



'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       Optional[int]								=						{
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Any								=						['PerceiverFeatureExtractor']
							lowerCAmelCase_     :       Any								=						['PerceiverImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Tuple								=						[
							    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PerceiverForImageClassificationConvProcessing',
							    'PerceiverForImageClassificationFourier',
							    'PerceiverForImageClassificationLearned',
							    'PerceiverForMaskedLM',
							    'PerceiverForMultimodalAutoencoding',
							    'PerceiverForOpticalFlow',
							    'PerceiverForSequenceClassification',
							    'PerceiverLayer',
							    'PerceiverModel',
							    'PerceiverPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
							from .tokenization_perceiver import PerceiverTokenizer

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_perceiver import PerceiverFeatureExtractor
														from .image_processing_perceiver import PerceiverImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_perceiver import (
														    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PerceiverForImageClassificationConvProcessing,
														    PerceiverForImageClassificationFourier,
														    PerceiverForImageClassificationLearned,
														    PerceiverForMaskedLM,
														    PerceiverForMultimodalAutoencoding,
														    PerceiverForOpticalFlow,
														    PerceiverForSequenceClassification,
														    PerceiverLayer,
														    PerceiverModel,
														    PerceiverPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_     :       Tuple								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''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 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				['image_processor', 'tokenizer']
						_lowerCAmelCase     :   Union[str, Any]	     =				'LayoutLMv3ImageProcessor'
						_lowerCAmelCase     :   List[Any]	     =				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      str=None				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			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					:      List[str]     					=			kwargs.pop('''feature_extractor'''				)

													SCREAMING_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__(lowerCAmelCase__				,    lowerCAmelCase__				)







						def __call__(  self   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,    lowerCAmelCase__   :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,    lowerCAmelCase__   :      Union[List[List[int]], List[List[List[int]]]] = None				,    lowerCAmelCase__   :      Optional[Union[List[int], List[List[int]]]] = None				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Union[bool, str, PaddingStrategy] = False				,    lowerCAmelCase__   :      Union[bool, str, TruncationStrategy] = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      int = 0				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Optional[Union[str, TensorType]] = None				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													# 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
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processor(images=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				)

													# second, apply the tokenizer
													if text is not None and self.image_processor.apply_ocr and text_pair is None:
																				if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			features['''words''']

													SCREAMING_SNAKE_CASE					:      Tuple     					=			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=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )

													# add pixel values
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			features.pop('''pixel_values'''				)
													if return_overflowing_tokens is True:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.get_overflowing_images(lowerCAmelCase__				,    encoded_inputs['''overflow_to_sample_mapping''']				)
													SCREAMING_SNAKE_CASE					:      Any     					=			images

													return encoded_inputs







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for sample_idx in overflow_to_sample_mapping:
																				images_with_overflow.append(images[sample_idx]				)

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

													return images_with_overflow







						def 					__lowercase    (  self   :      Any				,    *lowerCAmelCase__   :      Union[str, Any]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return self.tokenizer.batch_decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				,    *lowerCAmelCase__   :      Optional[Any]				,    **lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													return self.tokenizer.decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													return ["input_ids", "bbox", "attention_mask", "pixel_values"]







						@property
						def 					__lowercase    (  self   :      Union[str, 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 					__lowercase    (  self   :      List[Any]				):

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




