code: 
  
  """simple docstring"""
  
  
  
  
  
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  lowerCamelCase_     :     List[str]    		=						1_8_0
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=k_size // 2
      a	,   a					=np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      a					=(
          1
          / (2 * np.pi * sigma)
          * np.exp(-(np.square(__snake_case	) + np.square(__snake_case	)) / (2 * np.square(__snake_case	))	)
      )
      return g
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=np.zeros(__snake_case	)
  
      for row in range(1 ,					image_shape[0] - 1	):
          for col in range(1 ,					image_shape[1] - 1	):
              a					=gradient_direction[row, col]
  
              if (
                  0 <= direction < PI / 8
                  or 15 * PI / 8 <= direction <= 2 * PI
                  or 7 * PI / 8 <= direction <= 9 * PI / 8
              ):
                  a					=sobel_grad[row, col - 1]
                  a					=sobel_grad[row, col + 1]
                  if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                      a					=sobel_grad[row, col]
  
              elif (
                  PI / 8 <= direction < 3 * PI / 8
                  or 9 * PI / 8 <= direction < 11 * PI / 8
              ):
                  a					=sobel_grad[row + 1, col - 1]
                  a					=sobel_grad[row - 1, col + 1]
                  if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                      a					=sobel_grad[row, col]
  
              elif (
                  3 * PI / 8 <= direction < 5 * PI / 8
                  or 11 * PI / 8 <= direction < 13 * PI / 8
              ):
                  a					=sobel_grad[row - 1, col]
                  a					=sobel_grad[row + 1, col]
                  if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                      a					=sobel_grad[row, col]
  
              elif (
                  5 * PI / 8 <= direction < 7 * PI / 8
                  or 13 * PI / 8 <= direction < 15 * PI / 8
              ):
                  a					=sobel_grad[row - 1, col - 1]
                  a					=sobel_grad[row + 1, col + 1]
                  if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                      a					=sobel_grad[row, col]
  
      return destination
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      for row in range(1 ,					image_shape[0] - 1	):
          for col in range(1 ,					image_shape[1] - 1	):
              if destination[row, col] >= threshold_high:
                  a					=strong
              elif destination[row, col] <= threshold_low:
                  a					=0
              else:
                  a					=weak
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      for row in range(1 ,					image_shape[0]	):
          for col in range(1 ,					image_shape[1]	):
              if destination[row, col] == weak:
                  if 2_55 in (
                      destination[row, col + 1],
                      destination[row, col - 1],
                      destination[row - 1, col],
                      destination[row + 1, col],
                      destination[row - 1, col - 1],
                      destination[row + 1, col - 1],
                      destination[row - 1, col + 1],
                      destination[row + 1, col + 1],
                  ):
                      a					=strong
                  else:
                      a					=0
  def _A		(		lowercase ,					lowercase=15 ,					lowercase=30 ,					lowercase=1_28 ,					lowercase=2_55	):
  
  
  
  
  
  
  
      """simple docstring"""
      # gaussian_filter
      a					=img_convolve(__snake_case ,					gen_gaussian_kernel(9 ,					sigma=1.4	)	)
      # get the gradient and degree by sobel_filter
      a	,   a					=sobel_filter(__snake_case	)
      a					=PI + np.radadeg(__snake_case	)
  
      a					=suppress_non_maximum(image.shape ,					__snake_case ,					__snake_case	)
  
      detect_high_low_threshold(
          image.shape ,					__snake_case ,					__snake_case ,					__snake_case ,					__snake_case ,					__snake_case	)
  
      track_edge(image.shape ,					__snake_case ,					__snake_case ,					__snake_case	)
  
      return destination
  
  
  if __name__ == "__main__":
         # read original image in gray mode
         lowerCamelCase_     :     Optional[Any]    		=						cva.imread(R"""../image_data/lena.jpg""", 0)
         # canny edge detection
         lowerCamelCase_     :     Any    		=						canny(lena)
         cva.imshow("""canny""", canny_destination)
         cva.waitKey(0)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=TaConfig.from_pretrained(lowercase	)
      a					=FlaxTaForConditionalGeneration(config=lowercase	)
      a					=checkpoints.load_tax_checkpoint(lowercase	)
  
      a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
      # Encoder
      for layer_index in range(config.num_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
          if split_mlp_wi:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_attention_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
          a					=tax_mlp_layer_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_encoder_rel_embedding
  
      # Assigning
      a					=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      a					=tax_encoder_norm
  
      # Decoder
      for layer_index in range(config.num_decoder_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]
  
          # Encoder-Decoder-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
          # MLP
          if split_mlp_wi:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_pre_attention_layer_norm
  
          a					=tax_enc_dec_attention_key
          a					=tax_enc_dec_attention_out
          a					=tax_enc_dec_attention_query
          a					=tax_enc_dec_attention_value
  
          a					=tax_cross_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
  
          a					=txa_mlp_layer_norm
  
      # Decoder Normalization
      a					=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      a					=txa_decoder_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_decoder_rel_embedding
  
      # Token Embeddings
      a					=tax_model['''target''']['''token_embedder''']['''embedding''']
      a					=txa_token_embeddings
  
      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
          a					=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
      flax_model.save_pretrained(lowercase	)
      print('''T5X Model was sucessfully converted!'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						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."""
         )
         lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
         convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)