



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      _a				(  self       :							Dict					,       _snake_case       :							Optional[int]       ):






								"""simple docstring"""


								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(_snake_case       )



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       eager_mode=_snake_case					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case       )
								A__	  =     benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     'sgugger/tiny-distilbert-classification'
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       multi_process=_snake_case					,       only_pretrain_model=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case       )
								A__	  =     benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case       )
								A__	  =     benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'
								A__	  =     AutoConfig.from_pretrained(_snake_case       )
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       eager_mode=_snake_case					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case					,       [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      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'
								A__	  =     AutoConfig.from_pretrained(_snake_case       )
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case					,       [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      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case       )
								A__	  =     benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result       )
								self.check_results_dict_not_empty(results.memory_train_result       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'
								A__	  =     AutoConfig.from_pretrained(_snake_case       )
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case					,       [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      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     'patrickvonplaten/t5-tiny-random'
								A__	  =     AutoConfig.from_pretrained(_snake_case       )
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case					,       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      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'
								A__	  =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]					,       training=_snake_case					,       inference=_snake_case					,       sequence_lengths=[8]					,       batch_sizes=[1]					,       use_xla=_snake_case					,       multi_process=_snake_case					,       )
								A__	  =     TensorFlowBenchmark(_snake_case       )
								A__	  =     benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


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



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     'sshleifer/tiny-gpt2'

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

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






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    List[str]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




