


"""simple docstring"""







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__				    =       '''src/transformers'''


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase__				    =       [
    ('''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__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,				SCREAMING_SNAKE_CASE    )
  return [m.group(0    ) for m in matches]
def  a__      (    ):


  '''simple docstring'''







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

  lowerCAmelCase					:					str								=							set(list(pt_models.keys()    ) + list(tf_models.keys()    ) + list(flax_models.keys()    )    )
  lowerCAmelCase					:					List[str]								=							list(SCREAMING_SNAKE_CASE    )
  all_models.sort()

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

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

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

  return pd.DataFrame(SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					int								=							[
      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:
    lowerCAmelCase					:					Optional[Any]								=							[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    lowerCAmelCase					:					Dict								=							[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
      lowerCAmelCase					:					str								=							[]
      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  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[str]								=							get_frameworks_table()
  lowerCAmelCase					:					Any								=							Dataset.from_pandas(SCREAMING_SNAKE_CASE    )

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

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


  '''simple docstring'''







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

  if len(SCREAMING_SNAKE_CASE    ) > 0:
    lowerCAmelCase					:					Dict								=							", ".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__":
     lowerCAmelCase__				    =       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__				    =       parser.parse_args()

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









"""simple docstring"""







from typing import Any


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							data
         lowerCAmelCase					:					Tuple								=							None


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							None

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.head
         while temp is not None:
           print(temp.data		,					end=" "						)
           lowerCAmelCase					:					List[str]								=							temp.next
         print()

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							Node(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							self.head
         lowerCAmelCase					:					Optional[Any]								=							new_node







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         if node_data_a == node_data_a:
           return
         else:
           lowerCAmelCase					:					str								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					Union[str, Any]								=							node_a.next

           lowerCAmelCase					:					Any								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					List[Any]								=							node_a.next

           if node_a is None or node_a is None:
             return

           lowerCAmelCase						,  lowerCAmelCase					:					str								=							node_a.data, node_a.data


if __name__ == "__main__":
     lowerCAmelCase__				    =       LinkedList()
     for i in range(5, 0, -1):
          ll.push(i)

     ll.print_list()

     ll.swap_nodes(1, 4)
     print('''After swapping''')
     ll.print_list()





