




"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def     __snake_case   (							_lowercase     :   int ,_lowercase     :   Union[str, Any] ,_lowercase     :   int						):


    """simple docstring"""







    UpperCamelCase										=  TaConfig.from_pretrained(_lowercase						)
    UpperCamelCase										=  FlaxTaForConditionalGeneration(config=_lowercase						)
    UpperCamelCase										=  checkpoints.load_tax_checkpoint(_lowercase						)

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

    # Encoder
    for layer_index in range(config.num_layers						):
        UpperCamelCase										=  f'layers_{str(_lowercase						)}'

        # 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(_lowercase						)}'

        # 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(_lowercase						)
    print('''T5X Model was sucessfully converted!'''						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					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.'
  )
  SCREAMING_SNAKE_CASE_      =					parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""







from math import pi





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return degree / (180 / pi)


if __name__ == "__main__":
  from doctest import testmod

  testmod()