




'''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_			(  )					->			List[Any]:


      '''simple docstring'''




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

      return sorted(_UpperCamelCase						)

def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				set(get_all_model_names()						)

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

      return sorted(_UpperCamelCase						)

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]						)					->			Tuple:


      '''simple docstring'''




      lowerCamelCase__        =				COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      lowerCamelCase__        =				get_all_model_names()
      lowerCamelCase__        =				hf_api.list_models(
          filter=ModelFilter(
              author='hf-internal-testing'     ,				)						)
      lowerCamelCase__        =				set()
      for x in models:
            lowerCamelCase__        =				x.modelId
            lowerCamelCase__  ,		lowerCamelCase__        =				model.split('/'						)
            if not model.startswith('tiny-random-'						):
                  continue
            lowerCamelCase__        =				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(_UpperCamelCase						)

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

            lowerCamelCase__        =				sorted(content['tokenizer_classes']						)
            lowerCamelCase__        =				sorted(content['processor_classes']						)
            lowerCamelCase__        =				sorted(content['model_classes']						)

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


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

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

      __SCREAMING_SNAKE_CASE    :   List[str]       ="tiny_models"
      __SCREAMING_SNAKE_CASE    :   Union[str, Any]       =True
      __SCREAMING_SNAKE_CASE    :   str       =None
      __SCREAMING_SNAKE_CASE    :   Union[str, Any]       =get_tiny_model_names_from_repo()
      __SCREAMING_SNAKE_CASE    :   Any       =True
      __SCREAMING_SNAKE_CASE    :   Tuple       =True
      __SCREAMING_SNAKE_CASE    :   Optional[int]       ="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,
      )





from ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)