


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
UpperCAmelCase__  							="src/transformers"


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


# Regexes that match TF/Flax/PT model names.
UpperCAmelCase__  							=re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
UpperCAmelCase__  							=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.
UpperCAmelCase__  							=re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCAmelCase__  							=[
    ("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 lowerCAmelCase_      (				UpperCamelCase__	: Any ):






 """simple docstring"""







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


def lowerCAmelCase_      (				):






 """simple docstring"""







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

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

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

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

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

 return pd.DataFrame(__lowercase )


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[
     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									=	[model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
  __lowercase									=	[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
   __lowercase									=	[]
   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 lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: Union[str, Any] ):






 """simple docstring"""







 __lowercase									=	get_frameworks_table()
 __lowercase									=	Dataset.from_pandas(__lowercase )

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

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

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


def lowerCAmelCase_      (				):






 """simple docstring"""







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

 if len(__lowercase ) > 0:
  __lowercase									=	', '.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__":
       UpperCAmelCase__  							=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.")
       UpperCAmelCase__  							=parser.parse_args()

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




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 assert x is not None
 assert y is not None

 __lowercase									=	len(UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

 # declaring the array for storing the dp values
 __lowercase									=	[[0] * (n + 1) for _ in range(m + 1 )]  # noqa: E741

 for i in range(1				,   m + 1 ):
  for j in range(1				,   n + 1 ):
   __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

   __lowercase									=	max(l[i - 1][j]				,   l[i][j - 1]				,   l[i - 1][j - 1] + match )

 __lowercase									=	""""""
 __lowercase							,		__lowercase									=	m, n
 while i > 0 and j > 0:
  __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

  if l[i][j] == l[i - 1][j - 1] + match:
   if match == 1:
    __lowercase									=	x[i - 1] + seq
   i -= 1
   j -= 1
  elif l[i][j] == l[i - 1][j]:
   i -= 1
  else:
   j -= 1

 return l[m][n], seq


if __name__ == "__main__":
       UpperCAmelCase__  							="AGGTAB"
       UpperCAmelCase__  							="GXTXAYB"
       UpperCAmelCase__  							=4
       UpperCAmelCase__  							="GTAB"

       UpperCAmelCase__							,      UpperCAmelCase__  							=longest_common_subsequence(a, b)
       print("len =", ln, ", sub-sequence =", subseq)
       import doctest

       doctest.testmod()
