



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 		snake_case__			(				self	:       int			,     lowercase	:       List[Any]   )						->					Optional[Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       List[str]   )						->					int:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       List[Any]   )						->					str:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Dict   )						->					Dict:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       List[str]   )						->					Any:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     eager_mode=lowercase			,     multi_process=lowercase			,     )
									__lowercase					=				TensorFlowBenchmark(lowercase			,     [config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       Tuple   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				TensorFlowBenchmark(lowercase			,     [config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Union[str, Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Optional[int]   )						->					Any:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				TensorFlowBenchmark(lowercase			,     [config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result   )
									self.check_results_dict_not_empty(results.memory_train_result   )





			def 		snake_case__			(				self	:       List[str]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				"""patrickvonplaten/t5-tiny-random"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				TensorFlowBenchmark(lowercase			,     configs=[config]   )
									__lowercase					=				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 		snake_case__			(				self	:       List[Any]   )						->					str:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Dict   )						->					Dict:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       List[Any]   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""

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

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










from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase			)
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  str      =			field(default="""summarization"""    ,					metadata={"""include_in_asdict_even_if_is_default""": True}			)
			lowercase__      :  ClassVar[Features]      =			Features({"""text""": Value("""string"""			)}			)
			lowercase__      :  ClassVar[Features]      =			Features({"""summary""": Value("""string"""			)}			)
			lowercase__      :  str      =			"text"
			lowercase__      :  str      =			"summary"





			@property
			def 		snake_case__			(				self	:       List[Any]   )						->					Dict[str, str]:
									"""simple docstring"""







									return {self.text_column: "text", self.summary_column: "summary"}





