from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Optional[Any]							=	{
    """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_    :		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_    :		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_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['bs']				,						model_result['ss']   ):
																		__a		 =       model_result['result'][batch_size][sequence_length]
																		self.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       'patrickvonplaten/t5-tiny-random'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						configs=[config]   )
								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



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







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       'sshleifer/tiny-gpt2'

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

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




