




'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_	:     Any							     =	logging.get_logger(__name__)

UpperCAmelCase_	:     List[Any]							     =	{
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}





class 					lowercase__						(		A__      ):





							'''simple docstring'''


							A_							:   Tuple   			=						MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							A_							:   str   			=						"""mobilebert"""






							def __init__(					self					,      __snake_case=3_0522					,      __snake_case=512					,      __snake_case=24					,      __snake_case=4					,      __snake_case=512					,      __snake_case="relu"					,      __snake_case=0.0					,      __snake_case=0.1					,      __snake_case=512					,      __snake_case=2					,      __snake_case=0.02					,      __snake_case=1e-12					,      __snake_case=0					,      __snake_case=128					,      __snake_case=True					,      __snake_case=True					,      __snake_case=128					,      __snake_case=False					,      __snake_case=True					,      __snake_case=4					,      __snake_case="no_norm"					,      __snake_case=True					,      __snake_case=None					,      **__snake_case					,      ):
											super().__init__(pad_token_id=_lowerCAmelCase					,      **_lowerCAmelCase						)

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   vocab_size
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   hidden_size
											_SCREAMING_SNAKE_CASE				: int  	=   num_hidden_layers
											_SCREAMING_SNAKE_CASE				: int  	=   num_attention_heads
											_SCREAMING_SNAKE_CASE				: int  	=   hidden_act
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   intermediate_size
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   hidden_dropout_prob
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE				: int  	=   max_position_embeddings
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   type_vocab_size
											_SCREAMING_SNAKE_CASE				: List[str]  	=   initializer_range
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   layer_norm_eps
											_SCREAMING_SNAKE_CASE				: Tuple  	=   embedding_size
											_SCREAMING_SNAKE_CASE				: str  	=   trigram_input
											_SCREAMING_SNAKE_CASE				: Tuple  	=   use_bottleneck
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   intra_bottleneck_size
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   use_bottleneck_attention
											_SCREAMING_SNAKE_CASE				: Any  	=   key_query_shared_bottleneck
											_SCREAMING_SNAKE_CASE				: str  	=   num_feedforward_networks
											_SCREAMING_SNAKE_CASE				: Tuple  	=   normalization_type
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   classifier_activation

											if self.use_bottleneck:
															_SCREAMING_SNAKE_CASE				: int  	=   intra_bottleneck_size
											else:
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   hidden_size

											_SCREAMING_SNAKE_CASE				: Any  	=   classifier_dropout





class 					lowercase__						(		A__      ):





							'''simple docstring'''


							@property
							def UpperCAmelCase_			(					self						):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE				: List[str]  	=   {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE				: Dict  	=   {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											        ("""token_type_ids""", dynamic_axis),
											    ]						)








'''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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: 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"]:
								_SCREAMING_SNAKE_CASE				: Any  	=   getattr(transformers.models.auto							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								_SCREAMING_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:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set(get_all_model_names()  )

				with open("""tests/utils/tiny_model_summary.json"""  ) as fp:
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   json.load(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: 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.
				_SCREAMING_SNAKE_CASE				: 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     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   COMPOSITE_MODELS.values()

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

				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sorted(_models  )
				# All tiny model names on Hub
				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				for model in models:
								_SCREAMING_SNAKE_CASE				: str  	=   f"""hf-internal-testing/tiny-random-{model}"""
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.split("""-"""  )[0]
								try:
												_SCREAMING_SNAKE_CASE				: Tuple  	=   hf_api.repo_info(SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {
												    """tokenizer_classes""": set(),
												    """processor_classes""": set(),
												    """model_classes""": set(),
												    """sha""": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   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  )
												_SCREAMING_SNAKE_CASE				: str  	=   AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["processor_classes"].add(img_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Any  	=   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  )
												_SCREAMING_SNAKE_CASE				: int  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Tuple  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   getattr(SCREAMING_SNAKE_CASE__							,   f"""TF{model}"""  )
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass

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

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

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

				UpperCAmelCase_	:     Any							     =	'tiny_models'
				UpperCAmelCase_	:     Tuple							     =	True
				UpperCAmelCase_	:     Tuple							     =	None
				UpperCAmelCase_	:     Optional[int]							     =	get_tiny_model_names_from_repo()
				UpperCAmelCase_	:     Dict							     =	True
				UpperCAmelCase_	:     str							     =	True
				UpperCAmelCase_	:     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,
				)




