



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    (					):



 """simple docstring"""





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

 return sorted(__lowerCamelCase  )






def 						_lowerCAmelCase    (					):



 """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(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     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							:     Optional[int]							=						model_names.difference(__lowerCamelCase  )
 for model_name in copy.copy(__lowerCamelCase  ):
  if not model_name.startswith("TF"  ) and F"""TF{model_name}""" in not_on_hub:
   tiny_models_names.remove(__lowerCamelCase  )
  elif model_name.startswith("TF"  ) and model_name[2:] in not_on_hub:
   tiny_models_names.remove(__lowerCamelCase  )

 return sorted(__lowerCamelCase  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						COMPOSITE_MODELS.values()

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

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						sorted(_models  )
 # All tiny model names on Hub
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						{}
 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							:     List[str]							=						hf_api.repo_info(__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE							:     Tuple							=						{
       "tokenizer_classes": set(),
       "processor_classes": set(),
       "model_classes": set(),
       "sha": repo_info.sha,
   }
  except Exception:
   continue
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Tuple							=						AutoTokenizer.from_pretrained(__lowerCamelCase  )
   content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Any							=						AutoTokenizer.from_pretrained(__lowerCamelCase		,						use_fast=__lowerCamelCase  )
   content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     List[str]							=						AutoImageProcessor.from_pretrained(__lowerCamelCase  )
   content["processor_classes"].add(img_p.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						AutoFeatureExtractor.from_pretrained(__lowerCamelCase  )
   if not isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
    content["processor_classes"].add(feat_p.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						getattr(__lowerCamelCase		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE							:     int							=						model_class.from_pretrained(__lowerCamelCase  )
   content["model_classes"].add(m.__class__.__name__  )
  except Exception:
   pass
  try:
   time.sleep(1  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						getattr(__lowerCamelCase		,						F"""TF{model}"""  )
   __SCREAMING_SNAKE_CASE							:     Tuple							=						model_class.from_pretrained(__lowerCamelCase  )
   content["model_classes"].add(m.__class__.__name__  )
  except Exception:
   pass

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

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


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

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

      _lowerCamelCase         =	"""tiny_models"""
      _lowerCamelCase         =	True
      _lowerCamelCase         =	None
      _lowerCamelCase         =	get_tiny_model_names_from_repo()
      _lowerCamelCase         =	True
      _lowerCamelCase         =	True
      _lowerCamelCase         =	"""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_tf_available, is_torch_available


__A	:       Dict        =						{'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       Any        =						[
	    'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'CvtForImageClassification',
	    'CvtModel',
	    'CvtPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       List[str]        =						[
	    'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFCvtForImageClassification',
	    'TFCvtModel',
	    'TFCvtPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cvt import (
		    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CvtForImageClassification,
		    CvtModel,
		    CvtPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_cvt import (
		    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCvtForImageClassification,
		    TFCvtModel,
		    TFCvtPreTrainedModel,
		)


else:
	import sys

	__A	:       str        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





