






import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


A__			   = get_logger(__name__)


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					if text.startswith(snake_case    ):
										return text[len(snake_case    ) :]
					return text  # or whatever


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					return {remove_prefix(snake_case			,		"""model."""    ): v for k, v in sd.items()}


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					{}
					for k in state_dicts[0].keys():
										_lowerCAmelCase      			=					[sd[k] for sd in state_dicts]
										_lowerCAmelCase      			=					sum(snake_case    ) / len(snake_case    )
										assert isinstance(snake_case			,		torch.Tensor    )
										_lowerCAmelCase      			=					new_t
					return new_sd


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					AutoModelForSeqaSeqLM.from_pretrained(snake_case    )
					if os.path.isfile(snake_case    ):
										_lowerCAmelCase      			=					[pl_ckpt_path]
					else:
										assert os.path.isdir(snake_case    )
										_lowerCAmelCase      			=					list(Path(snake_case    ).glob("""*.ckpt"""    )    )
										assert ckpt_files, F'could not find any ckpt files inside the {pl_ckpt_path} directory'

					if len(snake_case    ) > 1:
										logger.info(F'averaging the weights of {ckpt_files}'    )

					_lowerCAmelCase      			=					[sanitize(torch.load(snake_case			,		map_location="""cpu"""    )["""state_dict"""]    ) for x in ckpt_files]
					_lowerCAmelCase      			=					average_state_dicts(snake_case    )

					_lowerCAmelCase			,			_lowerCAmelCase      			=					hf_model.load_state_dict(snake_case			,		strict=snake_case    )
					assert not missing, F'missing keys: {missing}'
					hf_model.save_pretrained(snake_case    )
					try:
										_lowerCAmelCase      			=					AutoTokenizer.from_pretrained(snake_case    )
										tok.save_pretrained(snake_case    )
					except Exception:
										pass
										# dont copy tokenizer if cant


if __name__ == "__main__":
	fire.Fire(convert_pl_to_hf)








import cva
import numpy as np


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					try:
										return int(image[x_coordinate][y_coordinate] >= center    )
					except (IndexError, TypeError):
										return 0


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					image[x_coordinate][y_coordinate]
					_lowerCAmelCase      			=					[1, 2, 4, 8, 16, 32, 64, 1_28]

					# skip get_neighbors_pixel if center is null
					if center is None:
										return 0

					# Starting from the top right, assigning value to pixels clockwise
					_lowerCAmelCase      			=					[
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate - 1			,		snake_case    ),
					]

					# Converting the binary value to decimal.
					return sum(
					    binary_value * power for binary_value, power in zip(snake_case			,		snake_case    )    )


if __name__ == "__main__":
	# Reading the image and converting it to grayscale.
	A__			   = cva.imread(
	    """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
	)

	# Create a numpy array as the same height and width of read image
	A__			   = np.zeros((image.shape[0], image.shape[1]))

	# Iterating through the image and calculating the
	# local binary pattern value for each pixel.
	for i in range(0, image.shape[0]):
		for j in range(0, image.shape[1]):
			A__			   = local_binary_value(image, i, j)

	cva.imshow("""local binary pattern""", lbp_image)
	cva.waitKey(0)
	cva.destroyAllWindows()



