






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 				_UpperCamelCase     (			self   )			->       Tuple:
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
															snake_case_           =				AutoConfig.from_pretrained(a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				TFAutoModel.from_pretrained(a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModel.from_pretrained(a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




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

															snake_case_           =				TFAutoModelForPreTraining.from_pretrained(a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModelForPreTraining.from_pretrained(a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




	@slow
	def 				_UpperCamelCase     (			self   )			->       Any:
								for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =				AutoConfig.from_pretrained(a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				TFAutoModelForCausalLM.from_pretrained(a			,     from_pt=a   )
															snake_case_     ,						snake_case_           =				TFAutoModelForCausalLM.from_pretrained(
															    a			,     output_loading_info=a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModelForCausalLM.from_pretrained(a			,     from_tf=a   )
															snake_case_     ,						snake_case_           =				AutoModelForCausalLM.from_pretrained(
															    a			,     output_loading_info=a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




	@slow
	def 				_UpperCamelCase     (			self   )			->       Dict:
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =				AutoConfig.from_pretrained(a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				TFAutoModelWithLMHead.from_pretrained(a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModelWithLMHead.from_pretrained(a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




	@slow
	def 				_UpperCamelCase     (			self   )			->       str:
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =				AutoConfig.from_pretrained(a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				TFAutoModelForMaskedLM.from_pretrained(a			,     from_pt=a   )
															snake_case_     ,						snake_case_           =				TFAutoModelForMaskedLM.from_pretrained(
															    a			,     output_loading_info=a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModelForMaskedLM.from_pretrained(a			,     from_tf=a   )
															snake_case_     ,						snake_case_           =				AutoModelForMaskedLM.from_pretrained(
															    a			,     output_loading_info=a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




	@slow
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =				AutoConfig.from_pretrained(a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				TFAutoModelForSeqaSeqLM.from_pretrained(a			,     from_pt=a   )
															snake_case_     ,						snake_case_           =				TFAutoModelForSeqaSeqLM.from_pretrained(
															    a			,     output_loading_info=a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModelForSeqaSeqLM.from_pretrained(a			,     from_tf=a   )
															snake_case_     ,						snake_case_           =				AutoModelForSeqaSeqLM.from_pretrained(
															    a			,     output_loading_info=a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




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

															snake_case_           =				TFAutoModelForSequenceClassification.from_pretrained(a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModelForSequenceClassification.from_pretrained(a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




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

															snake_case_           =				TFAutoModelForQuestionAnswering.from_pretrained(a			,     from_pt=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )

															snake_case_           =				AutoModelForQuestionAnswering.from_pretrained(a			,     from_tf=a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a			,     a   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				TFAutoModelWithLMHead.from_pretrained(a			,     from_pt=a   )
								self.assertIsInstance(a			,     a   )
								self.assertEqual(model.num_parameters()			,     1_44_10   )
								self.assertEqual(model.num_parameters(only_trainable=a   )			,     1_44_10   )

								snake_case_           =				AutoModelWithLMHead.from_pretrained(a			,     from_tf=a   )
								self.assertIsInstance(a			,     a   )
								self.assertEqual(model.num_parameters()			,     1_44_10   )
								self.assertEqual(model.num_parameters(only_trainable=a   )			,     1_44_10   )






	def 				_UpperCamelCase     (			self   )			->       str:
								snake_case_           =				TFAutoModelWithLMHead.from_pretrained(a			,     from_pt=a   )
								self.assertIsInstance(a			,     a   )
								self.assertEqual(model.num_parameters()			,     1_44_10   )
								self.assertEqual(model.num_parameters(only_trainable=a   )			,     1_44_10   )

								snake_case_           =				AutoModelWithLMHead.from_pretrained(a			,     from_tf=a   )
								self.assertIsInstance(a			,     a   )
								self.assertEqual(model.num_parameters()			,     1_44_10   )
								self.assertEqual(model.num_parameters(only_trainable=a   )			,     1_44_10   )













class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





