


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			:	List[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"]:
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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			:	List[Any]							=				[
								    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			:	Dict							=				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[int]							=				set(get_all_model_names()					)

				with open("""tests/utils/tiny_model_summary.json"""					) as fp:
								_SCREAMING_SNAKE_CASE			:	Any							=				json.load(SCREAMING_SNAKE_CASE__					)
				_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			:	Tuple							=				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			:	Any							=				COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				_SCREAMING_SNAKE_CASE			:	int							=				get_all_model_names()
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				hf_api.list_models(
				    filter=ModelFilter(
				        author="""hf-internal-testing"""							,				)					)
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				set()
				for x in models:
								_SCREAMING_SNAKE_CASE			:	str							=				x.modelId
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				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(SCREAMING_SNAKE_CASE__					)

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

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

								_SCREAMING_SNAKE_CASE			:	Tuple							=				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_		:       str							       =		argparse.ArgumentParser()
				parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
				UpperCAmelCase_		:       int							       =		parser.parse_args()

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

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








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if len(str(SCREAMING_SNAKE_CASE__					)					) == 1 or len(str(SCREAMING_SNAKE_CASE__					)					) == 1:
								return a * b

				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				max(len(str(SCREAMING_SNAKE_CASE__					)					)							,				len(str(SCREAMING_SNAKE_CASE__					)					)					)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				ma // 2

				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba((aa + aa)							,				(ba + ba)					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)







def       snake_case_       (  ):







				"""simple docstring"""





				print(karatsuba(1_5463							,				2_3489					)					)


if __name__ == "__main__":
				main()




