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
a_    					=       """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
a_    					=       direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a_    					=       re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a_    					=       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.
a_    					=       re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a_    					=       [
    ("""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    __lowercase     (						lowerCamelCase					:     int	):
 UpperCamelCase_					:						Tuple =					re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'  ,  SCREAMING_SNAKE_CASE__	)
 return [m.group(0	) for m in matches]





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 UpperCamelCase_					:						Optional[Any] =					{
     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_					:						int =					collections.defaultdict(SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						Tuple =					collections.defaultdict(SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						Any =					collections.defaultdict(SCREAMING_SNAKE_CASE__	)

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

  if lookup_dict is not None:
   while len(SCREAMING_SNAKE_CASE__	) > 0:
    if attr_name in model_prefix_to_model_type:
     UpperCamelCase_					:						List[Any] =					True
     break
    # Try again after removing the last word in the name
    UpperCamelCase_					:						List[str] =					"""""".join(camel_case_split(SCREAMING_SNAKE_CASE__	)[:-1]	)

 UpperCamelCase_					:						Optional[int] =					set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
 UpperCamelCase_					:						Tuple =					list(SCREAMING_SNAKE_CASE__	)
 all_models.sort()

 UpperCamelCase_					:						Any =					{"""model_type""": all_models}
 UpperCamelCase_					:						Tuple =					[pt_models[t] for t in all_models]
 UpperCamelCase_					:						Tuple =					[tf_models[t] for t in all_models]
 UpperCamelCase_					:						Optional[int] =					[flax_models[t] for t in all_models]

 # Now let's use the auto-mapping names to make sure
 UpperCamelCase_					:						str =					{}
 for t in all_models:
  if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
   UpperCamelCase_					:						int =					"""AutoProcessor"""
  elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
   UpperCamelCase_					:						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_					:						List[Any] =					"""AutoTokenizer"""

 UpperCamelCase_					:						int =					[processors[t] for t in all_models]

 return pd.DataFrame(SCREAMING_SNAKE_CASE__	)





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Optional[Any] =					[
     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_					:						Optional[int] =					[model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
  UpperCamelCase_					:						Any =					[auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
  # Loop through all three frameworks
  for module, cls, mapping in zip(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	):
   # The type of pipeline may not exist in this framework
   if not hasattr(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	):
    continue
   # First extract all model_names
   UpperCamelCase_					:						Any =					[]
   for name in getattr(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	).values():
    if isinstance(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	):
     model_names.append(SCREAMING_SNAKE_CASE__	)
    else:
     model_names.extend(list(SCREAMING_SNAKE_CASE__	)	)

            # 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    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str	):
 UpperCamelCase_					:						Union[str, Any] =					get_frameworks_table()
 UpperCamelCase_					:						Tuple =					Dataset.from_pandas(SCREAMING_SNAKE_CASE__	)

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

 # Sort the model classes to avoid some nondeterministic updates to create false update commits.
 UpperCamelCase_					:						Union[str, Any] =					sorted(table.keys()	)
 UpperCamelCase_					:						int =					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_					:						Optional[int] =					Dataset.from_pandas(SCREAMING_SNAKE_CASE__	)

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

  if commit_sha is not None:
   UpperCamelCase_					:						int =					(
       F"Update with commit {commit_sha}\n\nSee: "
       F"https://github.com/huggingface/transformers/commit/{commit_sha}"
   )
  else:
   UpperCamelCase_					:						Dict =					"""Update"""

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





def    __lowercase     (						):
 UpperCamelCase_					:						List[Any] =					{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
 UpperCamelCase_					:						str =					transformers_module.pipelines.SUPPORTED_TASKS
 UpperCamelCase_					:						Optional[Any] =					[]
 for key in pipeline_tasks:
  if key not in in_table:
   UpperCamelCase_					:						str =					pipeline_tasks[key]["""pt"""]
   if isinstance(SCREAMING_SNAKE_CASE__  ,  (list, tuple)	):
    UpperCamelCase_					:						Tuple =					model[0]
   UpperCamelCase_					:						List[Any] =					model.__name__
   if model not in in_table.values():
    missing.append(SCREAMING_SNAKE_CASE__	)

 if len(SCREAMING_SNAKE_CASE__	) > 0:
  UpperCamelCase_					:						List[Any] =					""", """.join(SCREAMING_SNAKE_CASE__	)
  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__":
   a_    					=       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.')
   a_    					=       parser.parse_args()

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





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						_snake_case(  *SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=2				) ->    Optional[Any]:
       from .. import __version__

       lowercase							:     int       	=      take_from
       lowercase							:     Tuple       	=      ()
       if not isinstance(args[0]					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      (args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(SCREAMING_SNAKE_CASE__				).base_version				) >= version.parse(SCREAMING_SNAKE_CASE__				):
                     raise ValueError(
                         f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                         f" version {__version__} is >= {version_name}"				)

              lowercase							:     int       	=      None
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     Union[str, Any]       	=      f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
              elif hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     values += (getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     int       	=      f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
              elif deprecated_kwargs is None:
                     lowercase							:     Dict       	=      f"`{attribute}` is deprecated and will be removed in version {version_name}."

              if warning is not None:
                     lowercase							:     Dict       	=      warning + """ """ if standard_warn else """"""
                     warnings.warn(warning + message					,	SCREAMING_SNAKE_CASE__					,	stacklevel=SCREAMING_SNAKE_CASE__				)

       if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
              lowercase							:     str       	=      inspect.getouterframes(inspect.currentframe()				)[1]
              lowercase							:     List[str]       	=      call_frame.filename
              lowercase							:     Tuple       	=      call_frame.lineno
              lowercase							:     List[str]       	=      call_frame.function
              lowercase	,			lowercase							:     Optional[Any]       	=      next(iter(deprecated_kwargs.items()				)				)
              raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"				)

       if len(SCREAMING_SNAKE_CASE__				) == 0:
              return
       elif len(SCREAMING_SNAKE_CASE__				) == 1:
              return values[0]
       return values
