

"""simple docstring"""




import logging
import os

from .state import PartialState







class      __lowercase			(    logging.LoggerAdapter					):

  @staticmethod
  def    __lowercase       (  A		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					PartialState()
   return not main_process_only or (main_process_only and state.is_main_process)




  def    __lowercase       (  self		:			Optional[int]					,A		:			Tuple					,A		:			Union[str, Any]					,*A		:			Optional[int]					,**A		:			str    ):






   '''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__					:  str					   =					kwargs.pop("""main_process_only"""					,A    )
   UpperCAmelCase__					:  Dict					   =					kwargs.pop("""in_order"""					,A    )

   if self.isEnabledFor(A    ):
    if self._should_log(A    ):
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					self.process(A					,A    )
     self.logger.log(A					,A					,*A					,**A    )

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







def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase = None      ):
 '''simple docstring'''





 if log_level is None:
  UpperCAmelCase__					:  Dict					   =					os.environ.get("""ACCELERATE_LOG_LEVEL"""							,    __UpperCamelCase      )
 UpperCAmelCase__					:  Dict					   =					logging.getLogger(__UpperCamelCase      )
 if log_level is not None:
  logger.setLevel(log_level.upper()      )
  logger.root.setLevel(log_level.upper()      )
 return MultiProcessAdapter(__UpperCamelCase							,    {}      )








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase =				{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				['RobertaTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RobertaForCausalLM',
          'RobertaForMaskedLM',
          'RobertaForMultipleChoice',
          'RobertaForQuestionAnswering',
          'RobertaForSequenceClassification',
          'RobertaForTokenClassification',
          'RobertaModel',
          'RobertaPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRobertaForCausalLM',
          'TFRobertaForMaskedLM',
          'TFRobertaForMultipleChoice',
          'TFRobertaForQuestionAnswering',
          'TFRobertaForSequenceClassification',
          'TFRobertaForTokenClassification',
          'TFRobertaMainLayer',
          'TFRobertaModel',
          'TFRobertaPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'FlaxRobertaForCausalLM',
          'FlaxRobertaForMaskedLM',
          'FlaxRobertaForMultipleChoice',
          'FlaxRobertaForQuestionAnswering',
          'FlaxRobertaForSequenceClassification',
          'FlaxRobertaForTokenClassification',
          'FlaxRobertaModel',
          'FlaxRobertaPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





