





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__:    List[str]  	=	'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
A__:    Optional[Any]  	=	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__:    Dict  	=	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__:    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 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	List[str]					=     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,_lowerCAmelCase)
    return [m.group(0) for m in matches]



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	Any					=     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    UpperCamelCase__:	Tuple					=     {
        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__:	Union[str, Any]					=     collections.defaultdict(_lowerCAmelCase)
    UpperCamelCase__:	Tuple					=     collections.defaultdict(_lowerCAmelCase)
    UpperCamelCase__:	Union[str, Any]					=     collections.defaultdict(_lowerCAmelCase)

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

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

    UpperCamelCase__:	List[Any]					=     set(list(pt_models.keys()) + list(tf_models.keys()) + list(flax_models.keys()))
    UpperCamelCase__:	int					=     list(_lowerCAmelCase)
    all_models.sort()

    UpperCamelCase__:	List[str]					=     {'model_type': all_models}
    UpperCamelCase__:	Dict					=     [pt_models[t] for t in all_models]
    UpperCamelCase__:	Tuple					=     [tf_models[t] for t in all_models]
    UpperCamelCase__:	str					=     [flax_models[t] for t in all_models]

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

    UpperCamelCase__:	List[Any]					=     [processors[t] for t in all_models]

    return pd.DataFrame(_lowerCAmelCase)



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	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:
        UpperCamelCase__:	str					=     [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
        UpperCamelCase__:	List[Any]					=     [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
        # Loop through all three frameworks
        for module, cls, mapping in zip(_lowerCAmelCase    ,_lowerCAmelCase    ,_lowerCAmelCase):
            # The type of pipeline may not exist in this framework
            if not hasattr(_lowerCAmelCase    ,_lowerCAmelCase):
                continue
            # First extract all model_names
            UpperCamelCase__:	List[Any]					=     []
            for name in getattr(_lowerCAmelCase    ,_lowerCAmelCase).values():
                if isinstance(_lowerCAmelCase    ,_lowerCAmelCase):
                    model_names.append(_lowerCAmelCase)
                else:
                    model_names.extend(list(_lowerCAmelCase))

                      # 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_ (							A_    ,A_):
    UpperCamelCase__:	int					=     get_frameworks_table()
    UpperCamelCase__:	Optional[int]					=     Dataset.from_pandas(_lowerCAmelCase)

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

    # Sort the model classes to avoid some nondeterministic updates to create false update commits.
    UpperCamelCase__:	Optional[Any]					=     sorted(table.keys())
    UpperCamelCase__:	List[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],
        })
    UpperCamelCase__:	Tuple					=     Dataset.from_pandas(_lowerCAmelCase)

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

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

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



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	Tuple					=     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
    UpperCamelCase__:	Tuple					=     transformers_module.pipelines.SUPPORTED_TASKS
    UpperCamelCase__:	List[Any]					=     []
    for key in pipeline_tasks:
        if key not in in_table:
            UpperCamelCase__:	str					=     pipeline_tasks[key]['pt']
            if isinstance(_lowerCAmelCase    ,(list, tuple)):
                UpperCamelCase__:	int					=     model[0]
            UpperCamelCase__:	List[Any]					=     model.__name__
            if model not in in_table.values():
                missing.append(_lowerCAmelCase)

    if len(_lowerCAmelCase) > 0:
        UpperCamelCase__:	Any					=     ', '.join(_lowerCAmelCase)
        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__:    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.''')
   A__:    Dict  	=	parser.parse_args()

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






'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






