"""simple docstring"""





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
							import tensorflow as tf


_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)


@dataclass
class   UpperCAmelCase_			(					_a):
						lowerCamelCase__	:					Tuple										=						[
						    "no_inference",
						    "no_cuda",
						    "no_tpu",
						    "no_speed",
						    "no_memory",
						    "no_env_print",
						    "no_multi_process",
						]


						def __init__(     self	,    **a							) ->      Tuple:
													for deprecated_arg in self.deprecated_args:
																				if deprecated_arg in kwargs:
																											lowercase__    :	Dict     					=			deprecated_arg[3:]
																											lowercase__    :	Tuple     					=			not kwargs.pop(a							)
																											logger.warning(
																											    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																											    f""" {positive_arg}={kwargs[positive_arg]}"""							)
													lowercase__    :	Optional[int]     					=			kwargs.pop('tpu_name'	,    self.tpu_name							)
													lowercase__    :	List[Any]     					=			kwargs.pop('device_idx'	,    self.device_idx							)
													lowercase__    :	Optional[int]     					=			kwargs.pop('eager_mode'	,    self.eager_mode							)
													lowercase__    :	Any     					=			kwargs.pop('use_xla'	,    self.use_xla							)
													super().__init__(**a							)

						lowerCamelCase__	:					str										=						field(
						    default=_a				,							metadata={"help": "Name of TPU"}				,							)
						lowerCamelCase__	:					int										=						field(
						    default=0				,							metadata={"help": "CPU / GPU device index. Defaults to 0."}				,							)
						lowerCamelCase__	:					bool										=						field(default=_a				,							metadata={"help": "Benchmark models in eager model."})
						lowerCamelCase__	:					bool										=						field(
						    default=_a				,							metadata={
						        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
						    }				,							)


						@cached_property
						def 	_UpperCAmelCase (     self							) ->      Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
													requires_backends(self	,    ['tf']							)
													lowercase__    :	Any     					=			None
													if self.tpu:
																				try:
																											if self.tpu_name:
																																		lowercase__    :	List[str]     					=			tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name							)
																											else:
																																		lowercase__    :	List[str]     					=			tf.distribute.cluster_resolver.TPUClusterResolver()
																				except ValueError:
																											lowercase__    :	List[str]     					=			None
													return tpu


						@cached_property
						def 	_UpperCAmelCase (     self							) ->      Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
													requires_backends(self	,    ['tf']							)
													if self.is_tpu:
																				tf.config.experimental_connect_to_cluster(self._setup_tpu							)
																				tf.tpu.experimental.initialize_tpu_system(self._setup_tpu							)

																				lowercase__    :	List[Any]     					=			tf.distribute.TPUStrategy(self._setup_tpu							)
													else:
																				# currently no multi gpu is allowed
																				if self.is_gpu:
																											# TODO: Currently only single GPU is supported
																											tf.config.set_visible_devices(self.gpu_list[self.device_idx]	,    'GPU'							)
																											lowercase__    :	int     					=			tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""							)
																				else:
																											tf.config.set_visible_devices([]	,    'GPU'							)  # disable GPU
																											lowercase__    :	str     					=			tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""							)

													return strategy


						@property
						def 	_UpperCAmelCase (     self							) ->      bool:
													requires_backends(self	,    ['tf']							)
													return self._setup_tpu is not None


						@property
						def 	_UpperCAmelCase (     self							) ->      "tf.distribute.Strategy":
													requires_backends(self	,    ['tf']							)
													return self._setup_strategy


						@property
						def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
													requires_backends(self	,    ['tf']							)
													return tf.config.list_physical_devices('GPU'							)


						@property
						def 	_UpperCAmelCase (     self							) ->      int:
													requires_backends(self	,    ['tf']							)
													if self.cuda:
																				return len(self.gpu_list							)
													return 0


						@property
						def 	_UpperCAmelCase (     self							) ->      bool:
													return self.n_gpu > 0


"""simple docstring"""





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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             for model_result in results.values():
                    for batch_size, sequence_length in zip(model_result['bs']	,    model_result['ss']							):
                           lowercase__    :	str     					=			model_result['result'][batch_size][sequence_length]
                           self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a							)
             lowercase__    :	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							) ->      int:
             lowercase__    :	List[str]     					=			'sgugger/tiny-distilbert-classification'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    only_pretrain_model=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	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							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	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]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[Any]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	Tuple     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	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							) ->      int:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[str]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	List[str]     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	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							) ->      Tuple:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Tuple     					=			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							) ->      str:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[int]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	str     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	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 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[str]     					=			'patrickvonplaten/t5-tiny-random'
             lowercase__    :	Any     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	List[str]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	int     					=			TensorFlowBenchmark(a	,    configs=[config]							)
             lowercase__    :	Union[str, 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(is_tf_available() and len(tf.config.list_physical_devices('GPU'							)							) == 0	,    'Cannot do xla on CPU.'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    use_xla=a	,    multi_process=a	,    )
             lowercase__    :	Any     					=			TensorFlowBenchmark(a							)
             lowercase__    :	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							) ->      List[str]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[Any]     					=			TensorFlowBenchmarkArguments(
                        models=[MODEL_ID]	,    inference=a	,    save_to_csv=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    inference_time_csv_file=os.path.join(a	,    'inf_time.csv'							)	,    inference_memory_csv_file=os.path.join(a	,    'inf_mem.csv'							)	,    env_info_csv_file=os.path.join(a	,    'env.csv'							)	,    multi_process=a	,    )
                    lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmark(a							)
                    benchmark.run()
                    self.assertTrue(Path(os.path.join(a	,    'inf_time.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'inf_mem.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'env.csv'							)							).exists()							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Tuple     					=			'sshleifer/tiny-gpt2'

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

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

