
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class        SCREAMING_SNAKE_CASE__	(			snake_case_				,    snake_case_				,    snake_case_):







				@register_to_config
				def __init__(    self		,							A_		,							A_		,							A_		,							A_		,							A_		,							A_		,							A_		,							A_		,							A_		,							A_ = False		,							)->						Tuple:







											'''simple docstring'''






											super().__init__()

											UpperCamelCase					       =			nn.Linear(A_		,							A_		,							bias=A_			)

											UpperCamelCase					       =			nn.Embedding(A_		,							A_			)
											UpperCamelCase					       =			False

											UpperCamelCase					       =			nn.Dropout(p=A_			)

											UpperCamelCase					       =			TaConfig(
											    d_model=A_		,							num_heads=A_		,							d_kv=A_		,							d_ff=A_		,							feed_forward_proj=A_		,							dropout_rate=A_		,							is_decoder=A_		,							is_encoder_decoder=A_		,							)
											UpperCamelCase					       =			nn.ModuleList()
											for lyr_num in range(A_			):
																		UpperCamelCase					       =			TaBlock(A_			)
																		self.encoders.append(A_			)

											UpperCamelCase					       =			TaLayerNorm(A_			)
											UpperCamelCase					       =			nn.Dropout(p=A_			)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						Optional[int]:







											'''simple docstring'''






											UpperCamelCase					       =			self.input_proj(A_			)

											# terminal relative positional encodings
											UpperCamelCase					       =			encoder_inputs.shape[1]
											UpperCamelCase					       =			torch.arange(A_		,							device=encoder_inputs.device			)

											UpperCamelCase					       =			encoder_inputs_mask.sum(-1			)
											UpperCamelCase					       =			torch.roll(input_positions.unsqueeze(0			)		,							tuple(seq_lens.tolist()			)		,							dims=0			)
											x += self.position_encoding(A_			)

											UpperCamelCase					       =			self.dropout_pre(A_			)

											# inverted the attention mask
											UpperCamelCase					       =			encoder_inputs.size()
											UpperCamelCase					       =			self.get_extended_attention_mask(A_		,							A_			)

											for lyr in self.encoders:
																		UpperCamelCase					       =			lyr(A_		,							A_			)[0]
											UpperCamelCase					       =			self.layer_norm(A_			)

											return self.dropout_post(A_			), encoder_inputs_mask






'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							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 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

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

							# 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(A):
														UpperCamelCase					       =			warp(A						,			A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			-kernel_t)

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# 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()




