


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(				):
				_SCREAMING_SNAKE_CASE     					=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     					=getattr(transformers.models.auto		,_A		,_A)
								if module is None:
												continue
								# all mappings in a single auto modeling file
								_SCREAMING_SNAKE_CASE     					=[
								    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:
												_SCREAMING_SNAKE_CASE     					=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(				):
				_SCREAMING_SNAKE_CASE     					=set(get_all_model_names())

				with open('''tests/utils/tiny_model_summary.json''') as fp:
								_SCREAMING_SNAKE_CASE     					=json.load(_A)
				_SCREAMING_SNAKE_CASE     					=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     					=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__):
				_SCREAMING_SNAKE_CASE     					=COMPOSITE_MODELS.values()

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

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

								_SCREAMING_SNAKE_CASE     					=sorted(content['''tokenizer_classes'''])
								_SCREAMING_SNAKE_CASE     					=sorted(content['''processor_classes'''])
								_SCREAMING_SNAKE_CASE     					=sorted(content['''model_classes'''])

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

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

							snake_case_  :     Tuple							  =							"tiny_models"
							snake_case_  :     Any							  =							True
							snake_case_  :     Optional[int]							  =							None
							snake_case_  :     str							  =							get_tiny_model_names_from_repo()
							snake_case_  :     Tuple							  =							True
							snake_case_  :     Dict							  =							True
							snake_case_  :     List[Any]							  =							"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 __future__ import annotations

from collections.abc import Iterator
from typing import Any

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=data
										_SCREAMING_SNAKE_CASE     					=None




						def __str__(    self   : List[Any]					)							->       str:


										"""simple docstring"""





										return f"{self.data}"

class   A__							:




						def __init__(    self   : Optional[Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None




						def __iter__(    self   : List[str]					)							->       Iterator[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.front
										while node:
														yield node.data
														_SCREAMING_SNAKE_CASE     					=node.next




						def __len__(    self   : List[Any]					)							->       int:


										"""simple docstring"""





										return len(tuple(iter(self					)					)					)




						def __str__(    self   : int					)							->       str:


										"""simple docstring"""





										return " <- ".join(str(_a					) for item in self					)




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       bool:


										"""simple docstring"""





										return len(self					) == 0




						def 							__UpperCamelCase			(    self   : int			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=Node(_a					)
										if self.is_empty():
														_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=node
										else:
														assert isinstance(self.rear			,      _a					)
														_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=node




						def 							__UpperCamelCase			(    self   : Dict					)							->       Any:


										"""simple docstring"""





										if self.is_empty():
														raise IndexError('''dequeue from empty queue'''					)
										assert isinstance(self.front			,      _a					)
										_SCREAMING_SNAKE_CASE     					=self.front
										_SCREAMING_SNAKE_CASE     					=self.front.next
										if self.front is None:
														_SCREAMING_SNAKE_CASE     					=None
										return node.data




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=None


if __name__ == "__main__":
							from doctest import testmod

							testmod()