
import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__lowerCAmelCase					:			Optional[int]			      =		logging.get_logger(__name__)
logging.set_verbosity_info()


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Any:
    if "xprophetnet" in prophetnet_checkpoint_path:
        __lowercase		:			int	    =       XLMProphetNetForConditionalGenerationOld.from_pretrained(__lowerCAmelCase	)
        __lowercase    ,      __lowercase		:			int	    =       XLMProphetNetForConditionalGeneration.from_pretrained(
            __lowerCAmelCase	,					output_loading_info=__lowerCAmelCase	)
    else:
        __lowercase		:			List[str]	    =       ProphetNetForConditionalGenerationOld.from_pretrained(__lowerCAmelCase	)
        __lowercase    ,      __lowercase		:			Optional[Any]	    =       ProphetNetForConditionalGeneration.from_pretrained(
            __lowerCAmelCase	,					output_loading_info=__lowerCAmelCase	)

    __lowercase		:			List[str]	    =       ['''key_proj''', '''value_proj''', '''query_proj''']

    __lowercase		:			Optional[Any]	    =       {
        '''self_attn''': '''ngram_self_attn''',
        '''cross_attn''': '''encoder_attn''',
        '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
        '''feed_forward_layer_norm''': '''final_layer_norm''',
        '''feed_forward''': '''''',
        '''intermediate''': '''fc1''',
        '''output''': '''fc2''',
        '''key_proj''': '''k_proj''',
        '''query_proj''': '''q_proj''',
        '''value_proj''': '''v_proj''',
        '''word_embeddings''': '''embed_tokens''',
        '''embeddings_layer_norm''': '''emb_layer_norm''',
        '''relative_pos_embeddings''': '''relative_linear''',
        '''ngram_embeddings''': '''ngram_input_embed''',
        '''position_embeddings''': '''embed_positions''',
    }

    for key in loading_info["missing_keys"]:
        __lowercase		:			List[str]	    =       key.split('''.'''	)

        if attributes[0] == "lm_head":
            __lowercase		:			Union[str, Any]	    =       prophet
            __lowercase		:			Dict	    =       prophet_old
        else:
            __lowercase		:			Optional[Any]	    =       prophet.prophetnet
            __lowercase		:			Any	    =       prophet_old.model

        __lowercase		:			Union[str, Any]	    =       False
        for attribute in attributes:
            if attribute in mapping:
                __lowercase		:			List[str]	    =       mapping[attribute]
                if not hasattr(__lowerCAmelCase	,					__lowerCAmelCase	) and len(__lowerCAmelCase	) > 0:
                    __lowercase		:			Tuple	    =       attribute
            elif hasattr(__lowerCAmelCase	,					__lowerCAmelCase	):
                __lowercase		:			Dict	    =       attribute

            if attribute == "weight":
                assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                __lowercase		:			List[str]	    =       old_model.weight
                logger.info(F'{attribute} is initialized.'	)
                __lowercase		:			Union[str, Any]	    =       True
                break
            elif attribute == "bias":
                assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                __lowercase		:			Tuple	    =       old_model.bias
                logger.info(F'{attribute} is initialized'	)
                __lowercase		:			Any	    =       True
                break
            elif attribute in special_keys and hasattr(__lowerCAmelCase	,					'''in_proj_weight'''	):
                __lowercase		:			Tuple	    =       old_model.in_proj_weight.shape[0] // 3
                __lowercase		:			Any	    =       getattr(__lowerCAmelCase	,					__lowerCAmelCase	)
                param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                if attribute == "query_proj":
                    __lowercase		:			Union[str, Any]	    =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]	)
                    __lowercase		:			str	    =       nn.Parameter(old_model.in_proj_bias[:embed_dim]	)

                elif attribute == "key_proj":
                    __lowercase		:			List[str]	    =       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]	)
                    __lowercase		:			Any	    =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]	)
                elif attribute == "value_proj":
                    __lowercase		:			Dict	    =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]	)
                    __lowercase		:			str	    =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]	)
                __lowercase		:			str	    =       True
                break
            elif attribute == "position_embeddings":
                assert (
                    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                ), "Hidden size has to match"
                assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                __lowercase		:			Optional[int]	    =       nn.Parameter(old_model.embed_positions.weight[:512, :]	)
                __lowercase		:			int	    =       True
                break

            if attribute.isdigit():
                __lowercase		:			int	    =       model[int(__lowerCAmelCase	)]
                __lowercase		:			Optional[int]	    =       old_model[int(__lowerCAmelCase	)]
            else:
                __lowercase		:			Dict	    =       getattr(__lowerCAmelCase	,					__lowerCAmelCase	)

                if old_attribute == "":
                    __lowercase		:			List[str]	    =       old_model
                else:
                    if not hasattr(__lowerCAmelCase	,					__lowerCAmelCase	):
                        raise ValueError(F'{old_model} does not have {old_attribute}'	)
                    __lowercase		:			List[Any]	    =       getattr(__lowerCAmelCase	,					__lowerCAmelCase	)

        if not is_key_init:
            raise ValueError(F'{key} was not correctly initialized!'	)

    print(F'Saving model to {pytorch_dump_folder_path}'	)
    prophet.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
 __lowerCAmelCase					:			List[str]			      =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 __lowerCAmelCase					:			Union[str, Any]			      =		parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
 from .deepspeed import (
     DeepSpeedEngineWrapper,
     DeepSpeedOptimizerWrapper,
     DeepSpeedSchedulerWrapper,
     DummyOptim,
     DummyScheduler,
     HfDeepSpeedConfig,
 )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers


