code: 
  
  
  
  
  """simple docstring"""
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Optional[Any]       ,     lowercase__  :     List[str]       ,     lowercase__  :     Optional[int]   ):
    snake_case		:						int								=				TaConfig.from_pretrained(lowercase__   )
    snake_case		:						List[str]								=				FlaxTaForConditionalGeneration(config=lowercase__   )
    snake_case		:						Any								=				checkpoints.load_tax_checkpoint(lowercase__   )
  
    snake_case		:						Any								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]
  
    # Encoder
    for layer_index in range(config.num_layers   ):
      snake_case		:						Any								=				F'''layers_{str(lowercase__   )}'''
  
      # Self-Attention
      snake_case		:						Dict								=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      snake_case		:						Optional[Any]								=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      snake_case		:						Any								=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      snake_case		:						List[str]								=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]
  
      # Layer Normalization
      snake_case		:						int								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]
  
      if split_mlp_wi:
        snake_case		:						Tuple								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
        snake_case		:						Any								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
        snake_case		:						Any								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]
  
      snake_case		:						Dict								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]
  
      # Layer Normalization
      snake_case		:						Optional[Any]								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
  
      # Assigning
      snake_case		:						Tuple								=				tax_attention_key
      snake_case		:						int								=				tax_attention_out
      snake_case		:						List[Any]								=				tax_attention_query
      snake_case		:						Union[str, Any]								=				tax_attention_value
  
      snake_case		:						Any								=				tax_attention_layer_norm
  
      if split_mlp_wi:
        snake_case		:						Tuple								=				tax_mlp_wi_a
        snake_case		:						List[str]								=				tax_mlp_wi_a
      else:
        snake_case		:						Optional[int]								=				tax_mlp_wi
  
      snake_case		:						Optional[Any]								=				tax_mlp_wo
      snake_case		:						Optional[Any]								=				tax_mlp_layer_norm
  
    # Only for layer 0:
    snake_case		:						int								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    snake_case		:						List[Any]								=				tax_encoder_rel_embedding
  
    # Assigning
    snake_case		:						Union[str, Any]								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
    snake_case		:						List[str]								=				tax_encoder_norm
  
    # Decoder
    for layer_index in range(config.num_decoder_layers   ):
      snake_case		:						Optional[Any]								=				F'''layers_{str(lowercase__   )}'''
  
      # Self-Attention
      snake_case		:						int								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      snake_case		:						str								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      snake_case		:						List[Any]								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      snake_case		:						List[Any]								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]
  
      # Layer Normalization
      snake_case		:						int								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]
  
      # Encoder-Decoder-Attention
      snake_case		:						str								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
          "kernel"
      ]
      snake_case		:						Optional[int]								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
          "kernel"
      ]
      snake_case		:						List[str]								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
          "kernel"
      ]
      snake_case		:						Tuple								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
          "kernel"
      ]
  
      # Layer Normalization
      snake_case		:						int								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]
  
      # MLP
      if split_mlp_wi:
        snake_case		:						List[str]								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
        snake_case		:						Tuple								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
        snake_case		:						Optional[Any]								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]
  
      snake_case		:						Any								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]
  
      # Layer Normalization
      snake_case		:						Optional[Any]								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
  
      # Assigning
      snake_case		:						Dict								=				tax_attention_key
      snake_case		:						List[Any]								=				tax_attention_out
      snake_case		:						List[Any]								=				tax_attention_query
      snake_case		:						List[Any]								=				tax_attention_value
  
      snake_case		:						Optional[int]								=				tax_pre_attention_layer_norm
  
      snake_case		:						int								=				tax_enc_dec_attention_key
      snake_case		:						Optional[Any]								=				tax_enc_dec_attention_out
      snake_case		:						List[str]								=				tax_enc_dec_attention_query
      snake_case		:						Tuple								=				tax_enc_dec_attention_value
  
      snake_case		:						Optional[Any]								=				tax_cross_layer_norm
  
      if split_mlp_wi:
        snake_case		:						Any								=				tax_mlp_wi_a
        snake_case		:						List[Any]								=				tax_mlp_wi_a
      else:
        snake_case		:						Union[str, Any]								=				tax_mlp_wi
  
      snake_case		:						Tuple								=				tax_mlp_wo
  
      snake_case		:						Union[str, Any]								=				txa_mlp_layer_norm
  
    # Decoder Normalization
    snake_case		:						Optional[Any]								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
    snake_case		:						Optional[Any]								=				txa_decoder_norm
  
    # Only for layer 0:
    snake_case		:						str								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    snake_case		:						Tuple								=				tax_decoder_rel_embedding
  
    # Token Embeddings
    snake_case		:						int								=				tax_model["target"]["token_embedder"]["embedding"]
    snake_case		:						Union[str, Any]								=				txa_token_embeddings
  
    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
      snake_case		:						Union[str, Any]								=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]
  
    flax_model.save_pretrained(lowercase__   )
    print("T5X Model was sucessfully converted!"   )
  
  
  if __name__ == "__main__":
         __A 		=			argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
         )
         parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
         parser.add_argument(
             "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
         )
         __A 		=			parser.parse_args()
         convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_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()
  
  