


"""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
snake_case__		:     str    				=				'''src/transformers'''


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
snake_case__		:     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      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	List[str]       =   re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' ,   _snake_case			)
      return [m.group(0			) for m in matches]


def      _snake_case						(						):
      lowerCAmelCase					:	Tuple       =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      lowerCAmelCase					:	str       =   {
          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					:	List[str]       =   collections.defaultdict(_snake_case			)
      lowerCAmelCase					:	List[Any]       =   collections.defaultdict(_snake_case			)
      lowerCAmelCase					:	Tuple       =   collections.defaultdict(_snake_case			)

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

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

      lowerCAmelCase					:	Union[str, Any]       =   set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
      lowerCAmelCase					:	Union[str, Any]       =   list(_snake_case			)
      all_models.sort()

      lowerCAmelCase					:	Dict       =   {'''model_type''': all_models}
      lowerCAmelCase					:	Any       =   [pt_models[t] for t in all_models]
      lowerCAmelCase					:	int       =   [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					:	Any       =   {}
      for t in all_models:
            if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                  lowerCAmelCase					:	int       =   '''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[int]       =   '''AutoFeatureExtractor'''
            else:
                  # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                  lowerCAmelCase					:	List[str]       =   '''AutoTokenizer'''

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

      return pd.DataFrame(_snake_case			)


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

            # 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      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[Any]			):
      lowerCAmelCase					:	Tuple       =   get_frameworks_table()
      lowerCAmelCase					:	int       =   Dataset.from_pandas(_snake_case			)

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

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      lowerCAmelCase					:	int       =   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(_snake_case			)

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

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

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


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

      if len(_snake_case			) > 0:
            lowerCAmelCase					:	Any       =   ''', '''.join(_snake_case			)
            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__":
 snake_case__		:     str    				=				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.''')
 snake_case__		:     Tuple    				=				parser.parse_args()

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






"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case__		:     Tuple    				=				logging.get_logger(__name__)

snake_case__		:     int    				=				{
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''levit'''






     def __init__(	self  :					str					,				UpperCamelCase_  :					Union[str, Any]=2_2_4					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					int=2					,				UpperCamelCase_  :					Union[str, Any]=1					,				UpperCamelCase_  :					Tuple=1_6					,				UpperCamelCase_  :					Dict=[1_2_8, 2_5_6, 3_8_4]					,				UpperCamelCase_  :					Optional[Any]=[4, 8, 1_2]					,				UpperCamelCase_  :					Dict=[4, 4, 4]					,				UpperCamelCase_  :					Any=[1_6, 1_6, 1_6]					,				UpperCamelCase_  :					str=0					,				UpperCamelCase_  :					int=[2, 2, 2]					,				UpperCamelCase_  :					Optional[Any]=[2, 2, 2]					,				UpperCamelCase_  :					str=0.02					,				**UpperCamelCase_  :					List[str]					,				):
           super().__init__(**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   image_size
           lowerCAmelCase					:	int       =   num_channels
           lowerCAmelCase					:	Optional[int]       =   kernel_size
           lowerCAmelCase					:	Dict       =   stride
           lowerCAmelCase					:	List[Any]       =   padding
           lowerCAmelCase					:	Dict       =   hidden_sizes
           lowerCAmelCase					:	List[str]       =   num_attention_heads
           lowerCAmelCase					:	Tuple       =   depths
           lowerCAmelCase					:	Dict       =   key_dim
           lowerCAmelCase					:	Union[str, Any]       =   drop_path_rate
           lowerCAmelCase					:	List[Any]       =   patch_size
           lowerCAmelCase					:	Tuple       =   attention_ratio
           lowerCAmelCase					:	Optional[int]       =   mlp_ratio
           lowerCAmelCase					:	Union[str, Any]       =   initializer_range
           lowerCAmelCase					:	List[str]       =   [
               ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
               ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
           ]



class 		snake_case_(       a__			):
     __UpperCamelCase  =					version.parse('''1.11'''			)






     @property
     def      lowerCamelCase__     (	self  :					Tuple				):
           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ]				)






     @property
     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           return 1E-4


