



'''simple docstring'''
import logging
import os

from .state import PartialState






class 				lowercase__      (				logging.LoggerAdapter  ):





	@staticmethod
	def       UpperCamelCase_    (					lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  PartialState()
							return not main_process_only or (main_process_only and state.is_main_process)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[Any]					,*lowerCamelCase__   :							Dict					,**lowerCamelCase__   :							List[str]					):







							'''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.'					)
							_UpperCamelCase   :						Dict												=  kwargs.pop('main_process_only'					,lowerCamelCase__					)
							_UpperCamelCase   :						Tuple												=  kwargs.pop('in_order'					,lowerCamelCase__					)

							if self.isEnabledFor(lowerCamelCase__					):
													if self._should_log(lowerCamelCase__					):
																			_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  self.process(lowerCamelCase__					,lowerCamelCase__					)
																			self.logger.log(lowerCamelCase__					,lowerCamelCase__					,*lowerCamelCase__					,**lowerCamelCase__					)

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






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_ = None				):
						if log_level is None:
												_UpperCamelCase   :						Any												=  os.environ.get('ACCELERATE_LOG_LEVEL'    ,    UpperCAmelCase_				)
						_UpperCamelCase   :						Any												=  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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_					:      Tuple    			=    {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      int    			=    ['LayoutLMv2TokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Union[str, Any]    			=    ['LayoutLMv2FeatureExtractor']
					snake_case_					:      str    			=    ['LayoutLMv2ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Optional[int]    			=    [
					    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMv2ForQuestionAnswering',
					    'LayoutLMv2ForSequenceClassification',
					    'LayoutLMv2ForTokenClassification',
					    'LayoutLMv2Layer',
					    'LayoutLMv2Model',
					    'LayoutLMv2PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaLayer,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)
else:
					import sys

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


