


"""simple docstring"""



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Dict						,							lowercase  :  Dict )	->						Tuple:


    """simple docstring"""





    snake_case    :      Dict      					=    AutoConfig.from_pretrained(lowercase )
    snake_case    :      Optional[Any]      					=    FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase )
    snake_case    :      List[str]      					=    checkpoints.load_tax_checkpoint(lowercase )

    snake_case    :      List[Any]      					=    "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

    if config.model_type == "t5":
        snake_case    :      Tuple      					=    "SelfAttention"
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case    :      Any      					=    "LocalSelfAttention"
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case    :      str      					=    "TransientGlobalSelfAttention"
    else:
        raise ValueError(
            "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
            " attribute with a value from ['local', 'transient-global]." )

    # Encoder
    for layer_index in range(config.num_layers ):
        snake_case    :      int      					=    F'layers_{str(lowercase )}'

        # Self-Attention
        snake_case    :      Any      					=    tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
        snake_case    :      str      					=    tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
        snake_case    :      Union[str, Any]      					=    tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
        snake_case    :      List[str]      					=    tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case    :      Optional[Any]      					=    tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

        # Layer Normalization
        snake_case    :      List[Any]      					=    tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

        if split_mlp_wi:
            snake_case    :      str      					=    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            snake_case    :      Optional[Any]      					=    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            snake_case    :      Optional[Any]      					=    tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

        snake_case    :      List[str]      					=    tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        snake_case    :      Optional[int]      					=    tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        snake_case    :      Optional[int]      					=    flax_model.params["encoder"]["block"][str(lowercase )]["layer"]
        snake_case    :      Tuple      					=    tax_attention_key
        snake_case    :      Optional[int]      					=    tax_attention_out
        snake_case    :      Optional[int]      					=    tax_attention_query
        snake_case    :      Optional[Any]      					=    tax_attention_value

        snake_case    :      Any      					=    tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case    :      Optional[int]      					=    tax_global_layer_norm

        if split_mlp_wi:
            snake_case    :      Any      					=    tax_mlp_wi_a
            snake_case    :      Union[str, Any]      					=    tax_mlp_wi_a
        else:
            snake_case    :      Any      					=    tax_mlp_wi

        snake_case    :      Any      					=    tax_mlp_wo
        snake_case    :      int      					=    tax_mlp_layer_norm

        snake_case    :      Optional[int]      					=    flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case    :      int      					=    tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    snake_case    :      int      					=    tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case    :      str      					=    tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
        snake_case    :      Optional[int]      					=    tax_encoder_global_rel_embedding

    # Assigning
    snake_case    :      List[str]      					=    tax_model["target"]["encoder"]["encoder_norm"]["scale"]
    snake_case    :      Optional[int]      					=    tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers ):
        snake_case    :      Any      					=    F'layers_{str(lowercase )}'

        # Self-Attention
        snake_case    :      List[Any]      					=    tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
        snake_case    :      Optional[int]      					=    tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
        snake_case    :      str      					=    tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
        snake_case    :      List[str]      					=    tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

        # Layer Normalization
        snake_case    :      List[Any]      					=    tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
            "scale"
        ]

        # Encoder-Decoder-Attention
        snake_case    :      Dict      					=    tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
        snake_case    :      str      					=    tax_enc_dec_attention_module["key"]["kernel"]
        snake_case    :      Dict      					=    tax_enc_dec_attention_module["out"]["kernel"]
        snake_case    :      Tuple      					=    tax_enc_dec_attention_module["query"]["kernel"]
        snake_case    :      str      					=    tax_enc_dec_attention_module["value"]["kernel"]

        # Layer Normalization
        snake_case    :      List[Any]      					=    tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

        # MLP
        if split_mlp_wi:
            snake_case    :      int      					=    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            snake_case    :      Optional[int]      					=    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            snake_case    :      List[Any]      					=    tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

        snake_case    :      Optional[Any]      					=    tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        snake_case    :      Optional[int]      					=    tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        snake_case    :      List[Any]      					=    flax_model.params["decoder"]["block"][str(lowercase )]["layer"]
        snake_case    :      List[str]      					=    tax_attention_key
        snake_case    :      int      					=    tax_attention_out
        snake_case    :      Dict      					=    tax_attention_query
        snake_case    :      Optional[Any]      					=    tax_attention_value

        snake_case    :      int      					=    tax_pre_attention_layer_norm

        snake_case    :      str      					=    tax_enc_dec_attention_key
        snake_case    :      Optional[int]      					=    tax_enc_dec_attention_out
        snake_case    :      Union[str, Any]      					=    tax_enc_dec_attention_query
        snake_case    :      Optional[int]      					=    tax_enc_dec_attention_value

        snake_case    :      Tuple      					=    tax_cross_layer_norm

        if split_mlp_wi:
            snake_case    :      List[str]      					=    tax_mlp_wi_a
            snake_case    :      Union[str, Any]      					=    tax_mlp_wi_a
        else:
            snake_case    :      Dict      					=    tax_mlp_wi

        snake_case    :      List[str]      					=    tax_mlp_wo

        snake_case    :      Tuple      					=    txa_mlp_layer_norm

        snake_case    :      Union[str, Any]      					=    flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case    :      int      					=    tax_model["target"]["decoder"]["decoder_norm"]["scale"]
    snake_case    :      Union[str, Any]      					=    txa_decoder_norm

    # Only for layer 0:
    snake_case    :      Union[str, Any]      					=    tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    snake_case    :      Union[str, Any]      					=    tax_decoder_rel_embedding

    # Token Embeddings
    snake_case    :      Tuple      					=    tax_model["target"]["token_embedder"]["embedding"]
    snake_case    :      Tuple      					=    txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case    :      List[str]      					=    tax_model["target"]["decoder"]["logits_dense"]["kernel"]

    flax_model.save_pretrained(lowercase )
    print("T5X Model was sucessfully converted!" )


if __name__ == "__main__":
  __snake_case   			=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  __snake_case   			=    parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  int )	->						bool:


    """simple docstring"""





    if p < 2:
        raise ValueError("p should not be less than 2!" )
    elif p == 2:
        return True

    snake_case    :      Dict      					=    4
    snake_case    :      str      					=    (1 << p) - 1
    for _ in range(p - 2 ):
        snake_case    :      Any      					=    ((s * s) - 2) % m
    return s == 0


if __name__ == "__main__":
  print(lucas_lehmer_test(7))
  print(lucas_lehmer_test(11))





