






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 				__UpperCAmelCase		(A__   ):
			__snake_case    :				Tuple									=							['image_processor', 'tokenizer']
			__snake_case    :				Dict									=							'AutoImageProcessor'
			__snake_case    :				Any									=							'AutoTokenizer'







			def __init__(     self:  Dict				,     UpperCAmelCase_:  List[str]=None				,     UpperCAmelCase_:  Tuple=None				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,     lowerCamelCase__				,     )
															_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""feature_extractor"""					)

									_SCREAMING_SNAKE_CASE     				=  image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""					)
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""					)

									super().__init__(lowerCamelCase__				,     lowerCamelCase__					)
									_SCREAMING_SNAKE_CASE     				=  self.image_processor
									_SCREAMING_SNAKE_CASE     				=  False







			def __call__(     self:  Optional[Any]				,     *UpperCAmelCase_:  List[str]				,     **UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									if self._in_target_context_manager:
															return self.current_processor(*lowerCamelCase__				,     **lowerCamelCase__					)

									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""images"""				,     lowerCamelCase__					)
									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""text"""				,     lowerCamelCase__					)
									if len(lowerCamelCase__					) > 0:
															_SCREAMING_SNAKE_CASE     				=  args[0]
															_SCREAMING_SNAKE_CASE     				=  args[1:]

									if images is None and text is None:
															raise ValueError("""You need to specify either an `images` or `text` input to process."""					)

									if images is not None:
															_SCREAMING_SNAKE_CASE     				=  self.image_processor(lowerCamelCase__				,     *lowerCamelCase__				,     **lowerCamelCase__					)
									if text is not None:
															_SCREAMING_SNAKE_CASE     				=  self.tokenizer(lowerCamelCase__				,     **lowerCamelCase__					)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															_SCREAMING_SNAKE_CASE     				=  encodings["""input_ids"""]
															return inputs







			def 							UpperCamelCase				(     self:  Dict				,     *UpperCAmelCase_:  List[str]				,     **UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									return self.tokenizer.batch_decode(*lowerCamelCase__				,     **lowerCamelCase__					)







			def 							UpperCamelCase				(     self:  List[str]				,     *UpperCAmelCase_:  List[str]				,     **UpperCAmelCase_:  int					):







									'''simple docstring'''




									return self.tokenizer.decode(*lowerCamelCase__				,     **lowerCamelCase__					)







			@contextmanager
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""					)
									_SCREAMING_SNAKE_CASE     				=  True
									_SCREAMING_SNAKE_CASE     				=  self.tokenizer
									yield
									_SCREAMING_SNAKE_CASE     				=  self.image_processor
									_SCREAMING_SNAKE_CASE     				=  False







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  int=False				,     UpperCAmelCase_:  Tuple=None					):







									'''simple docstring'''




									if added_vocab is None:
															_SCREAMING_SNAKE_CASE     				=  self.tokenizer.get_added_vocab()

									_SCREAMING_SNAKE_CASE     				=  {}

									while tokens:
															_SCREAMING_SNAKE_CASE     				=  re.search(R"""<s_(.*?)>"""				,     lowerCamelCase__				,     re.IGNORECASE					)
															if start_token is None:
																					break
															_SCREAMING_SNAKE_CASE     				=  start_token.group(1					)
															_SCREAMING_SNAKE_CASE     				=  re.search(RF'</s_{key}>'				,     lowerCamelCase__				,     re.IGNORECASE					)
															_SCREAMING_SNAKE_CASE     				=  start_token.group()
															if end_token is None:
																					_SCREAMING_SNAKE_CASE     				=  tokens.replace(lowerCamelCase__				,     """"""					)
															else:
																					_SCREAMING_SNAKE_CASE     				=  end_token.group()
																					_SCREAMING_SNAKE_CASE     				=  re.escape(lowerCamelCase__					)
																					_SCREAMING_SNAKE_CASE     				=  re.escape(lowerCamelCase__					)
																					_SCREAMING_SNAKE_CASE     				=  re.search(F'{start_token_escaped}(.*?){end_token_escaped}'				,     lowerCamelCase__				,     re.IGNORECASE					)
																					if content is not None:
																											_SCREAMING_SNAKE_CASE     				=  content.group(1					).strip()
																											if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																	_SCREAMING_SNAKE_CASE     				=  self.tokenajson(lowerCamelCase__				,     is_inner_value=lowerCamelCase__				,     added_vocab=lowerCamelCase__					)
																																	if value:
																																							if len(lowerCamelCase__					) == 1:
																																													_SCREAMING_SNAKE_CASE     				=  value[0]
																																							_SCREAMING_SNAKE_CASE     				=  value
																											else:  # leaf nodes
																																	_SCREAMING_SNAKE_CASE     				=  []
																																	for leaf in content.split(R"""<sep/>"""					):
																																							_SCREAMING_SNAKE_CASE     				=  leaf.strip()
																																							if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																													_SCREAMING_SNAKE_CASE     				=  leaf[1:-2]  # for categorical special tokens
																																							output[key].append(lowerCamelCase__					)
																																	if len(output[key]					) == 1:
																																							_SCREAMING_SNAKE_CASE     				=  output[key][0]

																					_SCREAMING_SNAKE_CASE     				=  tokens[tokens.find(lowerCamelCase__					) + len(lowerCamelCase__					) :].strip()
																					if tokens[:6] == r"<sep/>":  # non-leaf nodes
																											return [output] + self.tokenajson(tokens[6:]				,     is_inner_value=lowerCamelCase__				,     added_vocab=lowerCamelCase__					)

									if len(lowerCamelCase__					):
															return [output] if is_inner_value else output
									else:
															return [] if is_inner_value else {"text_sequence": tokens}







			@property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,     lowerCamelCase__				,     )
									return self.image_processor_class







			@property
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,     lowerCamelCase__				,     )
									return self.image_processor









from typing import TYPE_CHECKING

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


UpperCamelCase			    =     {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv3ForQuestionAnswering''',
		    '''LayoutLMv3ForSequenceClassification''',
		    '''LayoutLMv3ForTokenClassification''',
		    '''LayoutLMv3Model''',
		    '''LayoutLMv3PreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMv3ForQuestionAnswering''',
		    '''TFLayoutLMv3ForSequenceClassification''',
		    '''TFLayoutLMv3ForTokenClassification''',
		    '''TFLayoutLMv3Model''',
		    '''TFLayoutLMv3PreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3FeatureExtractor''']
		UpperCamelCase			    =     ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		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_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

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

else:
		import sys

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

