




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def   A		(__A  :       Tuple  ,							__A  :       List[Any]  ,							__A  :       Any			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				TaConfig.from_pretrained(__A			)
     UpperCAmelCase_		=				FlaxTaForConditionalGeneration(config=__A			)
     UpperCAmelCase_		=				checkpoints.load_tax_checkpoint(__A			)

     UpperCAmelCase_		=				'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers			):
          UpperCAmelCase_		=				F"""layers_{str(__A			)}"""

          # Self-Attention
          UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          UpperCAmelCase_		=				tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          UpperCAmelCase_		=				tax_attention_key
          UpperCAmelCase_		=				tax_attention_out
          UpperCAmelCase_		=				tax_attention_query
          UpperCAmelCase_		=				tax_attention_value

          UpperCAmelCase_		=				tax_attention_layer_norm

          if split_mlp_wi:
               UpperCAmelCase_		=				tax_mlp_wi_a
               UpperCAmelCase_		=				tax_mlp_wi_a
          else:
               UpperCAmelCase_		=				tax_mlp_wi

          UpperCAmelCase_		=				tax_mlp_wo
          UpperCAmelCase_		=				tax_mlp_layer_norm

     # Only for layer 0:
     UpperCAmelCase_		=				tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     UpperCAmelCase_		=				tax_encoder_rel_embedding

     # Assigning
     UpperCAmelCase_		=				tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     UpperCAmelCase_		=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers			):
          UpperCAmelCase_		=				F"""layers_{str(__A			)}"""

          # Self-Attention
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          UpperCAmelCase_		=				tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          UpperCAmelCase_		=				tax_attention_key
          UpperCAmelCase_		=				tax_attention_out
          UpperCAmelCase_		=				tax_attention_query
          UpperCAmelCase_		=				tax_attention_value

          UpperCAmelCase_		=				tax_pre_attention_layer_norm

          UpperCAmelCase_		=				tax_enc_dec_attention_key
          UpperCAmelCase_		=				tax_enc_dec_attention_out
          UpperCAmelCase_		=				tax_enc_dec_attention_query
          UpperCAmelCase_		=				tax_enc_dec_attention_value

          UpperCAmelCase_		=				tax_cross_layer_norm

          if split_mlp_wi:
               UpperCAmelCase_		=				tax_mlp_wi_a
               UpperCAmelCase_		=				tax_mlp_wi_a
          else:
               UpperCAmelCase_		=				tax_mlp_wi

          UpperCAmelCase_		=				tax_mlp_wo

          UpperCAmelCase_		=				txa_mlp_layer_norm

     # Decoder Normalization
     UpperCAmelCase_		=				tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     UpperCAmelCase_		=				txa_decoder_norm

     # Only for layer 0:
     UpperCAmelCase_		=				tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     UpperCAmelCase_		=				tax_decoder_rel_embedding

     # Token Embeddings
     UpperCAmelCase_		=				tax_model['''target''']['''token_embedder''']['''embedding''']
     UpperCAmelCase_		=				txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          UpperCAmelCase_		=				tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(__A			)
     print('''T5X Model was sucessfully converted!'''			)


if __name__ == "__main__":
    snake_case_					:					List[str]       						=			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."
    )
    snake_case_					:					int       						=			parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












def   A		(__A  :       int			)  -> int:







     """simple docstring"""


     if not isinstance(__A  ,							__A			):
          UpperCAmelCase_		=				F"""Input value of [number={number}] must be an integer"""
          raise TypeError(__A			)
     if number < 1:
          raise ValueError('''Input must be a positive integer'''			)
     return number * (2 * number - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






