






import logging
import os

from .state import PartialState


class 	__SCREAMING_SNAKE_CASE(						logging.LoggerAdapter						):
							@staticmethod
							def lowerCAmelCase_			(  UpperCamelCase:   Union[str, Any]    )		->		Optional[Any]:
											snake_case__						=  PartialState()
											return not main_process_only or (main_process_only and state.is_main_process)


							def lowerCAmelCase_			(  self:   Union[str, Any]							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Optional[Any]							,						*UpperCamelCase:   Dict							,						**UpperCamelCase:   List[str]    )		->		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.'    )
											snake_case__						=  kwargs.pop('main_process_only'							,						lowerCamelCase__    )
											snake_case__						=  kwargs.pop('in_order'							,						lowerCamelCase__    )

											if self.isEnabledFor(lowerCamelCase__    ):
															if self._should_log(lowerCamelCase__    ):
																			snake_case__						=  self.process(lowerCamelCase__							,						lowerCamelCase__    )
																			self.logger.log(lowerCamelCase__							,						lowerCamelCase__							,						*lowerCamelCase__							,						**lowerCamelCase__    )

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


def   a_			(   _A   , _A = None	)      ->      Optional[int]:







				"""simple docstring"""
				if log_level is None:
								snake_case__						=  os.environ.get('ACCELERATE_LOG_LEVEL'   , UpperCAmelCase_	)
				snake_case__						=  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_tf_available, is_torch_available


snake_case_					:      Union[str, Any]    			=    {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'AdaptiveEmbedding',
					    'TransfoXLForSequenceClassification',
					    'TransfoXLLMHeadModel',
					    'TransfoXLModel',
					    'TransfoXLPreTrainedModel',
					    'load_tf_weights_in_transfo_xl',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Any    			=    [
					    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFAdaptiveEmbedding',
					    'TFTransfoXLForSequenceClassification',
					    'TFTransfoXLLMHeadModel',
					    'TFTransfoXLMainLayer',
					    'TFTransfoXLModel',
					    'TFTransfoXLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
					from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_transfo_xl import (
										    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AdaptiveEmbedding,
										    TransfoXLForSequenceClassification,
										    TransfoXLLMHeadModel,
										    TransfoXLModel,
										    TransfoXLPreTrainedModel,
										    load_tf_weights_in_transfo_xl,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_transfo_xl import (
										    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAdaptiveEmbedding,
										    TFTransfoXLForSequenceClassification,
										    TFTransfoXLLMHeadModel,
										    TFTransfoXLMainLayer,
										    TFTransfoXLModel,
										    TFTransfoXLPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      Dict    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


