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__		:					Optional[Any]						=					"src/transformers"


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


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__		:					Optional[int]						=					re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					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__		:					Dict						=					re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__		:					Tuple						=					[
    ("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				(      lowercase__  :			Any ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,  __lowercase )
     return [m.group(0 ) for m in matches]







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     SCREAMING_SNAKE_CASE__				:				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.
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		collections.defaultdict(__lowercase )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		collections.defaultdict(__lowercase )
     SCREAMING_SNAKE_CASE__				:				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 ):
          SCREAMING_SNAKE_CASE__				:				Tuple			=		None
          if _re_tf_models.match(__lowercase ) is not None:
               SCREAMING_SNAKE_CASE__				:				List[str]			=		tf_models
               SCREAMING_SNAKE_CASE__				:				Any			=		_re_tf_models.match(__lowercase ).groups()[0]
          elif _re_flax_models.match(__lowercase ) is not None:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		flax_models
               SCREAMING_SNAKE_CASE__				:				int			=		_re_flax_models.match(__lowercase ).groups()[0]
          elif _re_pt_models.match(__lowercase ) is not None:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		pt_models
               SCREAMING_SNAKE_CASE__				:				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:
                         SCREAMING_SNAKE_CASE__				:				Any			=		True
                         break
                    # Try again after removing the last word in the name
                    SCREAMING_SNAKE_CASE__				:				Dict			=		''.join(camel_case_split(__lowercase )[:-1] )

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

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

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

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

     return pd.DataFrame(__lowercase )







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				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:
          SCREAMING_SNAKE_CASE__				:				Any			=		[model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
          SCREAMING_SNAKE_CASE__				:				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
               SCREAMING_SNAKE_CASE__				:				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				(      lowercase__  :			Optional[int]     ,  lowercase__  :			Union[str, Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		get_frameworks_table()
     SCREAMING_SNAKE_CASE__				:				Any			=		Dataset.from_pandas(__lowercase )

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

     # Sort the model classes to avoid some nondeterministic updates to create false update commits.
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		sorted(table.keys() )
     SCREAMING_SNAKE_CASE__				:				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],
         } )
     SCREAMING_SNAKE_CASE__				:				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:
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		(
                   f'''Update with commit {commit_sha}\n\nSee: '''
                   f'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
               )
          else:
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'Update'

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







def     _a				(      ):







     '''simple docstring'''


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

     if len(__lowercase ) > 0:
          SCREAMING_SNAKE_CASE__				:				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__":
      SCREAMING_SNAKE_CASE__		:					Union[str, 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.")
      SCREAMING_SNAKE_CASE__		:					Optional[int]						=					parser.parse_args()

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




from math import factorial, radians







def     _a				(      lowercase__  :			float     ,  lowercase__  :			int = 18     ,  lowercase__  :			int = 10 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     SCREAMING_SNAKE_CASE__				:				int			=		radians(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		angle_in_radians
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		3
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		-1

     for _ in range(lowercase__ ):
          result += (b * (angle_in_radians**a)) / factorial(lowercase__ )

          SCREAMING_SNAKE_CASE__				:				Any			=		-b  # One positive term and the next will be negative and so on...
          a += 2  # Increased by 2 for every term.

     return round(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      __import__("doctest").testmod()



