
"""simple docstring"""







import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    SqueezeBertForMaskedLM,
							    SqueezeBertForMultipleChoice,
							    SqueezeBertForQuestionAnswering,
							    SqueezeBertForSequenceClassification,
							    SqueezeBertForTokenClassification,
							    SqueezeBertModel,
							)




class 			A__	(     _lowerCamelCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=13	,		_SCREAMING_SNAKE_CASE=7	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=99	,		_SCREAMING_SNAKE_CASE=32	,		_SCREAMING_SNAKE_CASE=5	,		_SCREAMING_SNAKE_CASE=4	,		_SCREAMING_SNAKE_CASE=64	,		_SCREAMING_SNAKE_CASE="gelu"	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=5_12	,		_SCREAMING_SNAKE_CASE=16	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=0.02	,		_SCREAMING_SNAKE_CASE=3	,		_SCREAMING_SNAKE_CASE=4	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=4	,		_SCREAMING_SNAKE_CASE=1	,		):
			__lowerCAmelCase      :						Tuple						      =				parent
			__lowerCAmelCase      :						Optional[Any]						      =				batch_size
			__lowerCAmelCase      :						Dict						      =				seq_length
			__lowerCAmelCase      :						Optional[Any]						      =				is_training
			__lowerCAmelCase      :						Tuple						      =				use_input_mask
			__lowerCAmelCase      :						Tuple						      =				use_token_type_ids
			__lowerCAmelCase      :						Tuple						      =				use_labels
			__lowerCAmelCase      :						List[Any]						      =				vocab_size
			__lowerCAmelCase      :						Dict						      =				hidden_size
			__lowerCAmelCase      :						List[Any]						      =				num_hidden_layers
			__lowerCAmelCase      :						Any						      =				num_attention_heads
			__lowerCAmelCase      :						Any						      =				intermediate_size
			__lowerCAmelCase      :						Optional[Any]						      =				hidden_act
			__lowerCAmelCase      :						List[str]						      =				hidden_dropout_prob
			__lowerCAmelCase      :						int						      =				attention_probs_dropout_prob
			__lowerCAmelCase      :						Any						      =				max_position_embeddings
			__lowerCAmelCase      :						str						      =				type_vocab_size
			__lowerCAmelCase      :						Dict						      =				type_sequence_label_size
			__lowerCAmelCase      :						int						      =				initializer_range
			__lowerCAmelCase      :						Dict						      =				num_labels
			__lowerCAmelCase      :						List[str]						      =				num_choices
			__lowerCAmelCase      :						Optional[int]						      =				scope
			__lowerCAmelCase      :						List[Any]						      =				q_groups
			__lowerCAmelCase      :						str						      =				k_groups
			__lowerCAmelCase      :						Tuple						      =				v_groups
			__lowerCAmelCase      :						Union[str, Any]						      =				post_attention_groups
			__lowerCAmelCase      :						Dict						      =				intermediate_groups
			__lowerCAmelCase      :						Any						      =				output_groups

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				ids_tensor([self.batch_size, self.seq_length]	,		self.vocab_size			)

			__lowerCAmelCase      :						Optional[int]						      =				None
			if self.use_input_mask:
				__lowerCAmelCase      :						int						      =				random_attention_mask([self.batch_size, self.seq_length]			)

			__lowerCAmelCase      :						Union[str, Any]						      =				None
			__lowerCAmelCase      :						Dict						      =				None
			__lowerCAmelCase      :						Tuple						      =				None
			if self.use_labels:
				__lowerCAmelCase      :						int						      =				ids_tensor([self.batch_size]	,		self.type_sequence_label_size			)
				__lowerCAmelCase      :						Optional[int]						      =				ids_tensor([self.batch_size, self.seq_length]	,		self.num_labels			)
				__lowerCAmelCase      :						List[str]						      =				ids_tensor([self.batch_size]	,		self.num_choices			)

			__lowerCAmelCase      :						int						      =				self.get_config()

			return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

		def 							__lowerCamelCase							(				self			):
			return SqueezeBertConfig(
			    embedding_size=self.hidden_size	,		vocab_size=self.vocab_size	,		hidden_size=self.hidden_size	,		num_hidden_layers=self.num_hidden_layers	,		num_attention_heads=self.num_attention_heads	,		intermediate_size=self.intermediate_size	,		hidden_act=self.hidden_act	,		attention_probs_dropout_prob=self.hidden_dropout_prob	,		attention_dropout=self.attention_probs_dropout_prob	,		max_position_embeddings=self.max_position_embeddings	,		initializer_range=self.initializer_range	,		q_groups=self.q_groups	,		k_groups=self.k_groups	,		v_groups=self.v_groups	,		post_attention_groups=self.post_attention_groups	,		intermediate_groups=self.intermediate_groups	,		output_groups=self.output_groups	,		)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[int]						      =				SqueezeBertModel(config=_SCREAMING_SNAKE_CASE			)
			model.to(_SCREAMING_SNAKE_CASE			)
			model.eval()
			__lowerCAmelCase      :						List[str]						      =				model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				model(_SCREAMING_SNAKE_CASE			)
			self.parent.assertEqual(result.last_hidden_state.shape	,		(self.batch_size, self.seq_length, self.hidden_size)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Dict						      =				SqueezeBertForMaskedLM(config=_SCREAMING_SNAKE_CASE			)
			model.to(_SCREAMING_SNAKE_CASE			)
			model.eval()
			__lowerCAmelCase      :						Dict						      =				model(_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.seq_length, self.vocab_size)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						str						      =				SqueezeBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE			)
			model.to(_SCREAMING_SNAKE_CASE			)
			model.eval()
			__lowerCAmelCase      :						List[Any]						      =				model(
			    _SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		start_positions=_SCREAMING_SNAKE_CASE	,		end_positions=_SCREAMING_SNAKE_CASE			)
			self.parent.assertEqual(result.start_logits.shape	,		(self.batch_size, self.seq_length)			)
			self.parent.assertEqual(result.end_logits.shape	,		(self.batch_size, self.seq_length)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Dict						      =				self.num_labels
			__lowerCAmelCase      :						List[Any]						      =				SqueezeBertForSequenceClassification(_SCREAMING_SNAKE_CASE			)
			model.to(_SCREAMING_SNAKE_CASE			)
			model.eval()
			__lowerCAmelCase      :						Optional[int]						      =				model(_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_labels)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Dict						      =				self.num_labels
			__lowerCAmelCase      :						Dict						      =				SqueezeBertForTokenClassification(config=_SCREAMING_SNAKE_CASE			)
			model.to(_SCREAMING_SNAKE_CASE			)
			model.eval()

			__lowerCAmelCase      :						List[str]						      =				model(_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.seq_length, self.num_labels)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[str]						      =				self.num_choices
			__lowerCAmelCase      :						Any						      =				SqueezeBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE			)
			model.to(_SCREAMING_SNAKE_CASE			)
			model.eval()
			__lowerCAmelCase      :						str						      =				input_ids.unsqueeze(1			).expand(-1	,		self.num_choices	,		-1			).contiguous()
			__lowerCAmelCase      :						List[str]						      =				input_mask.unsqueeze(1			).expand(-1	,		self.num_choices	,		-1			).contiguous()
			__lowerCAmelCase      :						List[Any]						      =				model(
			    _SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE	,		)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_choices)			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.prepare_config_and_inputs()
			((__lowerCAmelCase)      ,       (__lowerCAmelCase)      ,       (__lowerCAmelCase)      ,       (__lowerCAmelCase)      ,       (__lowerCAmelCase)      ,       (__lowerCAmelCase))      :						Union[str, Any]						      =				config_and_inputs
			__lowerCAmelCase      :						str						      =				{'input_ids': input_ids, 'attention_mask': input_mask}
			return config, inputs_dict




@require_torch
class 			A__	(     _lowerCamelCase	,					_lowerCamelCase	,					unittest.TestCase):
		A_						:	List[Any]           =     (
		    (
		        SqueezeBertModel,
		        SqueezeBertForMaskedLM,
		        SqueezeBertForMultipleChoice,
		        SqueezeBertForQuestionAnswering,
		        SqueezeBertForSequenceClassification,
		        SqueezeBertForTokenClassification,
		    )
		    if is_torch_available()
		    else None
		)
		A_						:	int           =     (
		    {
		        'feature-extraction': SqueezeBertModel,
		        'fill-mask': SqueezeBertForMaskedLM,
		        'question-answering': SqueezeBertForQuestionAnswering,
		        'text-classification': SqueezeBertForSequenceClassification,
		        'token-classification': SqueezeBertForTokenClassification,
		        'zero-shot': SqueezeBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		A_						:	Dict           =     False
		A_						:	Optional[int]           =     True
		A_						:	List[str]           =     False

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				SqueezeBertModelTester(self			)
			__lowerCAmelCase      :						List[Any]						      =				ConfigTester(self	,		config_class=_SCREAMING_SNAKE_CASE	,		dim=37			)

		def 							__lowerCamelCase							(				self			):
			self.config_tester.run_common_tests()

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_squeezebert_model(*_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_squeezebert_for_masked_lm(*_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_squeezebert_for_question_answering(*_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_squeezebert_for_token_classification(*_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_SCREAMING_SNAKE_CASE			)







		@slow
		def 							__lowerCamelCase							(				self			):
			for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase      :						Any						      =				SqueezeBertModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)




@require_sentencepiece
@require_tokenizers
@require_torch
class 			A__	(     unittest.TestCase):

		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'			)

			__lowerCAmelCase      :						List[Any]						      =				torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]			)
			__lowerCAmelCase      :						Any						      =				model(_SCREAMING_SNAKE_CASE			)[0]
			__lowerCAmelCase      :						Any						      =				torch.Size((1, 3)			)
			self.assertEqual(output.shape	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				torch.tensor([[0.6401, -0.0349, -0.6041]]			)
			self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		atol=1E-4			)			)

"""simple docstring"""







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			for model_result in results.values():
				for batch_size, sequence_length in zip(model_result['bs']	,		model_result['ss']			):
					__lowerCAmelCase      :						Optional[Any]						      =				model_result['result'][batch_size][sequence_length]
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Tuple						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				'sgugger/tiny-distilbert-classification'
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		only_pretrain_model=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						str						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Tuple						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						Dict						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_train_result			)
			self.check_results_dict_not_empty(results.memory_train_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Any						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						List[Any]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_train_result			)
			self.check_results_dict_not_empty(results.memory_train_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'patrickvonplaten/t5-tiny-random'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		configs=[config]			)
			__lowerCAmelCase      :						Any						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'			)			) == 0	,		'Cannot do xla on CPU.'			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		use_xla=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]	,		inference=_SCREAMING_SNAKE_CASE	,		save_to_csv=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_time.csv'			)	,		inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_mem.csv'			)	,		env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE	,		'env.csv'			)	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
				__lowerCAmelCase      :						Tuple						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
				benchmark.run()
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_time.csv'			)			).exists()			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_mem.csv'			)			).exists()			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'env.csv'			)			).exists()			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				'sshleifer/tiny-gpt2'

			def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE			):
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'sequential'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'cumulative'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'current'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'total'			)			)

			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		log_filename=os.path.join(_SCREAMING_SNAKE_CASE	,		'log.txt'			)	,		log_print=_SCREAMING_SNAKE_CASE	,		trace_memory_line_by_line=_SCREAMING_SNAKE_CASE	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
				__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						int						      =				benchmark.run()
				_check_summary_is_not_empty(result.inference_summary			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'log.txt'			)			).exists()			)