

'''simple docstring'''

from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve

def      __magic_name__							(			A       , A       , A				)      ->		np.ndarray:
	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
	snake_case							=					np.stack(
	    np.meshgrid(np.arange(0       , image.shape[1]				)       , np.arange(0       , image.shape[0]				)				)       , 2				)
	snake_case							=					np.round(grid - flow				).astype(np.intaa				)

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

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

	# Set pixels at invalid locations to 0
	snake_case							=					0

	return warped

def      __magic_name__							(			A       , A       , A       , A = None       , )      ->		tuple[np.ndarray, np.ndarray]:
	if alpha is None:
		snake_case							=					0.1

	# Initialize flow
	snake_case							=					np.zeros_like(A				)
	snake_case							=					np.zeros_like(A				)

	# Prepare kernels for the calculation of the derivatives and the average velocity
	snake_case							=					np.array([[-1, 1], [-1, 1]]				) * 0.25
	snake_case							=					np.array([[-1, -1], [1, 1]]				) * 0.25
	snake_case							=					np.array([[1, 1], [1, 1]]				) * 0.25
	snake_case							=					np.array(
	    [[1 / 1_2, 1 / 6, 1 / 1_2], [1 / 6, 0, 1 / 6], [1 / 1_2, 1 / 6, 1 / 1_2]]				)

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

		snake_case							=					convolve(A       , A				)
		snake_case							=					convolve(A       , A				)

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

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

	return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

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

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[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				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





