


import logging
import os

from .state import PartialState
class 			SCREAMING_SNAKE_CASE__  (    logging.LoggerAdapter      ):
      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(	SCREAMING_SNAKE_CASE__    :	Dict					)		->      Optional[Any]:
             a_ :		Any							=  PartialState()
             return not main_process_only or (main_process_only and state.is_main_process)

      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	str					,							*SCREAMING_SNAKE_CASE__    :	List[Any]					,							**SCREAMING_SNAKE_CASE__    :	Any					)		->      Optional[int]:
             if PartialState._shared_state == {}:
                    raise RuntimeError(
                        'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'					)
             a_ :		List[str]							=  kwargs.pop('main_process_only'					,							SCREAMING_SNAKE_CASE__					)
             a_ :		List[str]							=  kwargs.pop('in_order'					,							SCREAMING_SNAKE_CASE__					)

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

                    elif in_order:
                           a_ :		Dict							=  PartialState()
                           for i in range(state.num_processes					):
                                  if i == state.process_index:
                                         a_							,		a_ :		List[Any]							=  self.process(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                                         self.logger.log(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							*SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
                                  state.wait_for_everyone()







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str = None    )     ->		Any:

       """simple docstring"""

       if log_level is None:
              a_ :		Tuple							=  os.environ.get('ACCELERATE_LOG_LEVEL'  ,   __A    )
       a_ :		List[str]							=  logging.getLogger(__A    )
       if log_level is not None:
              logger.setLevel(log_level.upper()    )
              logger.root.setLevel(log_level.upper()    )
       return MultiProcessAdapter(__A  ,   {}    )






from typing import TYPE_CHECKING

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


UpperCAmelCase_				:			Any   =	{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['MobileBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileBertForMaskedLM',
       'MobileBertForMultipleChoice',
       'MobileBertForNextSentencePrediction',
       'MobileBertForPreTraining',
       'MobileBertForQuestionAnswering',
       'MobileBertForSequenceClassification',
       'MobileBertForTokenClassification',
       'MobileBertLayer',
       'MobileBertModel',
       'MobileBertPreTrainedModel',
       'load_tf_weights_in_mobilebert',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Tuple   =	[
       'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFMobileBertForMaskedLM',
       'TFMobileBertForMultipleChoice',
       'TFMobileBertForNextSentencePrediction',
       'TFMobileBertForPreTraining',
       'TFMobileBertForQuestionAnswering',
       'TFMobileBertForSequenceClassification',
       'TFMobileBertForTokenClassification',
       'TFMobileBertMainLayer',
       'TFMobileBertModel',
       'TFMobileBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_mobilebert import (
       MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileBertConfig,
       MobileBertOnnxConfig,
   )
   from .tokenization_mobilebert import MobileBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mobilebert_fast import MobileBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilebert import (
          MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertLayer,
          MobileBertModel,
          MobileBertPreTrainedModel,
          load_tf_weights_in_mobilebert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilebert import (
          TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertMainLayer,
          TFMobileBertModel,
          TFMobileBertPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase_				:			Tuple   =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


