




'''simple docstring'''



import logging
import os

from .state import PartialState


class 		_snake_case					(     logging.LoggerAdapter	):




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     PartialState()
								return not main_process_only or (main_process_only and state.is_main_process)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase):
								if PartialState._shared_state == {}:
											raise RuntimeError(
											    """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.""")
								UpperCAmelCase__  :					Optional[Any]        =     kwargs.pop("""main_process_only"""							,							A_)
								UpperCAmelCase__  :					Union[str, Any]        =     kwargs.pop("""in_order"""							,							A_)

								if self.isEnabledFor(A_):
											if self._should_log(A_):
														UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     self.process(A_							,							A_)
														self.logger.log(A_							,							A_							,							*A_							,							**A_)

											elif in_order:
														UpperCAmelCase__  :					str        =     PartialState()
														for i in range(state.num_processes):
																	if i == state.process_index:
																				UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     self.process(A_							,							A_)
																				self.logger.log(A_							,							A_							,							*A_							,							**A_)
																	state.wait_for_everyone()







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = None					):
			if log_level is None:
						UpperCAmelCase__  :					str        =     os.environ.get("""ACCELERATE_LOG_LEVEL""" ,      snake_case__					)
			UpperCAmelCase__  :					List[Any]        =     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__ ,      {}					)



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

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