import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Dict				=    0
													# max greyscale value for #FFFFFF
													_snake_case						:     Union[str, Any]				=    int(self.get_greyscale(255      ,				255      ,				255				)				)

													if not self.min_threshold < threshold < self.max_threshold:
																			_snake_case						:     List[str]				=    f"""Factor value should be from 0 to {self.max_threshold}"""
																			raise ValueError(lowercase_				)

													_snake_case						:     List[Any]				=    input_img
													_snake_case						:     Dict				=    threshold
													_snake_case				,_snake_case						:     Any				=    self.input_img.shape[1], self.input_img.shape[0]

													# error table size (+4 columns and +1 row) greater than input image because of
													# lack of if statements
													_snake_case						:     List[Any]				=    [
													    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
													]
													_snake_case						:     str				=    np.ones((self.width, self.height, 3)      ,				np.uinta				) * 255



							@classmethod
							def        UpperCamelCase       (						cls      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													return 0.114 * blue + 0.587 * green + 0.2_126 * red



							def        UpperCamelCase       (						self				):
													for y in range(self.height				):
																			for x in range(self.width				):
																									_snake_case						:     Dict				=    int(self.get_greyscale(*self.input_img[y][x]				)				)
																									if self.threshold > greyscale + self.error_table[y][x]:
																															_snake_case						:     List[str]				=    (0, 0, 0)
																															_snake_case						:     str				=    greyscale + self.error_table[x][y]
																									else:
																															_snake_case						:     Union[str, Any]				=    (255, 255, 255)
																															_snake_case						:     Tuple				=    greyscale + self.error_table[x][y] - 255
																									self.error_table[y][x + 1] += int(8 / 32 * current_error				)
																									self.error_table[y][x + 2] += int(4 / 32 * current_error				)
																									self.error_table[y + 1][x] += int(8 / 32 * current_error				)
																									self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
																									self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
																									self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
																									self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
				# create Burke's instances with original images in greyscale
				__SCREAMING_SNAKE_CASE						:    Optional[Any]     = [
				    Burkes(imread('image_data/lena.jpg', 1), threshold)
				    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
				]

				for burkes in burkes_instances:
								burkes.process()

				for burkes in burkes_instances:
								imshow(
								    F'''Original image with dithering threshold: {burkes.threshold}''',
								    burkes.output_img,
								)

				waitKey(0)
				destroyAllWindows()
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)