





"""simple docstring"""




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

from ..utils import logging


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)



def        __lowercase       (				_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None     )			->       Union[str, Any]:


      '''simple docstring'''


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



@dataclass
class 		UpperCamelCase__			:



     '''simple docstring'''

     __snake_case   :     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"
             )
         }							,				)

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

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

     __snake_case   :     bool		     =      field(
         default=lowerCAmelCase_							,				metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,				)
     __snake_case   :     bool		     =      field(
         default=lowerCAmelCase_							,				metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,				)
     __snake_case   :     bool		     =      field(
         default=lowerCAmelCase_							,				metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}       )
     __snake_case   :     bool		     =      field(default=lowerCAmelCase_							,				metadata={"help": "Use FP16 to accelerate inference."}       )
     __snake_case   :     bool		     =      field(default=lowerCAmelCase_							,				metadata={"help": "Benchmark training of model"}       )
     __snake_case   :     bool		     =      field(default=lowerCAmelCase_							,				metadata={"help": "Verbose memory tracing"}       )
     __snake_case   :     bool		     =      field(
         default=lowerCAmelCase_							,				metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,				)
     __snake_case   :     bool		     =      field(
         default=lowerCAmelCase_							,				metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,				)
     __snake_case   :     bool		     =      field(default=lowerCAmelCase_							,				metadata={"help": "Trace memory line by line"}       )
     __snake_case   :     bool		     =      field(default=lowerCAmelCase_							,				metadata={"help": "Save result to a CSV file"}       )
     __snake_case   :     bool		     =      field(default=lowerCAmelCase_							,				metadata={"help": "Save all print statements in a log file"}       )
     __snake_case   :     bool		     =      field(default=lowerCAmelCase_							,				metadata={"help": "Whether to print environment information"}       )
     __snake_case   :     bool		     =      field(
         default=lowerCAmelCase_							,				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."
             )
         }							,				)
     __snake_case   :     str		     =      field(
         default=F"inference_time_{round(time()       )}.csv"							,				metadata={"help": "CSV filename used if saving time results to csv."}							,				)
     __snake_case   :     str		     =      field(
         default=F"inference_memory_{round(time()       )}.csv"							,				metadata={"help": "CSV filename used if saving memory results to csv."}							,				)
     __snake_case   :     str		     =      field(
         default=F"train_time_{round(time()       )}.csv"							,				metadata={"help": "CSV filename used if saving time results to csv for training."}							,				)
     __snake_case   :     str		     =      field(
         default=F"train_memory_{round(time()       )}.csv"							,				metadata={"help": "CSV filename used if saving memory results to csv for training."}							,				)
     __snake_case   :     str		     =      field(
         default=F"env_info_{round(time()       )}.csv"							,				metadata={"help": "CSV filename used if saving environment information."}							,				)
     __snake_case   :     str		     =      field(
         default=F"log_{round(time()       )}.csv"							,				metadata={"help": "Log filename used if print statements are saved in log."}							,				)
     __snake_case   :     int		     =      field(default=3							,				metadata={"help": "Times an experiment will be run."}       )
     __snake_case   :     bool		     =      field(
         default=lowerCAmelCase_							,				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]	)					->		Dict:




           '''simple docstring'''


           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."""						,lowerCamelCase__						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Optional[Any]:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self	)						,indent=2	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		List[str]:




           '''simple docstring'''


           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						:   Tuple	)					->		Dict:




           '''simple docstring'''


           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









import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict

@dataclass
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      ComputeEnvironment.AMAZON_SAGEMAKER
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     Optional[int]		     =      "ml.p3.2xlarge"
     __snake_case   :     List[str]		     =      "accelerate_sagemaker_execution_role"
     __snake_case   :     Tuple		     =      "hf-sm"
     __snake_case   :     Any		     =      "us-east-1"
     __snake_case   :     Union[str, Any]		     =      1
     __snake_case   :     Dict		     =      "accelerate-sagemaker-1"
     __snake_case   :     Tuple		     =      "1.6"
     __snake_case   :     List[str]		     =      "4.4"
     __snake_case   :     str		     =      "train.py"
     __snake_case   :     List[str]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "False",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]
     __snake_case   :     Optional[int]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "--do_test",
         "False",
         "--do_predict",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
           assert isinstance(converted_args["""model_name_or_path"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""do_train"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""epochs"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""learning_rate"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""max_steps"""]						,lowerCamelCase__	)

           with pytest.raises(lowerCamelCase__	):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)


