


'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					["image_processor", "tokenizer"]
    a									=					"AutoImageProcessor"
    a									=					"AutoTokenizer"


    def __init__(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=None   ,							SCREAMING_SNAKE_CASE    :							str=None   ,							**SCREAMING_SNAKE_CASE    :							List[str]):
           _A  :			str											=      None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                      ' instead.'   ,							SCREAMING_SNAKE_CASE   ,							)
                  _A  :			Any											=      kwargs.pop('feature_extractor')

           _A  :			Optional[int]											=      image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError('You need to specify an `image_processor`.')
           if tokenizer is None:
                  raise ValueError('You need to specify a `tokenizer`.')

           super().__init__(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
           _A  :			Optional[Any]											=      self.image_processor
           _A  :			int											=      False


    def __call__(						self    :							Any   ,							*SCREAMING_SNAKE_CASE    :							int   ,							**SCREAMING_SNAKE_CASE    :							Optional[Any]):
           # For backward compatibility
           if self._in_target_context_manager:
                  return self.current_processor(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)

           _A  :			Optional[int]											=      kwargs.pop('images'   ,							SCREAMING_SNAKE_CASE)
           _A  :			Tuple											=      kwargs.pop('text'   ,							SCREAMING_SNAKE_CASE)
           if len(SCREAMING_SNAKE_CASE) > 0:
                  _A  :			Any											=      args[0]
                  _A  :			Any											=      args[1:]

           if images is None and text is None:
                  raise ValueError('You need to specify either an `images` or `text` input to process.')

           if images is not None:
                  _A  :			List[str]											=      self.image_processor(SCREAMING_SNAKE_CASE   ,							*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)
           if text is not None:
                  _A  :			str											=      self.tokenizer(SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)

           if text is None:
                  return inputs
           elif images is None:
                  return encodings
           else:
                  _A  :			Union[str, Any]											=      encodings['input_ids']
                  return inputs


    def      A					(						self    :							Tuple   ,							*SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							**SCREAMING_SNAKE_CASE    :							Optional[int]):
           return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[int]   ,							*SCREAMING_SNAKE_CASE    :							Tuple   ,							**SCREAMING_SNAKE_CASE    :							Optional[int]):
           return self.tokenizer.decode(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)


    @contextmanager
    def      A					(						self    :							Union[str, Any]):
           warnings.warn(
               '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
               'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
               'your images inputs, or in a separate call.')
           _A  :			int											=      True
           _A  :			str											=      self.tokenizer
           yield
           _A  :			Optional[int]											=      self.image_processor
           _A  :			List[str]											=      False


    @property
    def      A					(						self    :							Dict):
           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,							SCREAMING_SNAKE_CASE   ,							)
           return self.image_processor_class


    @property
    def      A					(						self    :							List[str]):
           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,							SCREAMING_SNAKE_CASE   ,							)
           return self.image_processor










'''simple docstring'''







import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor






def    lowerCAmelCase__ (       ):
       _A  :			Union[str, Any]											=      set()
       # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
       for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
              _A  :			Optional[Any]											=      getattr(transformers.models.auto						,lowerCamelCase						,lowerCamelCase     )
              if module is None:
                     continue
              # all mappings in a single auto modeling file
              _A  :			Any											=      [
                  x
                  for x in dir(lowerCamelCase     )
                  if x.endswith('_MAPPING_NAMES'     )
                  and (x.startswith('MODEL_'     ) or x.startswith('TF_MODEL_'     ) or x.startswith('FLAX_MODEL_'     ))
              ]
              for name in mapping_names:
                     _A  :			Tuple											=      getattr(lowerCamelCase						,lowerCamelCase     )
                     if mapping is not None:
                            for v in mapping.values():
                                   if isinstance(lowerCamelCase						,(list, tuple)     ):
                                          model_names.update(lowerCamelCase     )
                                   elif isinstance(lowerCamelCase						,lowerCamelCase     ):
                                          model_names.add(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       ):
       # All model names defined in auto mappings
       _A  :			Union[str, Any]											=      set(get_all_model_names()     )

       with open('tests/utils/tiny_model_summary.json'     ) as fp:
              _A  :			Any											=      json.load(lowerCamelCase     )
       _A  :			Union[str, Any]											=      set()
       for model_base_name in tiny_model_info:
              tiny_models_names.update(tiny_model_info[model_base_name]['model_classes']     )

       # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
       _A  :			List[Any]											=      model_names.difference(lowerCamelCase     )
       for model_name in copy.copy(lowerCamelCase     ):
              if not model_name.startswith('TF'     ) and F'TF{model_name}' in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )
              elif model_name.startswith('TF'     ) and model_name[2:] in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]     ):
       _A  :			Optional[int]											=      COMPOSITE_MODELS.values()

       # All tiny model base names on Hub
       _A  :			Optional[int]											=      get_all_model_names()
       _A  :			Tuple											=      hf_api.list_models(
           filter=ModelFilter(
               author='hf-internal-testing'						,)     )
       _A  :			Any											=      set()
       for x in models:
              _A  :			List[Any]											=      x.modelId
              _A						,				_A  :			Any											=      model.split('/'     )
              if not model.startswith('tiny-random-'     ):
                     continue
              _A  :			Dict											=      model.replace('tiny-random-'						,''     )
              if not model[0].isupper():
                     continue
              if model not in model_names and model not in special_models:
                     continue
              _models.add(lowerCamelCase     )

       _A  :			Dict											=      sorted(_models     )
       # All tiny model names on Hub
       _A  :			List[Any]											=      {}
       for model in models:
              _A  :			Optional[Any]											=      F'hf-internal-testing/tiny-random-{model}'
              _A  :			Optional[Any]											=      model.split('-'     )[0]
              try:
                     _A  :			Any											=      hf_api.repo_info(lowerCamelCase     )
                     _A  :			Any											=      {
                         'tokenizer_classes': set(),
                         'processor_classes': set(),
                         'model_classes': set(),
                         'sha': repo_info.sha,
                     }
              except Exception:
                     continue
              try:
                     time.sleep(1     )
                     _A  :			Optional[Any]											=      AutoTokenizer.from_pretrained(lowerCamelCase     )
                     content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			str											=      AutoTokenizer.from_pretrained(lowerCamelCase						,use_fast=lowerCamelCase     )
                     content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Dict											=      AutoImageProcessor.from_pretrained(lowerCamelCase     )
                     content["processor_classes"].add(img_p.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Tuple											=      AutoFeatureExtractor.from_pretrained(lowerCamelCase     )
                     if not isinstance(lowerCamelCase						,lowerCamelCase     ):
                            content["processor_classes"].add(feat_p.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Optional[Any]											=      getattr(lowerCamelCase						,lowerCamelCase     )
                     _A  :			List[Any]											=      model_class.from_pretrained(lowerCamelCase     )
                     content["model_classes"].add(m.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Tuple											=      getattr(lowerCamelCase						,F'TF{model}'     )
                     _A  :			Optional[int]											=      model_class.from_pretrained(lowerCamelCase     )
                     content["model_classes"].add(m.__class__.__name__     )
              except Exception:
                     pass

              _A  :			Any											=      sorted(content['tokenizer_classes']     )
              _A  :			Tuple											=      sorted(content['processor_classes']     )
              _A  :			List[str]											=      sorted(content['model_classes']     )

              _A  :			int											=      content
              with open(os.path.join(lowerCamelCase						,'hub_tiny_model_summary.json'     )						,'w'     ) as fp:
                     json.dump(lowerCamelCase						,lowerCamelCase						,ensure_ascii=lowerCamelCase						,indent=4     )


if __name__ == "__main__":
   A   :  Dict       		=				argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A   :  Optional[int]       		=				parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   A   :  Any       		=				'''tiny_models'''
   A   :  Union[str, Any]       		=				True
   A   :  List[Any]       		=				None
   A   :  Any       		=				get_tiny_model_names_from_repo()
   A   :  List[str]       		=				True
   A   :  Union[str, Any]       		=				True
   A   :  Tuple       		=				'''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )






