


"""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				=				'''src/transformers'''


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__snake_case				=				[
    ('''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 					A_      (      _lowerCAmelCase   :    str				):






     """simple docstring"""

     _a					= re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''',   SCREAMING_SNAKE_CASE_				)
     return [m.group(0				) for m in matches]
def 					A_      (      ):






     """simple docstring"""

     _a					= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     _a					= {
         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.
     _a					= collections.defaultdict(SCREAMING_SNAKE_CASE_				)
     _a					= collections.defaultdict(SCREAMING_SNAKE_CASE_				)
     _a					= collections.defaultdict(SCREAMING_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(SCREAMING_SNAKE_CASE_				):
          _a					= None
          if _re_tf_models.match(SCREAMING_SNAKE_CASE_				) is not None:
               _a					= tf_models
               _a					= _re_tf_models.match(SCREAMING_SNAKE_CASE_				).groups()[0]
          elif _re_flax_models.match(SCREAMING_SNAKE_CASE_				) is not None:
               _a					= flax_models
               _a					= _re_flax_models.match(SCREAMING_SNAKE_CASE_				).groups()[0]
          elif _re_pt_models.match(SCREAMING_SNAKE_CASE_				) is not None:
               _a					= pt_models
               _a					= _re_pt_models.match(SCREAMING_SNAKE_CASE_				).groups()[0]

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

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

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

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

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

     return pd.DataFrame(SCREAMING_SNAKE_CASE_				)
def 					A_      (      _lowerCAmelCase   :    List[str]				):






     """simple docstring"""

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






     """simple docstring"""

     _a					= get_frameworks_table()
     _a					= Dataset.from_pandas(SCREAMING_SNAKE_CASE_				)

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

     # Sort the model classes to avoid some nondeterministic updates to create false update commits.
     _a					= sorted(table.keys()				)
     _a					= 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],
         }				)
     _a					= Dataset.from_pandas(SCREAMING_SNAKE_CASE_				)

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

          if commit_sha is not None:
               _a					= (
                   f'Update with commit {commit_sha}\n\nSee: '
                   f'https://github.com/huggingface/transformers/commit/{commit_sha}'
               )
          else:
               _a					= 'Update'

          upload_folder(
              repo_id='''huggingface/transformers-metadata''',   folder_path=SCREAMING_SNAKE_CASE_,   repo_type='''dataset''',   token=SCREAMING_SNAKE_CASE_,   commit_message=SCREAMING_SNAKE_CASE_,   )
def 					A_      (      ):






     """simple docstring"""

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

     if len(SCREAMING_SNAKE_CASE_				) > 0:
          _a					= ', '.join(SCREAMING_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				=				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				=				parser.parse_args()

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			  = {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = [
           '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''InstructBlipQFormerModel''',
           '''InstructBlipPreTrainedModel''',
           '''InstructBlipForConditionalGeneration''',
           '''InstructBlipVisionModel''',
       ]

if TYPE_CHECKING:
       from .configuration_instructblip import (
           INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           InstructBlipConfig,
           InstructBlipQFormerConfig,
           InstructBlipVisionConfig,
       )
       from .processing_instructblip import InstructBlipProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_instructblip import (
                  INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  InstructBlipForConditionalGeneration,
                  InstructBlipPreTrainedModel,
                  InstructBlipQFormerModel,
                  InstructBlipVisionModel,
              )

else:
       import sys

       snake_case_			  = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

