






from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = None					,     ):


		'''simple docstring'''

		if alpha is None:
				__A    =							0.1

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

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

				__A    =							convolve(lowerCAmelCase__					,     lowerCAmelCase__	)
				__A    =							convolve(lowerCAmelCase__					,     lowerCAmelCase__	)

				# 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()







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






