





import logging
import os

from .state import PartialState


class   A      (logging.LoggerAdapter   ):
			'''simple docstring'''




			@staticmethod
			def        a_	(      __lowerCAmelCase       :   Tuple       )     ->     Dict:







					"""simple docstring"""






					A__  				=       PartialState()
					return not main_process_only or (main_process_only and state.is_main_process)







			def        a_	(      self       :   str							,     __lowerCAmelCase       :   Optional[Any]							,     __lowerCAmelCase       :   Optional[Any]							,     *__lowerCAmelCase       :   Tuple							,     **__lowerCAmelCase       :   Any       )     ->     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__  				=       kwargs.pop("""main_process_only"""							,     __lowerCAmelCase       )
					A__  				=       kwargs.pop("""in_order"""							,     __lowerCAmelCase       )

					if self.isEnabledFor(__lowerCAmelCase       ):
							if self._should_log(__lowerCAmelCase       ):
									A__				,      A__  				=       self.process(__lowerCAmelCase							,     __lowerCAmelCase       )
									self.logger.log(__lowerCAmelCase							,     __lowerCAmelCase							,     *__lowerCAmelCase							,     **__lowerCAmelCase       )

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







def        __lowerCamelCase (							__a       :str   ,					__a       :str = None   )		->    int:






		"""simple docstring"""

		if log_level is None:
				A__  				=       os.environ.get("""ACCELERATE_LOG_LEVEL"""   ,					__a   )
		A__  				=       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_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Optional[Any]			=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Any			=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					A   : str			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



