






'''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
__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        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      Dict     							= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'  ,		A  )
   return [m.group(0  ) for m in matches]




def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







   """simple docstring"""
   __snake_case				:      List[str]     							= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   __snake_case				:      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.
   __snake_case				:      Optional[Any]     							= collections.defaultdict(A  )
   __snake_case				:      List[str]     							= collections.defaultdict(A  )
   __snake_case				:      Optional[Any]     							= collections.defaultdict(A  )

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

      if lookup_dict is not None:
         while len(A  ) > 0:
            if attr_name in model_prefix_to_model_type:
               __snake_case				:      Optional[int]     							= True
               break
            # Try again after removing the last word in the name
            __snake_case				:      Tuple     							= ''.join(camel_case_split(A  )[:-1]  )

   __snake_case				:      Optional[Any]     							= set(list(pt_models.keys()  ) + list(tf_models.keys()  ) + list(flax_models.keys()  )  )
   __snake_case				:      List[Any]     							= list(A  )
   all_models.sort()

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

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

   __snake_case				:      Optional[int]     							= [processors[t] for t in all_models]

   return pd.DataFrame(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      str     							= [
       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:
      __snake_case				:      Any     							= [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
      __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(A  ,		A  ,		A  ):
         # The type of pipeline may not exist in this framework
         if not hasattr(A  ,		A  ):
            continue
         # First extract all model_names
         __snake_case				:      Any     							= []
         for name in getattr(A  ,		A  ).values():
            if isinstance(A  ,		A  ):
               model_names.append(A  )
            else:
               model_names.extend(list(A  )  )

            # 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        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       List[Any]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= get_frameworks_table()
   __snake_case				:      Optional[Any]     							= Dataset.from_pandas(A  )

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

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   __snake_case				:      Tuple     							= sorted(table.keys()  )
   __snake_case				:      Tuple     							= 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],
       }  )
   __snake_case				:      Any     							= Dataset.from_pandas(A  )

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

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

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




def        _SCREAMING_SNAKE_CASE  (				)    -> Optional[int]:







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

   if len(A  ) > 0:
      __snake_case				:      Union[str, Any]     							= ', '.join(A  )
      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)







'''simple docstring'''






from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any




@dataclass
class a_    :
   _snake_case        =							42
   _snake_case        =							None
   _snake_case        =							None




def        _SCREAMING_SNAKE_CASE  (				)    -> Node | None:







   """simple docstring"""
   __snake_case				:      str     							= Node(1  )
   __snake_case				:      Tuple     							= Node(2  )
   __snake_case				:      Optional[int]     							= Node(3  )
   __snake_case				:      List[str]     							= Node(4  )
   __snake_case				:      List[str]     							= Node(5  )
   return tree




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [root.data, *preorder(root.left  ), *preorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return postorder(root.left  ) + postorder(root.right  ) + [root.data] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [*inorder(root.left  ), root.data, *inorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> int:







   """simple docstring"""
   return (max(height(root.left  )  ,		height(root.right  )  ) + 1) if root else 0




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   if root is None:
      return output

   __snake_case				:      Optional[int]     							= deque([root]  )

   while process_queue:
      __snake_case				:      List[str]     							= process_queue.popleft()
      output.append(node.data  )

      if node.left:
         process_queue.append(node.left  )
      if node.right:
         process_queue.append(node.right  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if not root:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.left  ,		level - 1  )
         populate_output(root.right  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if root is None:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.right  ,		level - 1  )
         populate_output(root.left  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None] | list[Any]:







   """simple docstring"""
   if root is None:
      return []

   __snake_case				:      list[Sequence[Node | None]]     							= []

   __snake_case				:      List[Any]     							= 0
   __snake_case				:      int     							= height(A  )

   for h in range(1  ,		height_tree + 1  ):
      if not flag:
         output.append(get_nodes_from_left_to_right(A  ,		A  )  )
         __snake_case				:      int     							= 1
      else:
         output.append(get_nodes_from_right_to_left(A  ,		A  )  )
         __snake_case				:      Tuple     							= 0

   return output




def        _SCREAMING_SNAKE_CASE  (				)    -> None:  # Main function for testing.







   """simple docstring"""
   __snake_case				:      Optional[int]     							= make_tree()
   print(F"""In-order Traversal: {inorder(A  )}"""  )
   print(F"""Pre-order Traversal: {preorder(A  )}"""  )
   print(F"""Post-order Traversal: {postorder(A  )}"""  ,		'\n'  )

   print(F"""Height of Tree: {height(A  )}"""  ,		'\n'  )

   print('Complete Level Order Traversal: '  )
   print(level_order(A  )  ,		'\n'  )

   print('Level-wise order Traversal: '  )

   for level in range(1  ,		height(A  ) + 1  ):
      print(F"""Level {level}:"""  ,		get_nodes_from_left_to_right(A  ,		level=A  )  )

   print('\nZigZag order Traversal: '  )
   print(zigzag(A  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()