'''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:			int	       =       """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__a:			List[str]	       =       direct_transformers_import(TRANSFORMERS_PATH)


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__a:			Dict	       =       [
    ("""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       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''		,					UpperCAmelCase					)
 return [m.group(0					) for m in matches]
def       __UpperCamelCase	(       ):
 lowercase__    :   Optional[int]						= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowercase__    :   Union[str, 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.
 lowercase__    :   str						= collections.defaultdict(UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= collections.defaultdict(UpperCAmelCase					)
 lowercase__    :   Optional[int]						= 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					):
  lowercase__    :   str						= None
  if _re_tf_models.match(UpperCAmelCase					) is not None:
   lowercase__    :   Optional[int]						= tf_models
   lowercase__    :   Union[str, Any]						= _re_tf_models.match(UpperCAmelCase					).groups()[0]
  elif _re_flax_models.match(UpperCAmelCase					) is not None:
   lowercase__    :   int						= flax_models
   lowercase__    :   Union[str, Any]						= _re_flax_models.match(UpperCAmelCase					).groups()[0]
  elif _re_pt_models.match(UpperCAmelCase					) is not None:
   lowercase__    :   int						= pt_models
   lowercase__    :   List[str]						= _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:
     lowercase__    :   Any						= True
     break
    # Try again after removing the last word in the name
    lowercase__    :   int						= ''''''.join(camel_case_split(UpperCAmelCase					)[:-1]					)

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

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

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

 lowercase__    :   Any						= [processors[t] for t in all_models]

 return pd.DataFrame(UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   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:
  lowercase__    :   Optional[int]						= [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
  lowercase__    :   Optional[int]						= [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
   lowercase__    :   Optional[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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= get_frameworks_table()
 lowercase__    :   Union[str, Any]						= Dataset.from_pandas(UpperCAmelCase					)

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

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

  upload_folder(
      repo_id='''huggingface/transformers-metadata'''		,					folder_path=UpperCAmelCase		,					repo_type='''dataset'''		,					token=UpperCAmelCase		,					commit_message=UpperCAmelCase		,					)
def       __UpperCamelCase	(       ):
 lowercase__    :   Dict						= {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
 lowercase__    :   Dict						= transformers_module.pipelines.SUPPORTED_TASKS
 lowercase__    :   List[str]						= []
 for key in pipeline_tasks:
  if key not in in_table:
   lowercase__    :   Tuple						= pipeline_tasks[key]['''pt''']
   if isinstance(UpperCAmelCase		,					(list, tuple)					):
    lowercase__    :   int						= model[0]
   lowercase__    :   List[str]						= model.__name__
   if model not in in_table.values():
    missing.append(UpperCAmelCase					)

 if len(UpperCAmelCase					) > 0:
  lowercase__    :   Union[str, Any]						= ''', '''.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:			Dict	       =       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:			List[str]	       =       parser.parse_args()

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


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase					):
 if not all(x.isalpha() for x in string					):
  raise ValueError('''String must only contain alphabetic characters.'''					)

 lowercase__    :   Tuple						= sorted(string.lower()					)
 return len(UpperCAmelCase					) == len(set(UpperCAmelCase					)					)


if __name__ == "__main__":
  __a:			Union[str, Any]	       =       input("""Enter a string """).strip()

  __a:			Tuple	       =       is_isogram(input_str)
  print(F'{input_str} is {"an" if isogram else "not an"} isogram.')

