


import logging
import os

from .state import PartialState

class       __magic_name__	(     logging.LoggerAdapter):


     @staticmethod
     def      UpperCAmelCase__	(      lowerCamelCase__							:     int			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   PartialState()
      return not main_process_only or (main_process_only and state.is_main_process)


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Union[str, Any]							,	*lowerCamelCase__							:     List[str]							,	**lowerCamelCase__							:     Dict			)  ->      Tuple:





      '''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.'''			)
      UpperCamelCase__					:    List[Any]          =   kwargs.pop('''main_process_only'''							,	lowerCamelCase__			)
      UpperCamelCase__					:    Union[str, Any]          =   kwargs.pop('''in_order'''							,	lowerCamelCase__			)

      if self.isEnabledFor(lowerCamelCase__			):
       if self._should_log(lowerCamelCase__			):
        UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   self.process(lowerCamelCase__							,	lowerCamelCase__			)
        self.logger.log(lowerCamelCase__							,	lowerCamelCase__							,	*lowerCamelCase__							,	**lowerCamelCase__			)

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



def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str = None     ):







 """simple docstring"""



 if log_level is None:
  UpperCamelCase__					:    Tuple          =   os.environ.get('''ACCELERATE_LOG_LEVEL'''    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Union[str, Any]          =   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    , {}     )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__UpperCamelCase       :       Dict						       =				{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       __UpperCamelCase       :       Tuple						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


