



from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			lowercase__         = np.stack((horizontal_flow, vertical_flow)  ,     2     )

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

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

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

			# Set pixels at invalid locations to 0
			lowercase__         = 0

			return warped



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_ = None  ,     ):

			if alpha is None:
						lowercase__         = 0.1

			# Initialize flow
			lowercase__         = np.zeros_like(lowercase_     )
			lowercase__         = np.zeros_like(lowercase_     )

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

						lowercase__         = convolve(lowercase_  ,     lowercase_     )
						lowercase__         = convolve(lowercase_  ,     lowercase_     )

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

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

			return horizontal_flow, vertical_flow


if __name__ == "__main__":
							import doctest

							doctest.testmod()











"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
