



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
lowerCamelCase	:	Dict				=   'src/transformers'


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


# Regexes that match TF/Flax/PT model names.
lowerCamelCase	:	Tuple				=   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCamelCase	:	Optional[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.
lowerCamelCase	:	int				=   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase	:	List[str]				=   [
    ('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__ (   lowercase					)  ->       Optional[int]:
   snake_case				:    Optional[int]           =	re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""						,lowercase					)
   return [m.group(0					) for m in matches]




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Optional[int]:
   snake_case				:    List[str]           =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   snake_case				:    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				:    Any           =	collections.defaultdict(lowercase					)
   snake_case				:    Tuple           =	collections.defaultdict(lowercase					)
   snake_case				:    Any           =	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					):
      snake_case				:    List[Any]           =	None
      if _re_tf_models.match(lowercase					) is not None:
         snake_case				:    List[Any]           =	tf_models
         snake_case				:    List[Any]           =	_re_tf_models.match(lowercase					).groups()[0]
      elif _re_flax_models.match(lowercase					) is not None:
         snake_case				:    Any           =	flax_models
         snake_case				:    Optional[Any]           =	_re_flax_models.match(lowercase					).groups()[0]
      elif _re_pt_models.match(lowercase					) is not None:
         snake_case				:    str           =	pt_models
         snake_case				:    Dict           =	_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:
               snake_case				:    Any           =	True
               break
            # Try again after removing the last word in the name
            snake_case				:    List[Any]           =	"""""".join(camel_case_split(lowercase					)[:-1]					)

   snake_case				:    Any           =	set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
   snake_case				:    str           =	list(lowercase					)
   all_models.sort()

   snake_case				:    Union[str, Any]           =	{"""model_type""": all_models}
   snake_case				:    int           =	[pt_models[t] for t in all_models]
   snake_case				:    Union[str, Any]           =	[tf_models[t] for t in all_models]
   snake_case				:    List[Any]           =	[flax_models[t] for t in all_models]

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

   snake_case				:    List[str]           =	[processors[t] for t in all_models]

   return pd.DataFrame(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   snake_case				:    List[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:
      snake_case				:    Dict           =	[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(lowercase						,lowercase						,lowercase					):
         # The type of pipeline may not exist in this framework
         if not hasattr(lowercase						,lowercase					):
            continue
         # First extract all model_names
         snake_case				:    Tuple           =	[]
         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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[int]:
   snake_case				:    Union[str, Any]           =	get_frameworks_table()
   snake_case				:    Optional[Any]           =	Dataset.from_pandas(lowercase					)

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

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

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




def 						SCREAMING_SNAKE_CASE__ (   )  ->       List[Any]:
   snake_case				:    Tuple           =	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   snake_case				:    Dict           =	transformers_module.pipelines.SUPPORTED_TASKS
   snake_case				:    List[str]           =	[]
   for key in pipeline_tasks:
      if key not in in_table:
         snake_case				:    List[Any]           =	pipeline_tasks[key]["""pt"""]
         if isinstance(lowercase						,(list, tuple)					):
            snake_case				:    Tuple           =	model[0]
         snake_case				:    List[str]           =	model.__name__
         if model not in in_table.values():
            missing.append(lowercase					)

   if len(lowercase					) > 0:
      snake_case				:    List[str]           =	""", """.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__":
       lowerCamelCase	:	Union[str, 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.')
       lowerCamelCase	:	Union[str, Any]				=   parser.parse_args()

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











# using dfs for finding eulerian path traversal




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=None					)  ->       Any:
   snake_case				:    Union[str, Any]           =	(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         snake_case     , snake_case				:    int           =	True, True
         snake_case				:    List[Any]           =	dfs(lowercase						,lowercase						,lowercase						,lowercase					)
   return path




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Union[str, Any]           =	0
   snake_case				:    Union[str, Any]           =	-1
   for i in range(lowercase					):
      if i not in graph.keys():
         continue
      if len(graph[i]					) % 2 == 1:
         odd_degree_nodes += 1
         snake_case				:    str           =	i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    Tuple           =	[[False for _ in range(max_node + 1					)] for _ in range(max_node + 1					)]
   snake_case     , snake_case				:    Any           =	check_circuit_or_path(lowercase						,lowercase					)
   if check == 3:
      print("""graph is not Eulerian"""					)
      print("""no path"""					)
      return
   snake_case				:    str           =	1
   if check == 2:
      snake_case				:    Optional[int]           =	odd_node
      print("""graph has a Euler path"""					)
   if check == 1:
      print("""graph has a Euler cycle"""					)
   snake_case				:    Dict           =	dfs(lowercase						,lowercase						,lowercase					)
   print(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Optional[Any]:
   snake_case				:    Union[str, Any]           =	{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   snake_case				:    List[Any]           =	{1: [2, 3], 2: [1, 3], 3: [1, 2]}
   snake_case				:    Tuple           =	{
       1: [],
       2: []
       # all degree is zero
   }
   snake_case				:    Tuple           =	10
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)


if __name__ == "__main__":
       main()





