code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  def _A		(		lowercase = 10_00	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=0
      a					=(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      a					=(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      a					=(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total
  
  
  if __name__ == "__main__":
         print(F'{solution() = }')
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)