


"""simple docstring"""


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A  :     Union[str, Any]    					=    re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class 						_UpperCamelCase						:
							'''simple docstring'''







							__UpperCAmelCase		:					str
							__UpperCAmelCase		:					Optional[str]          =None
							__UpperCAmelCase		:					Optional[Union[str, int]]          =None
							__UpperCAmelCase		:					Optional[Union[str, int]]          =None
							__UpperCAmelCase		:					Optional[Union[str, int]]          =None

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    _str_to_version_tuple(self.version_str			)

							def __repr__(  self			):
										return f"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"

							@property
							def     snake_case       (  self			):
										return self.major, self.minor, self.patch

							def     snake_case       (  self					,     __a			):
										if isinstance(__a					,     __a			):
													return Version(__a			)
										elif isinstance(__a					,     __a			):
													return other
										raise TypeError(f"{other} (type {type(__a			)}) cannot be compared to version."			)

							def __eq__(  self					,     __a			):
										try:
													__lowerCAmelCase       					=    self._validate_operand(__a			)
										except (TypeError, ValueError):
													return False
										else:
													return self.tuple == other.tuple

							def __lt__(  self					,     __a			):
										__lowerCAmelCase       					=    self._validate_operand(__a			)
										return self.tuple < other.tuple

							def __hash__(  self			):
										return hash(_version_tuple_to_str(self.tuple			)			)

							@classmethod
							def     snake_case       (  cls					,     __a			):
										__lowerCAmelCase       					=    {f.name for f in dataclasses.fields(cls			)}
										return cls(**{k: v for k, v in dic.items() if k in field_names}			)



							def     snake_case       (  self			):
										return self.version_str


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    _VERSION_REG.match(_UpperCamelCase   )
			if not res:
						raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."   )
			return tuple(int(_UpperCamelCase   ) for v in [res.group("major"   ), res.group("minor"   ), res.group("patch"   )]   )


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			return ".".join(str(_UpperCamelCase   ) for v in version_tuple   )








"""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	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a			):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]			):
																__lowerCAmelCase       					=    model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     only_pretrain_model=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "patrickvonplaten/t5-tiny-random"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     configs=[config]			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     use_xla=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    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					,     )
													__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    "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:
													__lowerCAmelCase       					=    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					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													__lowerCAmelCase       					=    benchmark.run()
													_check_summary_is_not_empty(result.inference_summary			)
													self.assertTrue(Path(os.path.join(__a					,     "log.txt"			)			).exists()			)




