src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py/convert_t5x_checkpoint_to_flax
def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path):
    config = AutoConfig.from_pretrained(config_name)
    flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=config)
    t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path)

    split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"]

    if config.model_type == "t5":
        encoder_attn_name = "SelfAttention"
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        encoder_attn_name = "LocalSelfAttention"
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        encoder_attn_name = "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):
        layer_name = f"layers_{str(layer_index)}"

        # Self-Attention
        t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
        t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
        t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
        t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            t5x_global_layer_norm = t5x_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

        # Layer Normalization
        t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

        if split_mlp_wi:
            t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

        t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        flax_model_encoder_layer_block = flax_model.params["encoder"]["block"][str(layer_index)]["layer"]
        flax_model_encoder_layer_block["0"][encoder_attn_name]["k"]["kernel"] = t5x_attention_key
        flax_model_encoder_layer_block["0"][encoder_attn_name]["o"]["kernel"] = t5x_attention_out
        flax_model_encoder_layer_block["0"][encoder_attn_name]["q"]["kernel"] = t5x_attention_query
        flax_model_encoder_layer_block["0"][encoder_attn_name]["v"]["kernel"] = t5x_attention_value

        flax_model_encoder_layer_block["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            flax_model_encoder_layer_block["0"][encoder_attn_name]["global_input_layer_norm"][
                "weight"
            ] = t5x_global_layer_norm

        if split_mlp_wi:
            flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
            flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
        else:
            flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi

        flax_model_encoder_layer_block["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo
        flax_model_encoder_layer_block["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm

        flax_model.params["encoder"]["block"][str(layer_index)]["layer"] = flax_model_encoder_layer_block

    # Only for layer 0:
    t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["relative_attention_bias"][
        "embedding"
    ] = t5x_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        t5x_encoder_global_rel_embedding = t5x_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
        flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["global_relative_attention_bias"][
            "embedding"
        ] = t5x_encoder_global_rel_embedding

    # Assigning
    t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"]
    flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers):
        layer_name = f"layers_{str(layer_index)}"

        # Self-Attention
        t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
        t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
        t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
        t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

        # Layer Normalization
        t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
            "scale"
        ]

        # Encoder-Decoder-Attention
        t5x_enc_dec_attention_module = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
        t5x_enc_dec_attention_key = t5x_enc_dec_attention_module["key"]["kernel"]
        t5x_enc_dec_attention_out = t5x_enc_dec_attention_module["out"]["kernel"]
        t5x_enc_dec_attention_query = t5x_enc_dec_attention_module["query"]["kernel"]
        t5x_enc_dec_attention_value = t5x_enc_dec_attention_module["value"]["kernel"]

        # Layer Normalization
        t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

        # MLP
        if split_mlp_wi:
            t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

        t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        flax_model_decoder_layer_block = flax_model.params["decoder"]["block"][str(layer_index)]["layer"]
        flax_model_decoder_layer_block["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key
        flax_model_decoder_layer_block["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out
        flax_model_decoder_layer_block["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query
        flax_model_decoder_layer_block["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value

        flax_model_decoder_layer_block["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm

        flax_model_decoder_layer_block["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key
        flax_model_decoder_layer_block["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out
        flax_model_decoder_layer_block["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query
        flax_model_decoder_layer_block["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value

        flax_model_decoder_layer_block["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm

        if split_mlp_wi:
            flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
            flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
        else:
            flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi

        flax_model_decoder_layer_block["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo

        flax_model_decoder_layer_block["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm

        flax_model.params["decoder"]["block"][str(layer_index)]["layer"] = flax_model_decoder_layer_block

    # Decoder Normalization
    tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"]
    flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm

    # Only for layer 0:
    t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][
        "embedding"
    ] = t5x_decoder_rel_embedding

    # Token Embeddings
    tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"]
    flax_model.params["shared"]["embedding"] = tx5_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in t5x_model["target"]["decoder"]:
        flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"]

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