
'''simple docstring'''
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase :  Dict      =      logging.get_logger(__name__)


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				["""input_ids""", """attention_mask"""]







				def __init__(    self		,							A_="[PAD]"		,							A_="[BOS]"		,							A_="[EOS]"		,							A_="[MASK]"		,							A_="[CLS]"		,							A_="[SEP]"		,							A_=2048		,							**A_		,							)->						None:







											'''simple docstring'''






											UpperCamelCase					       =			AddedToken(A_		,							lstrip=A_		,							rstrip=A_			) if isinstance(A_		,							A_			) else pad_token
											UpperCamelCase					       =			AddedToken(A_		,							lstrip=A_		,							rstrip=A_			) if isinstance(A_		,							A_			) else bos_token
											UpperCamelCase					       =			AddedToken(A_		,							lstrip=A_		,							rstrip=A_			) if isinstance(A_		,							A_			) else eos_token
											UpperCamelCase					       =			AddedToken(A_		,							lstrip=A_		,							rstrip=A_			) if isinstance(A_		,							A_			) else mask_token
											UpperCamelCase					       =			AddedToken(A_		,							lstrip=A_		,							rstrip=A_			) if isinstance(A_		,							A_			) else cls_token
											UpperCamelCase					       =			AddedToken(A_		,							lstrip=A_		,							rstrip=A_			) if isinstance(A_		,							A_			) else sep_token

											super().__init__(
											    pad_token=A_		,							bos_token=A_		,							eos_token=A_		,							mask_token=A_		,							cls_token=A_		,							sep_token=A_		,							model_max_length=A_		,							**A_		,							)

											UpperCamelCase					       =			2**8  # utf is 8 bits

											# define special tokens dict
											UpperCamelCase					       =			{
											    self.pad_token: 0,
											    self.bos_token: 1,
											    self.eos_token: 2,
											    self.mask_token: 3,
											    self.cls_token: 4,
											    self.sep_token: 5,
											}
											UpperCamelCase					       =			len(self.special_tokens_encoder			)
											UpperCamelCase					       =			{v: k for k, v in self.special_tokens_encoder.items()}







				def 		UpperCAmelCase_  (    self			)->						Dict[str, int]:







											'''simple docstring'''






											UpperCamelCase					       =			self.special_tokens_encoder.copy()
											vocab.update(self.added_tokens_encoder			)
											for i in range(self._utf_vocab_size			):
																		UpperCamelCase					       =			chr(A_			)
																		UpperCamelCase					       =			i + len(self.special_tokens_encoder			)
											return vocab







				@property
				def 		UpperCAmelCase_  (    self			)->						Optional[Any]:







											'''simple docstring'''






											return self._utf_vocab_size + self._num_special_tokens







				def 		UpperCAmelCase_  (    self		,							A_		,							A_ = None		,							A_ = False			)->						List[int]:







											'''simple docstring'''






											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=A_		,							token_ids_a=A_		,							already_has_special_tokens=A_			)

											# normal case: some special tokens
											if token_ids_a is None:
																		return [1] + [0] * len(A_			) + [1]
											return [1] + ([0] * len(A_			)) + [1] + ([0] * len(A_			)) + [1]







				def 		UpperCAmelCase_  (    self		,							A_		,							A_ = None			)->						List[int]:







											'''simple docstring'''






											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											else:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]







				def 		UpperCAmelCase_  (    self		,							A_			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			[chr(A_			) for i in text.encode('utf-8'			)]
											return tokens







				def 		UpperCAmelCase_  (    self		,							A_			)->						Any:







											'''simple docstring'''






											if token in self.special_tokens_encoder:
																		UpperCamelCase					       =			self.special_tokens_encoder[token]
											elif token in self.added_tokens_encoder:
																		UpperCamelCase					       =			self.added_tokens_encoder[token]
											elif len(A_			) != 1:
																		UpperCamelCase					       =			self.unk_token_id
											else:
																		UpperCamelCase					       =			ord(A_			) + self._num_special_tokens
											return token_id







				def 		UpperCAmelCase_  (    self		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






											if index in self.special_tokens_decoder:
																		UpperCamelCase					       =			self.special_tokens_decoder[index]
											elif index in self.added_tokens_decoder:
																		UpperCamelCase					       =			self.added_tokens_decoder[index]
											else:
																		UpperCamelCase					       =			chr(index - self._num_special_tokens			)
											return token







				def 		UpperCAmelCase_  (    self		,							A_			)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			B''
											for token in tokens:
																		if token in self.special_tokens_decoder:
																									UpperCamelCase					       =			self.special_tokens_decoder[token].encode('utf-8'			)
																		elif token in self.added_tokens_decoder:
																									UpperCamelCase					       =			self.special_tokens_decoder[token].encode('utf-8'			)
																		elif token in self.special_tokens_encoder:
																									UpperCamelCase					       =			token.encode('utf-8'			)
																		elif token in self.added_tokens_encoder:
																									UpperCamelCase					       =			token.encode('utf-8'			)
																		else:
																									UpperCamelCase					       =			bytes([ord(A_			)]			)
																		bstring += tok_string
											UpperCamelCase					       =			bstring.decode('utf-8'		,							errors='replace'			)
											return string







				def 		UpperCAmelCase_  (    self		,							A_		,							A_ = None			)->						Tuple[str]:







											'''simple docstring'''






											return ()






"""simple docstring"""

from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve




def 			lowercase__  (    lowerCAmelCase :    np.ndarray       , lowerCAmelCase :    np.ndarray       , lowerCAmelCase :    np.ndarray       )       ->					np.ndarray:






    """simple docstring"""




    UpperCAmelCase  	=    np.stack((horizontal_flow, vertical_flow)       , 2       )

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

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

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

    # Set pixels at invalid locations to 0
    UpperCAmelCase  	=    0

    return warped




def 			lowercase__  (    lowerCAmelCase :    np.ndarray       , lowerCAmelCase :    np.ndarray       , lowerCAmelCase :    SupportsIndex       , lowerCAmelCase :    float | None = None       , )       ->					tuple[np.ndarray, np.ndarray]:






    """simple docstring"""




    if alpha is None:
        UpperCAmelCase  	=    0.1

    # Initialize flow
    UpperCAmelCase  	=    np.zeros_like(lowerCAmelCase       )
    UpperCAmelCase  	=    np.zeros_like(lowerCAmelCase       )

    # Prepare kernels for the calculation of the derivatives and the average velocity
    UpperCAmelCase  	=    np.array([[-1, 1], [-1, 1]]       ) * 0.25
    UpperCAmelCase  	=    np.array([[-1, -1], [1, 1]]       ) * 0.25
    UpperCAmelCase  	=    np.array([[1, 1], [1, 1]]       ) * 0.25
    UpperCAmelCase  	=    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(lowerCAmelCase       ):
        UpperCAmelCase  	=    warp(lowerCAmelCase       , lowerCAmelCase       , lowerCAmelCase       )
        UpperCAmelCase  	=    convolve(lowerCAmelCase       , lowerCAmelCase       ) + convolve(lowerCAmelCase       , lowerCAmelCase       )
        UpperCAmelCase  	=    convolve(lowerCAmelCase       , lowerCAmelCase       ) + convolve(lowerCAmelCase       , lowerCAmelCase       )
        UpperCAmelCase  	=    convolve(lowerCAmelCase       , lowerCAmelCase       ) + convolve(lowerCAmelCase       , -kernel_t       )

        UpperCAmelCase  	=    convolve(lowerCAmelCase       , lowerCAmelCase       )
        UpperCAmelCase  	=    convolve(lowerCAmelCase       , lowerCAmelCase       )

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

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

    return horizontal_flow, vertical_flow


if __name__ == "__main__":
   import doctest

   doctest.testmod()



