




'''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_				(			_lowercase				):




					'''simple docstring'''






					lowercase__       =					['''image_processor''', '''tokenizer''']
					lowercase__       =					'''LayoutLMv2ImageProcessor'''
					lowercase__       =					('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






					def __init__(       self,    __UpperCamelCase=None,    __UpperCamelCase=None,    **__UpperCamelCase					):
							_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.",    _SCREAMING_SNAKE_CASE,    )
									_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__(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					)






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

							if return_overflowing_tokens is True and return_offsets_mapping is False:
									raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."					)

							# first, apply the image processor
							_snake_case      			=			self.image_processor(images=_SCREAMING_SNAKE_CASE,    return_tensors=_SCREAMING_SNAKE_CASE					)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE					):
											_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=_SCREAMING_SNAKE_CASE,    add_special_tokens=_SCREAMING_SNAKE_CASE,    padding=_SCREAMING_SNAKE_CASE,    truncation=_SCREAMING_SNAKE_CASE,    max_length=_SCREAMING_SNAKE_CASE,    stride=_SCREAMING_SNAKE_CASE,    pad_to_multiple_of=_SCREAMING_SNAKE_CASE,    return_token_type_ids=_SCREAMING_SNAKE_CASE,    return_attention_mask=_SCREAMING_SNAKE_CASE,    return_overflowing_tokens=_SCREAMING_SNAKE_CASE,    return_special_tokens_mask=_SCREAMING_SNAKE_CASE,    return_offsets_mapping=_SCREAMING_SNAKE_CASE,    return_length=_SCREAMING_SNAKE_CASE,    verbose=_SCREAMING_SNAKE_CASE,    return_tensors=_SCREAMING_SNAKE_CASE,    **_SCREAMING_SNAKE_CASE,    )

							# add pixel values
							_snake_case      			=			features.pop("pixel_values"					)
							if return_overflowing_tokens is True:
									_snake_case      			=			self.get_overflowing_images(_SCREAMING_SNAKE_CASE,    encoded_inputs["overflow_to_sample_mapping"]					)
							_snake_case      			=			images

							return encoded_inputs






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							_snake_case      			=			[]
							for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]					)

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

							return images_with_overflow






					def 							__SCREAMING_SNAKE_CASE      (       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE,    **_SCREAMING_SNAKE_CASE					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE,    **_SCREAMING_SNAKE_CASE					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",    _SCREAMING_SNAKE_CASE,    )
							return self.image_processor_class






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









import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


lowerCamelCase		=						logging.get_logger(__name__)





class    _a				(							_lowercase):




	def __init__(   self		:						Any						,   *_SCREAMING_SNAKE_CASE		:						int						,   **_SCREAMING_SNAKE_CASE		:						Dict						)->			None:
			warnings.warn(
			    '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
			    ''' Please use MaskFormerImageProcessor instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			super().__init__(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




