


"""simple docstring"""







from __future__ import annotations

lowerCamelCase_     :     int    		=						"""Alexander Joslin"""
def _A		(		lowercase	):







    """simple docstring"""

    if number <= 0:
        a					=f'''n must be greater than 0. Got n = {number}'''
        raise ValueError(lowercase	)

    a					=[number + 1] * (number + 1)

    # starting position
    a					=0
    for i in range(1 ,					lowercase	):
        a					=min(table[i + 1] ,					table[i] + 1	)
        # check if out of bounds
        if i * 2 <= number:
            a					=min(table[i * 2] ,					table[i] + 1	)
        # check if out of bounds
        if i * 3 <= number:
            a					=min(table[i * 3] ,					table[i] + 1	)
    return table[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()



"""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)