



'''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     (    )   ->       Optional[int]:



   """simple docstring"""






   A_  :		str						=					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_  :		List[Any]						=					getattr(transformers.models.auto      ,		a_      ,		a_		)
      if module is None:
         continue
      # all mappings in a single auto modeling file
      A_  :		Union[str, Any]						=					[
          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:
         A_  :		Union[str, Any]						=					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     (    )   ->       Tuple:



   """simple docstring"""






   A_  :		List[str]						=					set(get_all_model_names()		)

   with open("""tests/utils/tiny_model_summary.json"""		) as fp:
      A_  :		Tuple						=					json.load(a_		)
   A_  :		str						=					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[str]						=					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_		)   ->       Dict:



   """simple docstring"""






   A_  :		Any						=					COMPOSITE_MODELS.values()

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

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

      A_  :		Any						=					sorted(content["""tokenizer_classes"""]		)
      A_  :		Optional[Any]						=					sorted(content["""processor_classes"""]		)
      A_  :		Optional[int]						=					sorted(content["""model_classes"""]		)

      A_  :		Union[str, Any]						=					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__":
  UpperCamelCase__    :     List[Any]			  =   argparse.ArgumentParser()
  parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
  UpperCamelCase__    :     str			  =   parser.parse_args()

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

  UpperCamelCase__    :     int			  =   """tiny_models"""
  UpperCamelCase__    :     Optional[Any]			  =   True
  UpperCamelCase__    :     List[str]			  =   None
  UpperCamelCase__    :     List[Any]			  =   get_tiny_model_names_from_repo()
  UpperCamelCase__    :     Union[str, Any]			  =   True
  UpperCamelCase__    :     Dict			  =   True
  UpperCamelCase__    :     List[str]			  =   """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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """MobileViTV2ForImageClassification""",
				    """MobileViTV2ForSemanticSegmentation""",
				    """MobileViTV2Model""",
				    """MobileViTV2PreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase__ :			List[Any]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


