
import logging
import os

from .state import PartialState

class __snake_case		(      logging.LoggerAdapter     ):
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      PartialState()
           return not main_process_only or (main_process_only and state.is_main_process)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""      )
           lowercase							:     Any       	=      kwargs.pop("""main_process_only"""		,snake_case      )
           lowercase							:     Any       	=      kwargs.pop("""in_order"""		,snake_case      )

           if self.isEnabledFor(snake_case      ):
                  if self._should_log(snake_case      ):
                         lowercase	,			lowercase							:     Optional[int]       	=      self.process(snake_case		,snake_case      )
                         self.logger.log(snake_case		,snake_case		,*snake_case		,**snake_case      )

                  elif in_order:
                         lowercase							:     Any       	=      PartialState()
                         for i in range(state.num_processes      ):
                                if i == state.process_index:
                                       lowercase	,			lowercase							:     Union[str, Any]       	=      self.process(snake_case		,snake_case      )
                                       self.logger.log(snake_case		,snake_case		,*snake_case		,**snake_case      )
                                state.wait_for_everyone()







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None				) ->    Dict:
       if log_level is None:
              lowercase							:     Dict       	=      os.environ.get("""ACCELERATE_LOG_LEVEL"""					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     int       	=      logging.getLogger(SCREAMING_SNAKE_CASE__				)
       if log_level is not None:
              logger.setLevel(log_level.upper()				)
              logger.root.setLevel(log_level.upper()				)
       return MultiProcessAdapter(SCREAMING_SNAKE_CASE__					,	{}				)







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE_						   =       logging.getLogger(__name__)

@dataclass
class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[float]			=			field(
         default=0.0							,      metadata={"help": "The label smoothing epsilon to apply (if not zero)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to SortishSamler or not."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "whether to use adafactor"}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Encoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Decoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(default=lowerCAmelCase_							,      metadata={"help": "Dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Attention dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[str]			=			field(
         default="linear"							,      metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}"}							,      )


