
'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
	from transformers import (
	    AutoConfig,
	    BertConfig,
	    GPTaConfig,
	    TaConfig,
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
	from transformers import (
	    AutoModel,
	    AutoModelForCausalLM,
	    AutoModelForMaskedLM,
	    AutoModelForPreTraining,
	    AutoModelForQuestionAnswering,
	    AutoModelForSeqaSeqLM,
	    AutoModelForSequenceClassification,
	    AutoModelWithLMHead,
	    BertForMaskedLM,
	    BertForPreTraining,
	    BertForQuestionAnswering,
	    BertForSequenceClassification,
	    BertModel,
	    GPTaLMHeadModel,
	    RobertaForMaskedLM,
	    TaForConditionalGeneration,
	)

@is_pt_tf_cross_test
class       UpperCAmelCase					(       unittest.TestCase			):





		"""simple docstring"""





		@slow
		def _lowercase	(     self  )					->     Tuple:
				# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				for model_name in ["bert-base-uncased"]:
						_UpperCamelCase							:			Union[str, Any]     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			str     =   TFAutoModel.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Optional[int]     =   AutoModel.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		@slow
		def _lowercase	(     self  )					->     Any:
				# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				for model_name in ["bert-base-uncased"]:
						_UpperCamelCase							:			List[Any]     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Optional[Any]     =   TFAutoModelForPreTraining.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Tuple     =   AutoModelForPreTraining.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		@slow
		def _lowercase	(     self  )					->     Any:
				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_UpperCamelCase							:			Optional[int]     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			int     =   TFAutoModelForCausalLM.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						_UpperCamelCase,				_UpperCamelCase							:			Any     =   TFAutoModelForCausalLM.from_pretrained(
						    _snake_case	,       output_loading_info=_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Tuple     =   AutoModelForCausalLM.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						_UpperCamelCase,				_UpperCamelCase							:			Optional[Any]     =   AutoModelForCausalLM.from_pretrained(
						    _snake_case	,       output_loading_info=_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		@slow
		def _lowercase	(     self  )					->     Dict:
				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_UpperCamelCase							:			Optional[Any]     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Any     =   TFAutoModelWithLMHead.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Optional[int]     =   AutoModelWithLMHead.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		@slow
		def _lowercase	(     self  )					->     str:
				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_UpperCamelCase							:			Optional[int]     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			List[Any]     =   TFAutoModelForMaskedLM.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						_UpperCamelCase,				_UpperCamelCase							:			str     =   TFAutoModelForMaskedLM.from_pretrained(
						    _snake_case	,       output_loading_info=_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			int     =   AutoModelForMaskedLM.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						_UpperCamelCase,				_UpperCamelCase							:			Tuple     =   AutoModelForMaskedLM.from_pretrained(
						    _snake_case	,       output_loading_info=_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		@slow
		def _lowercase	(     self  )					->     Optional[Any]:
				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_UpperCamelCase							:			str     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Optional[int]     =   TFAutoModelForSeqaSeqLM.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						_UpperCamelCase,				_UpperCamelCase							:			Any     =   TFAutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case	,       output_loading_info=_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Any     =   AutoModelForSeqaSeqLM.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						_UpperCamelCase,				_UpperCamelCase							:			Any     =   AutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case	,       output_loading_info=_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		@slow
		def _lowercase	(     self  )					->     List[str]:
				# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				for model_name in ["bert-base-uncased"]:
						_UpperCamelCase							:			List[str]     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Any     =   TFAutoModelForSequenceClassification.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			List[Any]     =   AutoModelForSequenceClassification.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		@slow
		def _lowercase	(     self  )					->     List[Any]:
				# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				for model_name in ["bert-base-uncased"]:
						_UpperCamelCase							:			List[str]     =   AutoConfig.from_pretrained(_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			Tuple     =   TFAutoModelForQuestionAnswering.from_pretrained(_snake_case	,       from_pt=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )

						_UpperCamelCase							:			List[str]     =   AutoModelForQuestionAnswering.from_pretrained(_snake_case	,       from_tf=_snake_case  )
						self.assertIsNotNone(_snake_case  )
						self.assertIsInstance(_snake_case	,       _snake_case  )
		def _lowercase	(     self  )					->     Optional[int]:
				_UpperCamelCase							:			Union[str, Any]     =   TFAutoModelWithLMHead.from_pretrained(_snake_case	,       from_pt=_snake_case  )
				self.assertIsInstance(_snake_case	,       _snake_case  )
				self.assertEqual(model.num_parameters()	,       14410  )
				self.assertEqual(model.num_parameters(only_trainable=_snake_case  )	,       14410  )

				_UpperCamelCase							:			Tuple     =   AutoModelWithLMHead.from_pretrained(_snake_case	,       from_tf=_snake_case  )
				self.assertIsInstance(_snake_case	,       _snake_case  )
				self.assertEqual(model.num_parameters()	,       14410  )
				self.assertEqual(model.num_parameters(only_trainable=_snake_case  )	,       14410  )




		def _lowercase	(     self  )					->     str:
				_UpperCamelCase							:			List[Any]     =   TFAutoModelWithLMHead.from_pretrained(_snake_case	,       from_pt=_snake_case  )
				self.assertIsInstance(_snake_case	,       _snake_case  )
				self.assertEqual(model.num_parameters()	,       14410  )
				self.assertEqual(model.num_parameters(only_trainable=_snake_case  )	,       14410  )

				_UpperCamelCase							:			Union[str, Any]     =   AutoModelWithLMHead.from_pretrained(_snake_case	,       from_tf=_snake_case  )
				self.assertIsInstance(_snake_case	,       _snake_case  )
				self.assertEqual(model.num_parameters()	,       14410  )
				self.assertEqual(model.num_parameters(only_trainable=_snake_case  )	,       14410  )













from __future__ import annotations

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

lowercase    				=   TypeVar("T")
lowercase    				=   TypeVar("U")




class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       List[Any]:
								snake_case_           =				key
								snake_case_           =				val
								snake_case_           =				None
								snake_case_           =				None






	def __repr__(			self   )			->       str:
								return (
								    F'''Node: key: {self.key}, val: {self.val}, '''
								    F'''has next: {bool(self.next   )}, has prev: {bool(self.prev   )}'''
								)






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self   )			->       None:
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_     ,						snake_case_           =				self.rear, self.head




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




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								snake_case_           =				self.rear.prev

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

								snake_case_           =				node
								snake_case_           =				previous
								snake_case_           =				node
								snake_case_           =				self.rear






	def 				_UpperCamelCase     (			self			,     a   )			->       DoubleLinkedListNode[T, U] | None:

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

								snake_case_           =				node.next
								snake_case_           =				node.prev
								snake_case_           =				None
								snake_case_           =				None
								return node






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	lowerCAmelCase						=					{}




	def __init__(			self			,     a   )			->       str:
								snake_case_           =				DoubleLinkedList()
								snake_case_           =				capacity
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				{}




	def __repr__(			self   )			->       str:
								return (
								    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
								    F'''capacity={self.capacity}, current size={self.num_keys})'''
								)




	def __contains__(			self			,     a   )			->       bool:
								return key in self.cache




	def 				_UpperCamelCase     (			self			,     a   )			->       U | None:
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
															self.hits += 1
															snake_case_           =				self.cache[key]
															snake_case_           =				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 				_UpperCamelCase     (			self			,     a			,     a   )			->       None:

								if key not in self.cache:
															if self.num_keys >= self.capacity:
																						# delete first node (oldest) when over capacity
																						snake_case_           =				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
																						)  # node guaranteed to be in list assert node.key is not None

																						del self.cache[first_node.key]
																						self.num_keys -= 1
															snake_case_           =				DoubleLinkedListNode(a			,     a   )
															self.list.add(self.cache[key]   )
															self.num_keys += 1

								else:
															# bump node to the end of the list, update value
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node is not None  # node guaranteed to be in list
															snake_case_           =				value
															self.list.add(a   )






	@classmethod
	def 				_UpperCamelCase     (			cls			,     a = 1_28   )			->       Callable[[Callable[[T], U]], Callable[..., U]]:

								def cache_decorator_inner(a   ) -> Callable[..., U]:
															def cache_decorator_wrapper(*a   ) -> U:
																						if func not in cls.decorator_function_to_instance_map:
																													snake_case_           =				LRUCache(a   )

																						snake_case_           =				cls.decorator_function_to_instance_map[func].get(args[0]   )
																						if result is None:
																													snake_case_           =				func(*a   )
																													cls.decorator_function_to_instance_map[func].put(args[0]			,     a   )
																						return result

															def cache_info() -> LRUCache[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()





