




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    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   str  )						->   List[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['''bs''']       ,						model_result['''ss''']  ):
																		__magic_name__							:	Union[str, Any]    							=							model_result['''result'''][batch_size][sequence_length]
																		self.assertIsNotNone(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   str:
								__magic_name__							:	Dict    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Optional[int]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						eager_mode=lowerCamelCase_       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Union[str, Any]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	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 							UpperCAmelCase__       ( self			:   List[str]  )						->   Tuple:
								__magic_name__							:	List[str]    							=							'''sgugger/tiny-distilbert-classification'''
								__magic_name__							:	Union[str, Any]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						only_pretrain_model=lowerCamelCase_       ,						)
								__magic_name__							:	Optional[int]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	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 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								__magic_name__							:	Optional[Any]    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	List[str]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Union[str, Any]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	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 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Tuple:
								__magic_name__							:	Union[str, Any]    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Any    							=							AutoConfig.from_pretrained(lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						eager_mode=lowerCamelCase_       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Any    							=							TensorFlowBenchmark(lowerCamelCase_       ,						[config]  )
								__magic_name__							:	Dict    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )

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

			def 							UpperCAmelCase__       ( self			:   Any  )						->   str:
								__magic_name__							:	Any    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Dict    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Dict    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	Union[str, Any]    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result  )
								self.check_results_dict_not_empty(results.memory_train_result  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   int:
								__magic_name__							:	Union[str, Any]    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Any    							=							AutoConfig.from_pretrained(lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	int    							=							TensorFlowBenchmark(lowerCamelCase_       ,						[config]  )
								__magic_name__							:	Optional[Any]    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result  )
								self.check_results_dict_not_empty(results.memory_train_result  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Optional[Any]:
								__magic_name__							:	Optional[Any]    							=							'''patrickvonplaten/t5-tiny-random'''
								__magic_name__							:	str    							=							AutoConfig.from_pretrained(lowerCamelCase_  )
								__magic_name__							:	Any    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Optional[int]    							=							TensorFlowBenchmark(lowerCamelCase_       ,						configs=[config]  )
								__magic_name__							:	List[str]    							=							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 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   List[Any]:
								__magic_name__							:	Dict    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Tuple    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						use_xla=lowerCamelCase_       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	List[str]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	int    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )

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


			def 							UpperCAmelCase__       ( self			:   str  )						->   int:
								__magic_name__							:	Optional[Any]    							=							'''sshleifer/tiny-gpt2'''

								def _check_summary_is_not_empty(lowerCamelCase_			:   Optional[Any]  ):
													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:
													__magic_name__							:	Dict    							=							TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,						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_       ,						eager_mode=lowerCamelCase_       ,						multi_process=lowerCamelCase_       ,						)
													__magic_name__							:	Optional[Any]    							=							TensorFlowBenchmark(lowerCamelCase_  )
													__magic_name__							:	List[str]    							=							benchmark.run()
													_check_summary_is_not_empty(result.inference_summary  )
													self.assertTrue(Path(os.path.join(lowerCamelCase_       ,						'''log.txt'''  )  ).exists()  )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



