


'''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     __SCREAMING_SNAKE_CASE       (   _lowerCamelCase			):
     UpperCAmelCase			       =					['''image_processor''', '''tokenizer''']
     UpperCAmelCase			       =					'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase			       =					('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






     def __init__(      self  ,				__UpperCamelCase=None  ,				__UpperCamelCase=None  ,				**__UpperCamelCase		)							->     Any:
          _a            =		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  ,				)
               _a            =		kwargs.pop("feature_extractor"		)

          _a            =		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  ,				__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  ,				)							->     Optional[Any]:
          # 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
          _a            =		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		):
                    _a            =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
               _a            =		features['words']

          _a            =		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
          _a            =		features.pop("pixel_values"		)
          if return_overflowing_tokens is True:
               _a            =		self.get_overflowing_images(_lowerCamelCase  ,				encoded_inputs["overflow_to_sample_mapping"]		)
          _a            =		images

          return encoded_inputs






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Dict:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _a            =		[]
          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 			a_       (      self  ,				*__UpperCamelCase  ,				**__UpperCamelCase		)							->     Optional[Any]:
          return self.tokenizer.batch_decode(*_lowerCamelCase  ,				**_lowerCamelCase		)






     def 			a_       (      self  ,				*__UpperCamelCase  ,				**__UpperCamelCase		)							->     Tuple:
          return self.tokenizer.decode(*_lowerCamelCase  ,				**_lowerCamelCase		)






     @property
     def 			a_       (      self		)							->     Optional[Any]:
          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






     @property
     def 			a_       (      self		)							->     int:
          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 			a_       (      self		)							->     List[str]:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,				_lowerCamelCase  ,				)
          return self.image_processor


'''simple docstring'''
from .prime_check import Test

Test()



