code: 
  
  
  
  
  """simple docstring"""
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  __A 		=			logging.get_logger(__name__)
  
  __A 		=			["UtteranceLevel"]
  
  
  
  @torch.no_grad()
  def   UpperCamelCase__     (		lowercase__  :     List[Any]       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     str       ,     lowercase__  :     int   ):
    snake_case		:						Tuple								=				torch.load(snake_case_       ,     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		:						Optional[int]								=				checkpoint['''Downstream''']
  
    snake_case		:						Optional[int]								=				HubertConfig.from_pretrained(snake_case_   )
    snake_case		:						Tuple								=				HubertForSequenceClassification.from_pretrained(snake_case_       ,     config=snake_case_   )
    snake_case		:						List[str]								=				WavaVecaFeatureExtractor.from_pretrained(
        snake_case_       ,     return_attention_mask=snake_case_       ,     do_normalize=snake_case_   )
  
    if hf_congfig.use_weighted_layer_sum:
      snake_case		:						Optional[int]								=				checkpoint['''Featurizer''']['''weights''']
  
    snake_case		:						Tuple								=				downstream_dict['''projector.weight''']
    snake_case		:						str								=				downstream_dict['''projector.bias''']
    snake_case		:						int								=				downstream_dict['''model.post_net.linear.weight''']
    snake_case		:						List[Any]								=				downstream_dict['''model.post_net.linear.bias''']
  
    hf_feature_extractor.save_pretrained(snake_case_   )
    hf_model.save_pretrained(snake_case_   )
  
  
  if __name__ == "__main__":
         __A 		=			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.")
         __A 		=			parser.parse_args()
         convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  class 			lowerCamelCase__    :
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				0
  					# max greyscale value for #FFFFFF
  					snake_case		:						List[str]								=				int(self.get_greyscale(255				, 255				, 255							)							)
  
  					if not self.min_threshold < threshold < self.max_threshold:
  							snake_case		:						Union[str, Any]								=				F'''Factor value should be from 0 to {self.max_threshold}'''
  							raise ValueError(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				input_img
  					snake_case		:						Optional[Any]								=				threshold
  					snake_case			,				snake_case		:						List[str]								=				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		:						Union[str, Any]								=				[
  					    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
  					]
  					snake_case		:						Tuple								=				np.ones((self.width, self.height, 3)				, np.uinta							) * 255
  
  
  			@classmethod
  			def 							lowerCamelCase_				(     cls				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					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		:						List[Any]								=				greyscale + self.error_table[x][y]
  									else:
  											snake_case		:						int								=				(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
  							__A 		=			[
  							    Burkes(imread("image_data/lena.jpg", 1), threshold)
  							    for threshold in (1, 126, 130, 140)
  							]
  
  							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()
  
  