
'''simple docstring'''

import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class  _UpperCAmelCase :

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=2							,		lowerCAmelCase_=8							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=99							,		lowerCAmelCase_=16							,		lowerCAmelCase_=5							,		lowerCAmelCase_=2							,		lowerCAmelCase_=36							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=0.0							,		lowerCAmelCase_=0.0							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=16							,		lowerCAmelCase_=2							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=3							,		lowerCAmelCase_=4							,		lowerCAmelCase_=None							,		):

       '''simple docstring'''

       a_      :	Tuple      =							parent
       a_      :	List[Any]      =							batch_size
       a_      :	Any      =							seq_length
       a_      :	Optional[int]      =							is_training
       a_      :	Optional[Any]      =							use_input_mask
       a_      :	Union[str, Any]      =							use_token_type_ids
       a_      :	List[Any]      =							use_labels
       a_      :	Any      =							vocab_size
       a_      :	str      =							hidden_size
       a_      :	Union[str, Any]      =							num_hidden_layers
       a_      :	Tuple      =							num_attention_heads
       a_      :	Union[str, Any]      =							intermediate_size
       a_      :	int      =							hidden_act
       a_      :	Optional[int]      =							hidden_dropout_prob
       a_      :	Optional[Any]      =							attention_probs_dropout_prob
       a_      :	Dict      =							max_position_embeddings
       a_      :	Optional[int]      =							type_vocab_size
       a_      :	List[str]      =							type_sequence_label_size
       a_      :	int      =							initializer_range
       a_      :	Optional[Any]      =							num_labels
       a_      :	List[Any]      =							num_choices
       a_      :	Optional[int]      =							scope


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size )

       a_      :	Tuple      =							None
       if self.use_input_mask:
         a_      :	Any      =							random_attention_mask([self.batch_size, self.seq_length] )

       a_      :	List[Any]      =							None
       if self.use_token_type_ids:
         a_      :	Any      =							ids_tensor([self.batch_size, self.seq_length]							,		self.type_vocab_size )

       a_      :	Any      =							None
       a_      :	Tuple      =							None
       a_      :	Optional[int]      =							None
       if self.use_labels:
         a_      :	int      =							ids_tensor([self.batch_size]							,		self.type_sequence_label_size )
         a_      :	Optional[int]      =							ids_tensor([self.batch_size, self.seq_length]							,		self.num_labels )
         a_      :	Any      =							ids_tensor([self.batch_size]							,		self.num_choices )

       a_      :	List[Any]      =							self.get_config()

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return MraConfig(
           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							,		hidden_dropout_prob=self.hidden_dropout_prob							,		attention_probs_dropout_prob=self.attention_probs_dropout_prob							,		max_position_embeddings=self.max_position_embeddings							,		type_vocab_size=self.type_vocab_size							,		is_decoder=lowerCAmelCase_							,		initializer_range=self.initializer_range							,		)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_config()
       a_      :	Optional[Any]      =							3_00
       return config


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       (
           a_ 
       )      :	List[str]      =							self.prepare_config_and_inputs()

       a_      :	Dict      =							True
       a_      :	Optional[int]      =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       a_      :	Optional[Any]      =							ids_tensor([self.batch_size, self.seq_length]							,		vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[Any]      =							MraModel(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Optional[int]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_ )
       a_      :	Optional[int]      =							model(lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_ )
       a_      :	Any      =							model(lowerCAmelCase_ )
       self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	Optional[Any]      =							True
       a_      :	Optional[int]      =							MraModel(lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Optional[Any]      =							model(
           lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		encoder_hidden_states=lowerCAmelCase_							,		encoder_attention_mask=lowerCAmelCase_							,		)
       a_      :	Optional[int]      =							model(
           lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		encoder_hidden_states=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_ )
       self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							MraForMaskedLM(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	str      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	str      =							MraForQuestionAnswering(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	int      =							model(
           lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		start_positions=lowerCAmelCase_							,		end_positions=lowerCAmelCase_							,		)
       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							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							self.num_labels
       a_      :	Dict      =							MraForSequenceClassification(lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Tuple      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							self.num_labels
       a_      :	Optional[Any]      =							MraForTokenClassification(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Tuple      =							model(lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.num_labels) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							self.num_choices
       a_      :	List[Any]      =							MraForMultipleChoice(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Union[str, Any]      =							input_ids.unsqueeze(1 ).expand(-1							,		self.num_choices							,		-1 ).contiguous()
       a_      :	List[Any]      =							token_type_ids.unsqueeze(1 ).expand(-1							,		self.num_choices							,		-1 ).contiguous()
       a_      :	Union[str, Any]      =							input_mask.unsqueeze(1 ).expand(-1							,		self.num_choices							,		-1 ).contiguous()
       a_      :	str      =							model(
           lowerCAmelCase_							,		attention_mask=lowerCAmelCase_							,		token_type_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_							,		)
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_choices) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.prepare_config_and_inputs()
       (
           a_ 
       )      :	str      =							config_and_inputs
       a_      :	Optional[int]      =							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					(
         (
             MraModel,
             MraForMaskedLM,
             MraForMultipleChoice,
             MraForQuestionAnswering,
             MraForSequenceClassification,
             MraForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     a_    	=					False
     a_    	=					False
     a_    	=					False
     a_    	=					False

     a_    	=					()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							MraModelTester(self )
       a_      :	Optional[int]      =							ConfigTester(self							,		config_class=lowerCAmelCase_							,		hidden_size=37 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.config_tester.run_common_tests()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         a_      :	Any      =							type
         self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_ )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_      :	Optional[Any]      =							MraModel.from_pretrained(lowerCAmelCase_ )
         self.assertIsNotNone(lowerCAmelCase_ )






     @unittest.skip(reason="""MRA does not output attentions""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return







@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
       a_      :	str      =							torch.arange(2_56 ).unsqueeze(0 )

       with torch.no_grad():
         a_      :	Optional[Any]      =							model(lowerCAmelCase_ )[0]

       a_      :	int      =							torch.Size((1, 2_56, 7_68) )
       self.assertEqual(output.shape							,		lowerCAmelCase_ )

       a_      :	Optional[Any]      =							torch.tensor(
           [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
       a_      :	int      =							torch.arange(2_56 ).unsqueeze(0 )

       with torch.no_grad():
         a_      :	Dict      =							model(lowerCAmelCase_ )[0]

       a_      :	int      =							5_02_65

       a_      :	Any      =							torch.Size((1, 2_56, vocab_size) )
       self.assertEqual(output.shape							,		lowerCAmelCase_ )

       a_      :	Tuple      =							torch.tensor(
           [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
       a_      :	Any      =							torch.arange(40_96 ).unsqueeze(0 )

       with torch.no_grad():
         a_      :	List[Any]      =							model(lowerCAmelCase_ )[0]

       a_      :	str      =							5_02_65

       a_      :	str      =							torch.Size((1, 40_96, vocab_size) )
       self.assertEqual(output.shape							,		lowerCAmelCase_ )

       a_      :	Optional[Any]      =							torch.tensor(
           [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )




'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       for model_result in results.values():
         for batch_size, sequence_length in zip(model_result["""bs"""]							,		model_result["""ss"""] ):
           a_      :	str      =							model_result["""result"""][batch_size][sequence_length]
           self.assertIsNotNone(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	List[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 ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sgugger/tiny-distilbert-classification"""
       a_      :	str      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		only_pretrain_model=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	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 ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		torchscript=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[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(torch_device == """cpu"""							,		"""Cant do half precision""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		fpaa=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	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 ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Dict      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       # set architectures equal to `None`
       a_      :	Any      =							None
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	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 ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Tuple      =							benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result )
       self.check_results_dict_not_empty(results.memory_train_result )


     @unittest.skipIf(torch_device == """cpu"""							,		"""Can't do half precision""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		fpaa=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[str]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[int]      =							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 ):

       '''simple docstring'''

       a_      :	List[Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[str]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	List[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 ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tinier_bart"""
       a_      :	Union[str, Any]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Dict      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	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 ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Tuple      =							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 ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tinier_bart"""
       a_      :	int      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Dict      =							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 ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							"""sshleifer/tiny-gpt2"""
       with tempfile.TemporaryDirectory() as tmp_dir:
         a_      :	List[str]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		save_to_csv=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		inference_time_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" )							,		train_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" )							,		inference_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" )							,		train_time_csv_file=os.path.join(lowerCAmelCase_							,		"""train_time.csv""" )							,		env_info_csv_file=os.path.join(lowerCAmelCase_							,		"""env.csv""" )							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_ )
         benchmark.run()
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""env.csv""" ) ).exists() )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""

       def _check_summary_is_not_empty(lowerCAmelCase_ ):
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""sequential""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""cumulative""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""current""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""total""" ) )

       with tempfile.TemporaryDirectory() as tmp_dir:
         a_      :	List[Any]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		log_filename=os.path.join(lowerCAmelCase_							,		"""log.txt""" )							,		log_print=lowerCAmelCase_							,		trace_memory_line_by_line=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Dict      =							PyTorchBenchmark(lowerCAmelCase_ )
         a_      :	Tuple      =							benchmark.run()
         _check_summary_is_not_empty(result.inference_summary )
         _check_summary_is_not_empty(result.train_summary )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""log.txt""" ) ).exists() )


