import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : Optional[int]						,				__SCREAMING_SNAKE_CASE    : int						,				__SCREAMING_SNAKE_CASE    : List[Any]						,				__SCREAMING_SNAKE_CASE    : List[Any]					):







    '''simple docstring'''
    __snake_case    :							int             =  ElectraConfig.from_json_file(__SCREAMING_SNAKE_CASE					)
    print(F'''Building PyTorch model from configuration: {config}'''					)

    if discriminator_or_generator == "discriminator":
        __snake_case    :							List[Any]             =  ElectraForPreTraining(__SCREAMING_SNAKE_CASE					)
    elif discriminator_or_generator == "generator":
        __snake_case    :							Optional[int]             =  ElectraForMaskedLM(__SCREAMING_SNAKE_CASE					)
    else:
        raise ValueError("""The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'"""					)

    # Load weights from tf checkpoint
    load_tf_weights_in_electra(
        __SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				discriminator_or_generator=__SCREAMING_SNAKE_CASE					)

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    torch.save(model.state_dict()						,				__SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
     lowercase_   				=       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--discriminator_or_generator",
         default=None,
         type=str,
         required=True,
         help=(
             "Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or "
             "\'generator\'."
         ),
     )
     lowercase_   				=       parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
     )





"""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()



