



from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve

def       A_   (						__a  :					np.ndarray			,		__a  :					np.ndarray			,		__a  :					np.ndarray		):

				"""simple docstring"""
				a__					   =		np.stack((horizontal_flow, vertical_flow)			,		2		)

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

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

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

				# Set pixels at invalid locations to 0
				a__					   =		0

				return warped

def       A_   (						__a  :					np.ndarray			,		__a  :					np.ndarray			,		__a  :					SupportsIndex			,		__a  :					float | None = None			,		):

				"""simple docstring"""
				if alpha is None:
								a__					   =		0.1

				# Initialize flow
				a__					   =		np.zeros_like(__a		)
				a__					   =		np.zeros_like(__a		)

				# Prepare kernels for the calculation of the derivatives and the average velocity
				a__					   =		np.array([[-1, 1], [-1, 1]]		) * 0.2_5
				a__					   =		np.array([[-1, -1], [1, 1]]		) * 0.2_5
				a__					   =		np.array([[1, 1], [1, 1]]		) * 0.2_5
				a__					   =		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		):
								a__					   =		warp(__a			,		__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		-kernel_t		)

								a__					   =		convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		)

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

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

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
	import doctest

	doctest.testmod()









"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


a  :						List[Any]					       =							logging.get_logger(__name__)

a  :						List[Any]					       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a  :						Any					       =							{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

a  :						str					       =							{
    '''microsoft/mpnet-base''': 512,
}

a  :						Union[str, Any]					       =							{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						VOCAB_FILES_NAMES
    __lowercase    :   Optional[int]											=						PRETRAINED_VOCAB_FILES_MAP
    __lowercase    :   Dict											=						PRETRAINED_INIT_CONFIGURATION
    __lowercase    :   Optional[Any]											=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase    :   Dict											=						MPNetTokenizer
    __lowercase    :   Dict											=						['input_ids', 'attention_mask']


    def __init__(      self   ,			__lowercase=None   ,			__lowercase=None   ,			__lowercase=True   ,			__lowercase="<s>"   ,			__lowercase="</s>"   ,			__lowercase="</s>"   ,			__lowercase="<s>"   ,			__lowercase="[UNK]"   ,			__lowercase="<pad>"   ,			__lowercase="<mask>"   ,			__lowercase=True   ,			__lowercase=None   ,			**__lowercase   ,			):
     super().__init__(
         __lowercase   ,			tokenizer_file=__lowercase   ,			do_lower_case=__lowercase   ,			bos_token=__lowercase   ,			eos_token=__lowercase   ,			sep_token=__lowercase   ,			cls_token=__lowercase   ,			unk_token=__lowercase   ,			pad_token=__lowercase   ,			mask_token=__lowercase   ,			tokenize_chinese_chars=__lowercase   ,			strip_accents=__lowercase   ,			**__lowercase   ,			)

     UpperCAmelCase__ 						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("""lowercase"""   ,			__lowercase			) != do_lower_case
         or pre_tok_state.get("""strip_accents"""   ,			__lowercase			) != strip_accents
     ):
      UpperCAmelCase__ 						=				getattr(__lowercase   ,			pre_tok_state.pop("""type"""			)			)
      UpperCAmelCase__ 						=				do_lower_case
      UpperCAmelCase__ 						=				strip_accents
      UpperCAmelCase__ 						=				pre_tok_class(**__lowercase			)

     UpperCAmelCase__ 						=				do_lower_case


    @property
    def 		A__      (      self			):
     if self._mask_token is None:
      if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""			)
      return None
     return str(self._mask_token			)


    @mask_token.setter
    def 		A__      (      self   ,			__lowercase			):
     UpperCAmelCase__ 						=				AddedToken(__lowercase   ,			lstrip=__lowercase   ,			rstrip=__lowercase			) if isinstance(__lowercase   ,			__lowercase			) else value
     UpperCAmelCase__ 						=				value


    def 		A__      (      self   ,			__lowercase   ,			__lowercase=None			):
     UpperCAmelCase__ 						=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				[self.sep_token_id]
     UpperCAmelCase__ 						=				[self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep			) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				self._tokenizer.model.save(__lowercase   ,			name=__lowercase			)
     return tuple(__lowercase			)



