





'''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
lowercase__		:     Dict			=							'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowercase__		:     Optional[int]			=							direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowercase__		:     Tuple			=							re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowercase__		:     int			=							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.
lowercase__		:     List[str]			=							re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowercase__		:     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   :	Dict )						->						Optional[int]:





     """simple docstring"""

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







def    a__							(							)						->						int:





     """simple docstring"""

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

     _UpperCamelCase							       =			set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
     _UpperCamelCase							       =			list(lowercase )
     all_models.sort()

     _UpperCamelCase							       =			{'''model_type''': all_models}
     _UpperCamelCase							       =			[pt_models[t] for t in all_models]
     _UpperCamelCase							       =			[tf_models[t] for t in all_models]
     _UpperCamelCase							       =			[flax_models[t] for t in all_models]

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

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

     return pd.DataFrame(lowercase )







def    a__							(							lowercase   :	str )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			[
         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							       =			[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
          _UpperCamelCase							       =			[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
               _UpperCamelCase							       =			[]
               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[Any],   lowercase   :	Any )						->						List[str]:





     """simple docstring"""

     _UpperCamelCase							       =			get_frameworks_table()
     _UpperCamelCase							       =			Dataset.from_pandas(lowercase )

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

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

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







def    a__							(							)						->						Any:





     """simple docstring"""

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

     if len(lowercase ) > 0:
          _UpperCamelCase							       =			''', '''.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__":
   lowercase__		:     Optional[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.')
   lowercase__		:     Any			=							parser.parse_args()

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










'''simple docstring'''

from __future__ import annotations

import queue
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			data
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None







def    a__							(							)						->						TreeNode:





     """simple docstring"""

     print('''\n********Press N to stop entering at any point of time********\n''' )
     _UpperCamelCase							       =			input('''Enter the value of the root node: ''' ).strip().lower()
     _UpperCamelCase							       =			queue.Queue()
     _UpperCamelCase							       =			TreeNode(int(lowercase ) )
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          _UpperCamelCase							       =			F"""Enter the left node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			left_node
          q.put(lowercase )
          _UpperCamelCase							       =			F"""Enter the right node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			right_node
          q.put(lowercase )
     raise







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     print(node.data,   end=''',''' )
     pre_order(node.left )
     pre_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     in_order(node.left )
     print(node.data,   end=''',''' )
     in_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     post_order(node.left )
     post_order(node.right )
     print(node.data,   end=''',''' )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          print(node_dequeued.data,   end=''',''' )
          if node_dequeued.left:
               q.put(node_dequeued.left )
          if node_dequeued.right:
               q.put(node_dequeued.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			[]
          while not q.empty():
               _UpperCamelCase							       =			q.get()
               print(node_dequeued.data,   end=''',''' )
               if node_dequeued.left:
                    list_.append(node_dequeued.left )
               if node_dequeued.right:
                    list_.append(node_dequeued.right )
          print()
          for node in list_:
               q.put(lowercase )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data,   end=''',''' )
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          # end of while means current node doesn't have left child
          _UpperCamelCase							       =			stack.pop()
          # start to traverse its right child
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          _UpperCamelCase							       =			stack.pop()
          print(n.data,   end=''',''' )
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase       ,							_UpperCamelCase							       =			[], []
     _UpperCamelCase							       =			node
     stacka.append(lowercase )
     while stacka:  # to find the reversed order of post order, store it in stack2
          _UpperCamelCase							       =			stacka.pop()
          if n.left:
               stacka.append(n.left )
          if n.right:
               stacka.append(n.right )
          stacka.append(lowercase )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data,   end=''',''' )







def    a__							(							lowercase   :	str = "",   lowercase   :	List[str]=50,   lowercase   :	List[str]="*" )						->						str:





     """simple docstring"""

     if not s:
          return "\n" + width * char
     _UpperCamelCase       ,							_UpperCamelCase							       =			divmod(width - len(lowercase ) - 2,   2 )
     return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(prompt('Binary Tree Traversals'))

   lowercase__		:     TreeNode			=							build_tree()
   print(prompt('Pre Order Traversal'))
   pre_order(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal'))
   in_order(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal'))
   post_order(node)
   print(prompt() + '\n')

   print(prompt('Level Order Traversal'))
   level_order(node)
   print(prompt() + '\n')

   print(prompt('Actual Level Order Traversal'))
   level_order_actual(node)
   print('*' * 50 + '\n')

   print(prompt('Pre Order Traversal - Iteration Version'))
   pre_order_iter(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal - Iteration Version'))
   in_order_iter(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal - Iteration Version'))
   post_order_iter(node)
   print(prompt())



