






'''simple docstring'''







import logging
import os

from .state import PartialState





class 						SCREAMING_SNAKE_CASE       (       logging.LoggerAdapter							):






			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      List[Any]       )      ->      Optional[Any]:
							a_		:	Union[str, Any]									=   PartialState()
							return not main_process_only or (main_process_only and state.is_main_process)







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

							if self.isEnabledFor(__SCREAMING_SNAKE_CASE       ):
											if self._should_log(__SCREAMING_SNAKE_CASE       ):
															a_					,      a_		:	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_		:	Tuple									=   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 		_UpperCAmelCase (				__A				: str    ,		__A				: str = None   ):
				if log_level is None:
								a_		:	Union[str, Any]									=   os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,		__A   )
				a_		:	Optional[int]									=   logging.getLogger(__A   )
				if log_level is not None:
								logger.setLevel(log_level.upper()   )
								logger.root.setLevel(log_level.upper()   )
				return MultiProcessAdapter(__A    ,		{}   )












'''simple docstring'''







from typing import TYPE_CHECKING

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


__lowerCAmelCase									=   {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   ['DeiTFeatureExtractor']
		__lowerCAmelCase									=   ['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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




