

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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	["""image_processor""", """tokenizer"""]
			__A    	=	"""LayoutLMv3ImageProcessor"""
			__A    	=	("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")


			def __init__(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					**__UpperCamelCase				):

				"""simple docstring"""


				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.'			,					__UpperCamelCase			,					)
					snake_case_   		=			kwargs.pop('feature_extractor'				)

				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__(__UpperCamelCase			,					__UpperCamelCase				)


			def __call__(     self			,					__UpperCamelCase			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = True			,					__UpperCamelCase = False			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = 0			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = False			,					__UpperCamelCase = False			,					__UpperCamelCase = False			,					__UpperCamelCase = False			,					__UpperCamelCase = True			,					__UpperCamelCase = None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				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_   		=			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_   		=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
					snake_case_   		=			features['words']

				snake_case_   		=			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_   		=			features.pop('pixel_values'				)
				if return_overflowing_tokens is True:
					snake_case_   		=			self.get_overflowing_images(__UpperCamelCase			,					encoded_inputs['overflow_to_sample_mapping']				)
				snake_case_   		=			images

				return encoded_inputs


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			[]
				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       __lowerCAmelCase			(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


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


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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



A									=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
