






"""simple docstring"""



from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__A		:	List[Any]      =		TypeVar('''T''')
__A		:	Any      =		TypeVar('''U''')





class  _UpperCAmelCase	(			Generic[T, U]							):




					def __init__(    self					:						int   ,						A					:						T | None   ,						A					:						U | None       )  ->  Optional[int]:
							lowercase_			:			int								=       key
							lowercase_			:			Any								=       val
							lowercase_			:			int								=       0
							lowercase_			:			DoubleLinkedListNode[T, U] | None								=       None
							lowercase_			:			DoubleLinkedListNode[T, U] | None								=       None






					def __repr__(    self					:						Optional[int]       )  ->  str:
							return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
							    self.key   ,						self.val   ,						self.freq   ,						self.next is not None   ,						self.prev is not None       )





class  _UpperCAmelCase	(			Generic[T, U]							):




					def __init__(    self					:						List[str]       )  ->  None:
							lowercase_			:			DoubleLinkedListNode[T, U]								=       DoubleLinkedListNode(A   ,						A       )
							lowercase_			:			DoubleLinkedListNode[T, U]								=       DoubleLinkedListNode(A   ,						A       )
							lowercase_	, lowercase_			:			List[Any]								=       self.rear, self.head




					def __repr__(    self					:						Tuple       )  ->  str:
							lowercase_			:			Any								=       ['''DoubleLinkedList''']
							lowercase_			:			str								=       self.head
							while node.next is not None:
									rep.append(str(A       )       )
									lowercase_			:			int								=       node.next
							rep.append(str(self.rear       )       )
							return ",\n    ".join(A       )




					def   A  (    self					:						int   ,						A					:						DoubleLinkedListNode[T, U]       )  ->  None:
							lowercase_			:			Optional[int]								=       self.rear.prev

							# All nodes other than self.head are guaranteed to have non-None previous
							assert previous is not None

							lowercase_			:			int								=       node
							lowercase_			:			List[Any]								=       previous
							lowercase_			:			List[Any]								=       node
							lowercase_			:			Dict								=       self.rear
							node.freq += 1
							self._position_node(A       )




					def   A  (    self					:						Optional[Any]   ,						A					:						DoubleLinkedListNode[T, U]       )  ->  None:

							while node.prev is not None and node.prev.freq > node.freq:
									# swap node with previous node
									lowercase_			:			Tuple								=       node.prev

									lowercase_			:			Optional[Any]								=       previous_node.prev
									lowercase_			:			Any								=       node.prev
									lowercase_			:			Tuple								=       previous_node
									lowercase_			:			Optional[Any]								=       node






					def   A  (    self					:						List[str]   ,						A					:						DoubleLinkedListNode[T, U]       )  ->  DoubleLinkedListNode[T, U] | None:

							if node.prev is None or node.next is None:
									return None

							lowercase_			:			Union[str, Any]								=       node.next
							lowercase_			:			Union[str, Any]								=       node.prev
							lowercase_			:			List[Any]								=       None
							lowercase_			:			List[str]								=       None
							return node





class  _UpperCAmelCase	(			Generic[T, U]							):
					SCREAMING_SNAKE_CASE_		:    dict[Callable[[T], U], LFUCache[T, U]]							   =       {}




					def __init__(    self					:						Optional[int]   ,						A					:						int       )  ->  List[str]:
							lowercase_			:			DoubleLinkedList[T, U]								=       DoubleLinkedList()
							lowercase_			:			List[str]								=       capacity
							lowercase_			:			str								=       0
							lowercase_			:			Dict								=       0
							lowercase_			:			Optional[int]								=       0
							lowercase_			:			dict[T, DoubleLinkedListNode[T, U]]								=       {}




					def __repr__(    self					:						Optional[Any]       )  ->  str:
							return (
							    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
							    F'''capacity={self.capacity}, current_size={self.num_keys})'''
							)




					def __contains__(    self					:						Any   ,						A					:						T       )  ->  bool:
							return key in self.cache




					def   A  (    self					:						List[Any]   ,						A					:						T       )  ->  U | None:

							if key in self.cache:
									self.hits += 1
									lowercase_			:			DoubleLinkedListNode[T, U]								=       self.cache[key]
									lowercase_			:			Dict								=       self.list.remove(self.cache[key]       )
									assert node == value_node

									# node is guaranteed not None because it is in self.cache
									assert node is not None
									self.list.add(A       )
									return node.val
							self.miss += 1
							return None




					def   A  (    self					:						Any   ,						A					:						T   ,						A					:						U       )  ->  None:

							if key not in self.cache:
									if self.num_keys >= self.capacity:
											# delete first node when over capacity
											lowercase_			:			Union[str, Any]								=       self.list.head.next

											# guaranteed to have a non-None first node when num_keys > 0
											# explain to type checker via assertions
											assert first_node is not None
											assert first_node.key is not None
											assert self.list.remove(A       ) is not None
											# first_node guaranteed to be in list

											del self.cache[first_node.key]
											self.num_keys -= 1
									lowercase_			:			List[str]								=       DoubleLinkedListNode(A   ,						A       )
									self.list.add(self.cache[key]       )
									self.num_keys += 1

							else:
									lowercase_			:			int								=       self.list.remove(self.cache[key]       )
									assert node is not None  # node guaranteed to be in list
									lowercase_			:			Optional[int]								=       value
									self.list.add(A       )






					@classmethod
					def   A  (    cls					:						type[LFUCache[T, U]]   ,						A					:						int = 1_28       )  ->  Callable[[Callable[[T], U]], Callable[..., U]]:

							def cache_decorator_inner(A					:						Callable[[T], U]       ) -> Callable[..., U]:
									def cache_decorator_wrapper(*A					:						T       ) -> U:
											if func not in cls.decorator_function_to_instance_map:
													lowercase_			:			List[str]								=       LFUCache(A       )

											lowercase_			:			Union[str, Any]								=       cls.decorator_function_to_instance_map[func].get(args[0]       )
											if result is None:
													lowercase_			:			List[str]								=       func(*A       )
													cls.decorator_function_to_instance_map[func].put(args[0]   ,						A       )
											return result

									def cache_info() -> LFUCache[T, U]:
											return cls.decorator_function_to_instance_map[func]

									setattr(A   ,						'''cache_info'''   ,						A       )  # noqa: B010

									return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
						import doctest

						doctest.testmod()











"""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 						lowercase   (    ):
		lowercase_			:			Tuple								=       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"]:
				lowercase_			:			str								=       getattr(transformers.models.auto		, __snake_case		, __snake_case     )
				if module is None:
						continue
				# all mappings in a single auto modeling file
				lowercase_			:			Tuple								=       [
				    x
				    for x in dir(__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:
						lowercase_			:			Tuple								=       getattr(__snake_case		, __snake_case     )
						if mapping is not None:
								for v in mapping.values():
										if isinstance(__snake_case		, (list, tuple)     ):
												model_names.update(__snake_case     )
										elif isinstance(__snake_case		, __snake_case     ):
												model_names.add(__snake_case     )

		return sorted(__snake_case     )



def 						lowercase   (    ):
		# All model names defined in auto mappings
		lowercase_			:			Tuple								=       set(get_all_model_names()     )

		with open('''tests/utils/tiny_model_summary.json'''     ) as fp:
				lowercase_			:			Tuple								=       json.load(__snake_case     )
		lowercase_			:			Union[str, 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.
		lowercase_			:			Union[str, Any]								=       model_names.difference(__snake_case     )
		for model_name in copy.copy(__snake_case     ):
				if not model_name.startswith('''TF'''     ) and F'''TF{model_name}''' in not_on_hub:
						tiny_models_names.remove(__snake_case     )
				elif model_name.startswith('''TF'''     ) and model_name[2:] in not_on_hub:
						tiny_models_names.remove(__snake_case     )

		return sorted(__snake_case     )



def 						lowercase   (    __snake_case    :			List[Any]     ):
		lowercase_			:			List[str]								=       COMPOSITE_MODELS.values()

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

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

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

				lowercase_			:			str								=       content
				with open(os.path.join(__snake_case		, '''hub_tiny_model_summary.json'''     )		, '''w'''     ) as fp:
						json.dump(__snake_case		, __snake_case		, ensure_ascii=__snake_case		, indent=4     )


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

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

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



