



"""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    __SCREAMING_SNAKE_CASE		( ):
      """simple docstring"""






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

      return sorted(SCREAMING_SNAKE_CASE__   )

def    __SCREAMING_SNAKE_CASE		( ):
      """simple docstring"""






      snake_case      : str							=   set(get_all_model_names()   )

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

      return sorted(SCREAMING_SNAKE_CASE__   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Union[str, Any]   ):
      """simple docstring"""






      snake_case      : Union[str, Any]							=   COMPOSITE_MODELS.values()

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

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

            snake_case      : str							=   sorted(content["tokenizer_classes"]   )
            snake_case      : Tuple							=   sorted(content["processor_classes"]   )
            snake_case      : Tuple							=   sorted(content["model_classes"]   )

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


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

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

   A    =	'tiny_models'
   A    =	True
   A    =	None
   A    =	get_tiny_model_names_from_repo()
   A    =	True
   A    =	True
   A    =	'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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A    =	{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'GPTJForCausalLM',
			    'GPTJForQuestionAnswering',
			    'GPTJForSequenceClassification',
			    'GPTJModel',
			    'GPTJPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TFGPTJForCausalLM',
			    'TFGPTJForQuestionAnswering',
			    'TFGPTJForSequenceClassification',
			    'TFGPTJModel',
			    'TFGPTJPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'FlaxGPTJForCausalLM',
			    'FlaxGPTJModel',
			    'FlaxGPTJPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gptj import (
						    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTJForCausalLM,
						    GPTJForQuestionAnswering,
						    GPTJForSequenceClassification,
						    GPTJModel,
						    GPTJPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    TFGPTJPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
			import sys

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



