

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_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''image_processor''', '''tokenizer''']
 _lowerCAmelCase									=	'''LayoutLMv2ImageProcessor'''
 _lowerCAmelCase									=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')




 def __init__(			self  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  **_UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     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  ,  )
     __UpperCAmelCase	:  Optional[int]		=     kwargs.pop('feature_extractor'							)

   __UpperCAmelCase	:  Dict		=     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 = 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
   __UpperCAmelCase	:  List[Any]		=     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							):
       __UpperCAmelCase	:  Dict		=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
     __UpperCAmelCase	:  Optional[int]		=     features['words']

   __UpperCAmelCase	:  Union[str, Any]		=     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
   __UpperCAmelCase	:  int		=     features.pop('pixel_values'							)
   if return_overflowing_tokens is True:
     __UpperCAmelCase	:  str		=     self.get_overflowing_images(_UpperCAmelCase  ,  encoded_inputs['overflow_to_sample_mapping']							)
   __UpperCAmelCase	:  Dict		=     images

   return encoded_inputs




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   __UpperCAmelCase	:  Optional[Any]		=     []
   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  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.batch_decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




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




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








from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    np.stack((horizontal_flow, vertical_flow)			,							2						)

    # Create a grid of all pixel coordinates and subtract the flow to get the
    # target pixels coordinates
    SCREAMING_SNAKE_CASE__         =    np.stack(
        np.meshgrid(np.arange(0			,							image.shape[1]						)			,							np.arange(0			,							image.shape[0]						)						)			,							2						)
    SCREAMING_SNAKE_CASE__         =    np.round(grid - flow						).astype(np.intaa						)

    # Find the locations outside of the original image
    SCREAMING_SNAKE_CASE__         =    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]						))
    SCREAMING_SNAKE_CASE__         =    0

    SCREAMING_SNAKE_CASE__         =    image[grid[:, :, 1], grid[:, :, 0]]

    # Set pixels at invalid locations to 0
    SCREAMING_SNAKE_CASE__         =    0

    return warped





def 				__a							(       __A			,							__A			,							__A			,							__A = None			,							):
    if alpha is None:
        SCREAMING_SNAKE_CASE__         =    0.1

    # Initialize flow
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)
    SCREAMING_SNAKE_CASE__         =    np.zeros_like(__A						)

    # Prepare kernels for the calculation of the derivatives and the average velocity
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, 1], [-1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[-1, -1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array([[1, 1], [1, 1]]						) * 0.25
    SCREAMING_SNAKE_CASE__         =    np.array(
        [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]						)

    # Iteratively refine the flow
    for _ in range(__A						):
        SCREAMING_SNAKE_CASE__         =    warp(__A			,							__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						) + convolve(__A			,							-kernel_t						)

        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)
        SCREAMING_SNAKE_CASE__         =    convolve(__A			,							__A						)

        # This updates the flow as proposed in the paper (Step 12)
        SCREAMING_SNAKE_CASE__         =    (
            derivative_x * avg_horizontal_velocity
            + derivative_y * avg_vertical_velocity
            + derivative_t
        )
        SCREAMING_SNAKE_CASE__         =    update / (alpha**2 + derivative_x**2 + derivative_y**2)

        SCREAMING_SNAKE_CASE__         =    avg_horizontal_velocity - derivative_x * update
        SCREAMING_SNAKE_CASE__         =    avg_vertical_velocity - derivative_y * update

    return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()


