



import logging
import os

from .state import PartialState



class 						lowerCamelCase   (    logging.LoggerAdapter	):







					'''simple docstring'''







					@staticmethod
					def  A__		(      lowerCAmelCase  ):
							UpperCAmelCase_						       =					PartialState()
							return not main_process_only or (main_process_only and state.is_main_process)



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						*lowerCAmelCase     ,						**lowerCAmelCase  ):
							if PartialState._shared_state == {}:
									raise RuntimeError(
									    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."  )
							UpperCAmelCase_						       =					kwargs.pop("main_process_only"     ,						__lowerCamelCase  )
							UpperCAmelCase_						       =					kwargs.pop("in_order"     ,						__lowerCamelCase  )

							if self.isEnabledFor(__lowerCamelCase  ):
									if self._should_log(__lowerCamelCase  ):
											UpperCAmelCase_       ,    UpperCAmelCase_						       =					self.process(__lowerCamelCase     ,						__lowerCamelCase  )
											self.logger.log(__lowerCamelCase     ,						__lowerCamelCase     ,						*__lowerCamelCase     ,						**__lowerCamelCase  )

									elif in_order:
											UpperCAmelCase_						       =					PartialState()
											for i in range(state.num_processes  ):
													if i == state.process_index:
															UpperCAmelCase_       ,    UpperCAmelCase_						       =					self.process(__lowerCamelCase     ,						__lowerCamelCase  )
															self.logger.log(__lowerCamelCase     ,						__lowerCamelCase     ,						*__lowerCamelCase     ,						**__lowerCamelCase  )
													state.wait_for_everyone()







def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE = None			)		->				int:
		if log_level is None:
				UpperCAmelCase_						       =					os.environ.get("ACCELERATE_LOG_LEVEL"      ,  A__			)
		UpperCAmelCase_						       =					logging.getLogger(A__			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(A__      ,  {}			)




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__A			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d