





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
SCREAMING_SNAKE_CASE_		     = """src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE_		     = [
    ("""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     (   _SCREAMING_SNAKE_CASE			)				->     Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		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     (   )				->     Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      SCREAMING_SNAKE_CASE	=		{
          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.
      SCREAMING_SNAKE_CASE	=		collections.defaultdict(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		collections.defaultdict(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		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			):
            SCREAMING_SNAKE_CASE	=		None
            if _re_tf_models.match(_SCREAMING_SNAKE_CASE			) is not None:
                  SCREAMING_SNAKE_CASE	=		tf_models
                  SCREAMING_SNAKE_CASE	=		_re_tf_models.match(_SCREAMING_SNAKE_CASE			).groups()[0]
            elif _re_flax_models.match(_SCREAMING_SNAKE_CASE			) is not None:
                  SCREAMING_SNAKE_CASE	=		flax_models
                  SCREAMING_SNAKE_CASE	=		_re_flax_models.match(_SCREAMING_SNAKE_CASE			).groups()[0]
            elif _re_pt_models.match(_SCREAMING_SNAKE_CASE			) is not None:
                  SCREAMING_SNAKE_CASE	=		pt_models
                  SCREAMING_SNAKE_CASE	=		_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:
                              SCREAMING_SNAKE_CASE	=		True
                              break
                        # Try again after removing the last word in the name
                        SCREAMING_SNAKE_CASE	=		"""""".join(camel_case_split(_SCREAMING_SNAKE_CASE			)[:-1]			)

      SCREAMING_SNAKE_CASE	=		set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
      SCREAMING_SNAKE_CASE	=		list(_SCREAMING_SNAKE_CASE			)
      all_models.sort()

      SCREAMING_SNAKE_CASE	=		{"""model_type""": all_models}
      SCREAMING_SNAKE_CASE	=		[pt_models[t] for t in all_models]
      SCREAMING_SNAKE_CASE	=		[tf_models[t] for t in all_models]
      SCREAMING_SNAKE_CASE	=		[flax_models[t] for t in all_models]

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

      SCREAMING_SNAKE_CASE	=		[processors[t] for t in all_models]

      return pd.DataFrame(_SCREAMING_SNAKE_CASE			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[
          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:
            SCREAMING_SNAKE_CASE	=		[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
            SCREAMING_SNAKE_CASE	=		[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
                  SCREAMING_SNAKE_CASE	=		[]
                  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     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		get_frameworks_table()
      SCREAMING_SNAKE_CASE	=		Dataset.from_pandas(_SCREAMING_SNAKE_CASE			)

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

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      SCREAMING_SNAKE_CASE	=		sorted(table.keys()			)
      SCREAMING_SNAKE_CASE	=		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],
          }			)
      SCREAMING_SNAKE_CASE	=		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:
                  SCREAMING_SNAKE_CASE	=		(
                      F"""Update with commit {commit_sha}\n\nSee: """
                      F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
                  )
            else:
                  SCREAMING_SNAKE_CASE	=		"""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     (   )				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
      SCREAMING_SNAKE_CASE	=		transformers_module.pipelines.SUPPORTED_TASKS
      SCREAMING_SNAKE_CASE	=		[]
      for key in pipeline_tasks:
            if key not in in_table:
                  SCREAMING_SNAKE_CASE	=		pipeline_tasks[key]["""pt"""]
                  if isinstance(_SCREAMING_SNAKE_CASE				,		(list, tuple)			):
                        SCREAMING_SNAKE_CASE	=		model[0]
                  SCREAMING_SNAKE_CASE	=		model.__name__
                  if model not in in_table.values():
                        missing.append(_SCREAMING_SNAKE_CASE			)

      if len(_SCREAMING_SNAKE_CASE			) > 0:
            SCREAMING_SNAKE_CASE	=		""", """.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__":
      SCREAMING_SNAKE_CASE_		     = 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.""")
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      return price * (1 + tax_rate)


if __name__ == "__main__":
      print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
      print(F'''{price_plus_tax(125.50, 0.05) = }''')


