


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

						return sorted(lowercase__				)


def 	_UpperCamelCase							(				):
						# All model names defined in auto mappings
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		set(get_all_model_names()				)

						with open('''tests/utils/tiny_model_summary.json'''				) as fp:
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		json.load(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		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(lowercase__				)
						for model_name in copy.copy(lowercase__				):
												if not model_name.startswith('''TF'''				) and F'''TF{model_name}''' in not_on_hub:
																		tiny_models_names.remove(lowercase__				)
												elif model_name.startswith('''TF'''				) and model_name[2:] in not_on_hub:
																		tiny_models_names.remove(lowercase__				)

						return sorted(lowercase__				)


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Dict		      =		COMPOSITE_MODELS.values()

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

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

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

												__SCREAMING_SNAKE_CASE						:					int		      =		content
												with open(os.path.join(lowercase__   ,	'''hub_tiny_model_summary.json'''				)   ,	'''w'''				) as fp:
																		json.dump(lowercase__   ,	lowercase__   ,	ensure_ascii=lowercase__   ,	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     :			str      =parser.parse_args()

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

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




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		(
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						__SCREAMING_SNAKE_CASE						:					list		      =		[]
						for char_count in range(lowercase__				):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]				)
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]				)

						return output_result_list


if __name__ == "__main__":
						print(alternative_list_arrange(['A', 'B', 'C'], [1, 2, 3, 4, 5]), end=' ')
