


'''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 lowerCamelCase    (    ):





	"""simple docstring"""







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

	return sorted(_UpperCamelCase	)





def lowerCamelCase    (    ):





	"""simple docstring"""







	__magic_name__ :					Dict  			=  set(get_all_model_names()	)

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

	return sorted(_UpperCamelCase	)





def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	):





	"""simple docstring"""







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

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

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

		__magic_name__ :					Union[str, Any]  			=  sorted(content['tokenizer_classes']	)
		__magic_name__ :					str  			=  sorted(content['processor_classes']	)
		__magic_name__ :					Union[str, Any]  			=  sorted(content['model_classes']	)

		__magic_name__ :					Any  			=  content
		with open(os.path.join(_UpperCamelCase	,	'hub_tiny_model_summary.json'	)	,	'w'	) as fp:
			json.dump(_UpperCamelCase	,	_UpperCamelCase	,	ensure_ascii=_UpperCamelCase	,	indent=4	)


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

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

							lowerCAmelCase   :List[str]								=    "tiny_models"
							lowerCAmelCase   :Union[str, Any]								=    True
							lowerCAmelCase   :str								=    None
							lowerCAmelCase   :Union[str, Any]								=    get_tiny_model_names_from_repo()
							lowerCAmelCase   :Any								=    True
							lowerCAmelCase   :Tuple								=    True
							lowerCAmelCase   :Optional[int]								=    "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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)

lowerCAmelCase   :Dict								=    {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}
class _lowerCamelCase (		lowercase__							):



		'''simple docstring'''







		A_			:    Optional[Any]   					=       """yoso"""







		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )

			__magic_name__ :					List[Any]  			=  vocab_size
			__magic_name__ :					int  			=  max_position_embeddings
			__magic_name__ :					int  			=  hidden_size
			__magic_name__ :					Any  			=  num_hidden_layers
			__magic_name__ :					List[str]  			=  num_attention_heads
			__magic_name__ :					str  			=  intermediate_size
			__magic_name__ :					Any  			=  hidden_act
			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
			__magic_name__ :					str  			=  attention_probs_dropout_prob
			__magic_name__ :					int  			=  initializer_range
			__magic_name__ :					List[Any]  			=  type_vocab_size
			__magic_name__ :					Tuple  			=  layer_norm_eps
			__magic_name__ :					Tuple  			=  position_embedding_type
			__magic_name__ :					Optional[Any]  			=  use_expectation
			__magic_name__ :					Union[str, Any]  			=  hash_code_len
			__magic_name__ :					int  			=  num_hash
			__magic_name__ :					List[Any]  			=  conv_window
			__magic_name__ :					Optional[Any]  			=  use_fast_hash
			__magic_name__ :					int  			=  lsh_backward