


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


      '''simple docstring'''



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

      return sorted(__A       )







def _UpperCamelCase			(	)				->						int:


      '''simple docstring'''



      UpperCamelCase__      =							set(get_all_model_names()       )

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

      return sorted(__A       )







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							COMPOSITE_MODELS.values()

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

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

            UpperCamelCase__      =							sorted(content["tokenizer_classes"]       )
            UpperCamelCase__      =							sorted(content["processor_classes"]       )
            UpperCamelCase__      =							sorted(content["model_classes"]       )

            UpperCamelCase__      =							content
            with open(os.path.join(__A ,     "hub_tiny_model_summary.json"       ) ,     "w"       ) as fp:
                  json.dump(__A ,     __A ,     ensure_ascii=__A ,     indent=4       )


if __name__ == "__main__":
  a__    :   Any								=			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__    :   str								=			"""tiny_models"""
  a__    :   List[Any]								=			True
  a__    :   Optional[int]								=			None
  a__    :   List[Any]								=			get_tiny_model_names_from_repo()
  a__    :   List[Any]								=			True
  a__    :   Dict								=			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,
  )









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






				'''simple docstring'''

				__lowerCamelCase		:   Optional[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"]:
								__lowerCamelCase		:   Dict           =       getattr(transformers.models.auto				,    lowercase				,    lowercase     )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								__lowerCamelCase		:   Any           =       [
								    x
								    for x in dir(lowercase     )
								    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		:   Union[str, Any]           =       getattr(lowercase				,    lowercase     )
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(lowercase				,    (list, tuple)     ):
																								model_names.update(lowercase     )
																				elif isinstance(lowercase				,    lowercase     ):
																								model_names.add(lowercase     )

				return sorted(lowercase     )





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Tuple           =       set(get_all_model_names()     )

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

				return sorted(lowercase     )





def 				a_							(    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[int]           =       COMPOSITE_MODELS.values()

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

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

								__lowerCamelCase		:   List[Any]           =       sorted(content["tokenizer_classes"]     )
								__lowerCamelCase		:   str           =       sorted(content["processor_classes"]     )
								__lowerCamelCase		:   Union[str, Any]           =       sorted(content["model_classes"]     )

								__lowerCamelCase		:   Tuple           =       content
								with open(os.path.join(lowercase				,    "hub_tiny_model_summary.json"     )				,    "w"     ) as fp:
												json.dump(lowercase				,    lowercase				,    ensure_ascii=lowercase				,    indent=4     )


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

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

				__lowercase	:   str   		=					"""tiny_models"""
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Optional[int]   		=					None
				__lowercase	:   List[Any]   		=					get_tiny_model_names_from_repo()
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Dict   		=					True
				__lowercase	:   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,
				)





