


'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		int   		=  ['image_processor', 'tokenizer']
							lowercase			:		List[str]   		=  'LayoutLMv3ImageProcessor'
							lowercase			:		Any   		=  ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')





							def __init__(  self		:List[str]  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:int=None  ,**_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							Optional[Any]			       =						None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""  ,_UpperCamelCase  ,)
											snake_case_		:							Union[str, Any]			       =						kwargs.pop("""feature_extractor"""			)

									snake_case_		:							Any			       =						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__(_UpperCamelCase  ,_UpperCamelCase			)





							def __call__(  self		:Any  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,_UpperCamelCase		:Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,_UpperCamelCase		:Union[List[List[int]], List[List[List[int]]]] = None  ,_UpperCamelCase		:Optional[Union[List[int], List[List[int]]]] = None  ,_UpperCamelCase		:bool = True  ,_UpperCamelCase		:Union[bool, str, PaddingStrategy] = False  ,_UpperCamelCase		:Union[bool, str, TruncationStrategy] = None  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:int = 0  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[bool] = None  ,_UpperCamelCase		:Optional[bool] = None  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = True  ,_UpperCamelCase		:Optional[Union[str, TensorType]] = None  ,**_UpperCamelCase		:int  ,):
									# verify input
									if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""			)

									if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""			)

									# first, apply the image processor
									snake_case_		:							Tuple			       =						self.image_processor(images=_UpperCamelCase  ,return_tensors=_UpperCamelCase			)

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(_UpperCamelCase  ,_UpperCamelCase			):
													snake_case_		:							int			       =						[text]  # add batch dimension (as the image processor always adds a batch dimension)
											snake_case_		:							str			       =						features["""words"""]

									snake_case_		:							Optional[int]			       =						self.tokenizer(
									    text=text if text is not None else features["""words"""]  ,text_pair=text_pair if text_pair is not None else None  ,boxes=boxes if boxes is not None else features["""boxes"""]  ,word_labels=_UpperCamelCase  ,add_special_tokens=_UpperCamelCase  ,padding=_UpperCamelCase  ,truncation=_UpperCamelCase  ,max_length=_UpperCamelCase  ,stride=_UpperCamelCase  ,pad_to_multiple_of=_UpperCamelCase  ,return_token_type_ids=_UpperCamelCase  ,return_attention_mask=_UpperCamelCase  ,return_overflowing_tokens=_UpperCamelCase  ,return_special_tokens_mask=_UpperCamelCase  ,return_offsets_mapping=_UpperCamelCase  ,return_length=_UpperCamelCase  ,verbose=_UpperCamelCase  ,return_tensors=_UpperCamelCase  ,**_UpperCamelCase  ,)

									# add pixel values
									snake_case_		:							Any			       =						features.pop("""pixel_values"""			)
									if return_overflowing_tokens is True:
											snake_case_		:							List[Any]			       =						self.get_overflowing_images(_UpperCamelCase  ,encoded_inputs["""overflow_to_sample_mapping"""]			)
									snake_case_		:							str			       =						images

									return encoded_inputs





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:List[str]			):
									# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
									snake_case_		:							int			       =						[]
									for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]			)

									if len(_UpperCamelCase			) != len(_UpperCamelCase			):
											raise ValueError(
											    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
											    F''' {len(_UpperCamelCase			)} and {len(_UpperCamelCase			)}'''			)

									return images_with_overflow





							def 		a__   (  self		:List[str]  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:str			):
									return self.tokenizer.batch_decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]  ,*_UpperCamelCase		:Optional[Any]  ,**_UpperCamelCase		:Union[str, Any]			):
									return self.tokenizer.decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							@property
							def 		a__   (  self		:List[str]			):
									return ["input_ids", "bbox", "attention_mask", "pixel_values"]





							@property
							def 		a__   (  self		:Union[str, Any]			):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,_UpperCamelCase  ,)
									return self.image_processor_class





							@property
							def 		a__   (  self		:int			):
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,_UpperCamelCase  ,)
									return self.image_processor



'''simple docstring'''






from typing import TYPE_CHECKING

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


__A	:	Tuple     =     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A	:	Tuple     =     [
							    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ASTForAudioClassification',
							    'ASTModel',
							    'ASTPreTrainedModel',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A	:	int     =     ['ASTFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							__A	:	Union[str, Any]     =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)