
'''simple docstring'''




import logging
import os

from .state import PartialState





class  A				(							logging.LoggerAdapter						):





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         A       :   List[str]					=						PartialState()
         return not main_process_only or (main_process_only and state.is_main_process)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """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.'''	)
         A       :   Dict					=						kwargs.pop('''main_process_only'''						,			SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						kwargs.pop('''in_order'''						,			SCREAMING_SNAKE_CASE	)

         if self.isEnabledFor(SCREAMING_SNAKE_CASE	):
             if self._should_log(SCREAMING_SNAKE_CASE	):
                 A,  A       :   Optional[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       :   str					=						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 		lowerCAmelCase_				(   snake_case__		,				snake_case__ = None						):




    '''simple docstring'''



    if log_level is None:
        A       :   str					=						os.environ.get('''ACCELERATE_LOG_LEVEL'''		,				snake_case__						)
    A       :   Dict					=						logging.getLogger(snake_case__						)
    if log_level is not None:
        logger.setLevel(log_level.upper()						)
        logger.root.setLevel(log_level.upper()						)
    return MultiProcessAdapter(snake_case__		,				{}						)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase       =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TapasForMaskedLM',
			    'TapasForQuestionAnswering',
			    'TapasForSequenceClassification',
			    'TapasModel',
			    'TapasPreTrainedModel',
			    'load_tf_weights_in_tapas',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFTapasForMaskedLM',
			    'TFTapasForQuestionAnswering',
			    'TFTapasForSequenceClassification',
			    'TFTapasModel',
			    'TFTapasPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
			from .tokenization_tapas import TapasTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tapas import (
						    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TapasForMaskedLM,
						    TapasForQuestionAnswering,
						    TapasForSequenceClassification,
						    TapasModel,
						    TapasPreTrainedModel,
						    load_tf_weights_in_tapas,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_tapas import (
						    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTapasForMaskedLM,
						    TFTapasForQuestionAnswering,
						    TFTapasForSequenceClassification,
						    TFTapasModel,
						    TFTapasPreTrainedModel,
						)


else:
			import sys

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


