



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async



def 	__a					(   lowerCAmelCase_       : List[Any]=None    )				->			Optional[int]:






						'''simple docstring'''



						if subparsers is not None:
												UpperCAmelCase_=       subparsers.add_parser("""test"""    )
						else:
												UpperCAmelCase_=       argparse.ArgumentParser("""Accelerate test command"""    )

						parser.add_argument(
						    """--config_file"""      ,default=_lowercase      ,help=(
						        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
						        """location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have """
						        """such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed """
						        """with \'huggingface\'."""
						    )      ,)

						if subparsers is not None:
												parser.set_defaults(func=_lowercase    )
						return parser



def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       os.path.sep.join(__file__.split(os.path.sep    )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""]    )

						if args.config_file is None:
												UpperCAmelCase_=       script_name
						else:
												UpperCAmelCase_=       F"""--config_file={args.config_file} {script_name}"""

						UpperCAmelCase_=       ["""accelerate-launch"""] + test_args.split()
						UpperCAmelCase_=       execute_subprocess_async(_lowercase      ,env=os.environ.copy()    )
						if result.returncode == 0:
												print("""Test is a success! You are ready for your distributed training!"""    )



def 	__a					(   )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       test_command_parser()
						UpperCAmelCase_=       parser.parse_args()
						test_command(_lowercase    )


if __name__ == "__main__":
			main()









import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__A       =						logging.get_logger(__name__)



def 	__a					(   lowerCAmelCase_       : Tuple=None      ,lowerCAmelCase_       : Optional[Any]=None    )				->			Tuple:






						'''simple docstring'''



						return field(default_factory=lambda: default      ,metadata=lowerCAmelCase_    )






@dataclass
class 		lowercase						:






	"""simple docstring"""


	a__				: List[str]		       =							list_field(
	    default=[]			,   metadata={
	        "help": (
	            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
	            " of all available models"
	        )
	    }			,   )

	a__				: List[int]		       =							list_field(
	    default=[8]			,   metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"})

	a__				: List[int]		       =							list_field(
	    default=[8, 32, 128, 512]			,   metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}			,   )

	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Use FP16 to accelerate inference."})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Benchmark training of model"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Verbose memory tracing"})
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}			,   )
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }			,   )
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Trace memory line by line"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Save result to a CSV file"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Save all print statements in a log file"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Whether to print environment information"})
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={
	        "help": (
	            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
	            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
	            " for debugging / testing and on TPU."
	        )
	    }			,   )
	a__				: str		       =							field(
	    default=F'inference_time_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving time results to csv."}			,   )
	a__				: str		       =							field(
	    default=F'inference_memory_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving memory results to csv."}			,   )
	a__				: str		       =							field(
	    default=F'train_time_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving time results to csv for training."}			,   )
	a__				: str		       =							field(
	    default=F'train_memory_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving memory results to csv for training."}			,   )
	a__				: str		       =							field(
	    default=F'env_info_{round(time())}.csv'			,   metadata={"help": "CSV filename used if saving environment information."}			,   )
	a__				: str		       =							field(
	    default=F'log_{round(time())}.csv'			,   metadata={"help": "Log filename used if print statements are saved in log."}			,   )
	a__				: int		       =							field(default=3			,   metadata={"help": "Times an experiment will be run."})
	a__				: bool		       =							field(
	    default=snake_case__			,   metadata={
	        "help": (
	            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
	            " model weights."
	        )
	    }			,   )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			str:
							warnings.warn(
							    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
							    """ are deprecated in general and it is advised to use external Benchmarking libraries """
							    """ to benchmark Transformer models."""						,  __UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							return json.dumps(dataclasses.asdict(self					)						,  indent=2					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							if len(self.models					) <= 0:
													raise ValueError(
													    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
													    """ bert-base-cased` or `args.models = ['bert-base-cased']."""					)
							return self.models




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							if not self.multi_process:
													return False
							elif self.is_tpu:
													logger.info("""Multiprocessing is currently not possible on TPU."""					)
													return False
							else:
													return True




