=     get_frameworks_table()
 UpperCamelCase			:      Any 			=     Dataset.from_pandas(__lowercase  )

 UpperCamelCase			:      Any 			=     hf_hub_download(
     "huggingface/transformers-metadata"							,     "pipeline_tags.json"							,     repo_type="dataset"							,     token=__lowercase  )
 UpperCamelCase			:      Dict 			=     Dataset.from_json(__lowercase  )
 UpperCamelCase			:      Any 			=     {
     tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
     for i in range(len(__lowercase  )  )
 }
 UpperCamelCase			:      str 			=     update_pipeline_and_auto_class_table(__lowercase  )

 # Sort the model classes to avoid some nondeterministic updates to create false update commits.
 UpperCamelCase			:      Optional[int] 			=     sorted(table.keys()  )
 UpperCamelCase			:      Union[str, Any] 			=     pd.DataFrame(
     {
         "model_class": model_classes,
         "pipeline_tag": [table[m][0] for m in model_classes],
         "auto_class": [table[m][1] for m in model_classes],
     }  )
 UpperCamelCase			:      Any 			=     Dataset.from_pandas(__lowercase  )

 with tempfile.TemporaryDirectory() as tmp_dir:
  frameworks_dataset.to_json(os.path.join(__lowercase							,     "frameworks.json"  )  )
  tags_dataset.to_json(os.path.join(__lowercase							,     "pipeline_tags.json"  )  )

  if commit_sha is not None:
   UpperCamelCase			:      Optional[Any] 			=     (
       F"""Update with commit {commit_sha}\n\nSee: """
       F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
   )
  else:
   UpperCamelCase			:      Optional[int] 			=     'Update'

  upload_folder(
      repo_id="huggingface/transformers-metadata"							,     folder_path=__lowercase							,     repo_type="dataset"							,     token=__lowercase							,     commit_message=__lowercase							,     )



def 	A_       (  )		->				int:
 UpperCamelCase			:      str 			=     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
 UpperCamelCase			:      List[Any] 			=     transformers_module.pipelines.SUPPORTED_TASKS
 UpperCamelCase			:      Optional[int] 			=     []
 for key in pipeline_tasks:
  if key not in in_table:
   UpperCamelCase			:      str 			=     pipeline_tasks[key]['pt']
   if isinstance(__lowercase							,     (list, tuple)  ):
    UpperCamelCase			:      Dict 			=     model[0]
   UpperCamelCase			:      Optional[int] 			=     model.__name__
   if model not in in_table.values():
    missing.append(__lowercase  )

 if len(__lowercase  ) > 0:
  UpperCamelCase			:      Any 			=     ', '.join(__lowercase  )
  raise ValueError(
      "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
      F"""`utils/update_metadata.py`: {msg}. Please add them!"""  )


if __name__ == "__main__":
      __lowerCamelCase :  Any										=  argparse.ArgumentParser()
      parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
      parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
      parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
      __lowerCamelCase :  List[str]										=  parser.parse_args()

      if args.check_only:
            check_pipeline_tags()
      else:
            update_metadata(args.token, args.commit_sha)







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__lowerCamelCase :  Dict										=  logging.get_logger(__name__)

__lowerCamelCase :  Union[str, Any]										=  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__lowerCamelCase :  Dict										=  {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__lowerCamelCase :  Tuple										=  {
    """facebook/blenderbot_small-90M""": 512,
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:Dict							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:List[Any]							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Optional[Any]							=     BlenderbotSmallTokenizer





      def __init__(				self ,							A_=None ,							A_=None ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_=False ,							A_=True ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           ByteLevelBPETokenizer(
               vocab=A_ ,							merges=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_ ,							) ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							**A_ ,							)
       UpperCamelCase			:      Union[str, Any] 			=     add_prefix_space





      def  __UpperCamelCase(				self ,							A_ ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      int 			=     [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep     ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]
