





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 :   Tuple  			=  'src/transformers'


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_A :   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						(   UpperCAmelCase					)    ->    int:







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



def 	_a						(   )    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   lowerCamelCase__       :						Optional[int]        =							{
       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__       :						int        =							collections.defaultdict(UpperCAmelCase					)
   lowerCamelCase__       :						Optional[Any]        =							collections.defaultdict(UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							collections.defaultdict(UpperCAmelCase					)

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

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

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

   lowerCamelCase__       :						Union[str, Any]        =							{'''model_type''': all_models}
   lowerCamelCase__       :						List[Any]        =							[pt_models[t] for t in all_models]
   lowerCamelCase__       :						Union[str, Any]        =							[tf_models[t] for t in all_models]
   lowerCamelCase__       :						str        =							[flax_models[t] for t in all_models]

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

   lowerCamelCase__       :						Tuple        =							[processors[t] for t in all_models]

   return pd.DataFrame(UpperCAmelCase					)



def 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							[
       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__       :						Any        =							[model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"]
      lowerCamelCase__       :						str        =							[auto_class, f"TF_{auto_class}", f"Flax_{auto_class}"]
      # Loop through all three frameworks
      for module, cls, mapping in zip(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					):
         # The type of pipeline may not exist in this framework
         if not hasattr(UpperCAmelCase      ,  UpperCAmelCase					):
            continue
         # First extract all model_names
         lowerCamelCase__       :						int        =							[]
         for name in getattr(UpperCAmelCase      ,  UpperCAmelCase					).values():
            if isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
               model_names.append(UpperCAmelCase					)
            else:
               model_names.extend(list(UpperCAmelCase					)					)

            # 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						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							get_frameworks_table()
   lowerCamelCase__       :						str        =							Dataset.from_pandas(UpperCAmelCase					)

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

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   lowerCamelCase__       :						List[str]        =							sorted(table.keys()					)
   lowerCamelCase__       :						Optional[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[int]        =							Dataset.from_pandas(UpperCAmelCase					)

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

      if commit_sha is not None:
         lowerCamelCase__       :						List[Any]        =							(
             f"Update with commit {commit_sha}\n\nSee: "
             f"https://github.com/huggingface/transformers/commit/{commit_sha}"
         )
      else:
         lowerCamelCase__       :						Dict        =							'''Update'''

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



def 	_a						(   )    ->    int:







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

   if len(UpperCAmelCase					) > 0:
      lowerCamelCase__       :						List[str]        =							''', '''.join(UpperCAmelCase					)
      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 :   int  			=  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 :   Any  			=  parser.parse_args()

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










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







   """simple docstring"""
   if deriv:
      return value * (1 - value)
   return 1 / (1 + math.exp(-value					))


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, Any]        =							layer_1_error * sigmoid_function(UpperCAmelCase      ,  UpperCAmelCase					)
      # Update weight
      weight += INITIAL_VALUE * layer_1_delta

   return layer_a * 100


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



