




'''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 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					int							    =			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"]:
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			getattr(transformers.models.auto						,  snake_case						,  snake_case )
      if module is None:
         continue
      # all mappings in a single auto modeling file
      SCREAMING_SNAKE_CASE__      :					Any							    =			[
          x
          for x in dir(snake_case )
          if x.endswith('''_MAPPING_NAMES''' )
          and (x.startswith('''MODEL_''' ) or x.startswith('''TF_MODEL_''' ) or x.startswith('''FLAX_MODEL_''' ))
      ]
      for name in mapping_names:
         SCREAMING_SNAKE_CASE__      :					int							    =			getattr(snake_case						,  snake_case )
         if mapping is not None:
            for v in mapping.values():
               if isinstance(snake_case						,  (list, tuple) ):
                  model_names.update(snake_case )
               elif isinstance(snake_case						,  snake_case ):
                  model_names.add(snake_case )

   return sorted(snake_case )


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			set(get_all_model_names() )

   with open('''tests/utils/tiny_model_summary.json''' ) as fp:
      SCREAMING_SNAKE_CASE__      :					Dict							    =			json.load(snake_case )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			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.
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			model_names.difference(snake_case )
   for model_name in copy.copy(snake_case ):
      if not model_name.startswith('''TF''' ) and f"""TF{model_name}""" in not_on_hub:
         tiny_models_names.remove(snake_case )
      elif model_name.startswith('''TF''' ) and model_name[2:] in not_on_hub:
         tiny_models_names.remove(snake_case )

   return sorted(snake_case )


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Tuple							    =			COMPOSITE_MODELS.values()

   # All tiny model base names on Hub
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			get_all_model_names()
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			hf_api.list_models(
       filter=ModelFilter(
           author='''hf-internal-testing'''						,  ) )
   SCREAMING_SNAKE_CASE__      :					Any							    =			set()
   for x in models:
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			x.modelId
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			model.split('''/''' )
      if not model.startswith('''tiny-random-''' ):
         continue
      SCREAMING_SNAKE_CASE__      :					Any							    =			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(snake_case )

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

      SCREAMING_SNAKE_CASE__      :					Dict							    =			sorted(content['''tokenizer_classes'''] )
      SCREAMING_SNAKE_CASE__      :					Any							    =			sorted(content['''processor_classes'''] )
      SCREAMING_SNAKE_CASE__      :					str							    =			sorted(content['''model_classes'''] )

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


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

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

       lowercase__								=						'''tiny_models'''
       lowercase__								=						True
       lowercase__								=						None
       lowercase__								=						get_tiny_model_names_from_repo()
       lowercase__								=						True
       lowercase__								=						True
       lowercase__								=						'''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,
       )







'''simple docstring'''





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''EfficientNetImageProcessor'''
      __lowercase					=	('''BertTokenizer''', '''BertTokenizerFast''')






      def __init__(self			,	a__			,	a__       )							-> Tuple:
         super().__init__(a__			,	a__       )






      def __call__(self			,	a__=None			,	a__=None			,	a__="max_length"			,	a__=6_4			,	a__=None			,	**a__       )							-> Any:
         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

         if text is not None:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.tokenizer(
                a__			,	padding=a__			,	max_length=a__			,	return_tensors=a__			,	**a__       )

         if images is not None:
            SCREAMING_SNAKE_CASE__      :					Any							    =			self.image_processor(a__			,	return_tensors=a__			,	**a__       )

         if text is not None and images is not None:
            SCREAMING_SNAKE_CASE__      :					str							    =			image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__       )			,	tensor_type=a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.tokenizer.model_input_names
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

