


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"""






		lowerCAmelCase__     :    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__     :    Optional[int]					  =      getattr(transformers.models.auto			,     _a			,     _a )
				if module is None:
						continue
				# all mappings in a single auto modeling file
				lowerCAmelCase__     :    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:
						lowerCAmelCase__     :    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   lowerCamelCase_					( ):




		"""simple docstring"""






		lowerCAmelCase__     :    Dict					  =      set(get_all_model_names() )

		with open('''tests/utils/tiny_model_summary.json''' ) as fp:
				lowerCAmelCase__     :    Union[str, Any]					  =      json.load(_a )
		lowerCAmelCase__     :    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.
		lowerCAmelCase__     :    List[Any]					  =      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   lowerCamelCase_					( _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Any					  =      COMPOSITE_MODELS.values()

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

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

				lowerCAmelCase__     :    List[str]					  =      sorted(content['''tokenizer_classes'''] )
				lowerCAmelCase__     :    Optional[int]					  =      sorted(content['''processor_classes'''] )
				lowerCAmelCase__     :    int					  =      sorted(content['''model_classes'''] )

				lowerCAmelCase__     :    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__":
		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,
		)





from typing import TYPE_CHECKING

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


lowerCamelCase		=	{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	['''SegformerFeatureExtractor''']
		lowerCamelCase		=	['''SegformerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SegformerDecodeHead''',
		    '''SegformerForImageClassification''',
		    '''SegformerForSemanticSegmentation''',
		    '''SegformerLayer''',
		    '''SegformerModel''',
		    '''SegformerPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFSegformerDecodeHead''',
		    '''TFSegformerForImageClassification''',
		    '''TFSegformerForSemanticSegmentation''',
		    '''TFSegformerModel''',
		    '''TFSegformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_segformer import SegformerFeatureExtractor
				from .image_processing_segformer import SegformerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_segformer import (
				    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SegformerDecodeHead,
				    SegformerForImageClassification,
				    SegformerForSemanticSegmentation,
				    SegformerLayer,
				    SegformerModel,
				    SegformerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_segformer import (
				    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSegformerDecodeHead,
				    TFSegformerForImageClassification,
				    TFSegformerForSemanticSegmentation,
				    TFSegformerModel,
				    TFSegformerPreTrainedModel,
				)

else:
		import sys

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




