





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


A__:    Any  	=	logging.get_logger(__name__)
logging.set_verbosity_info()



def 				lowerCAmelCase_ (							A_    ,A_):
    if "xprophetnet" in prophetnet_checkpoint_path:
        UpperCamelCase__:	Tuple					=     XLMProphetNetForConditionalGenerationOld.from_pretrained(A_)
        UpperCamelCase__				, UpperCamelCase__:	Tuple					=     XLMProphetNetForConditionalGeneration.from_pretrained(
            A_    ,output_loading_info=A_)
    else:
        UpperCamelCase__:	Any					=     ProphetNetForConditionalGenerationOld.from_pretrained(A_)
        UpperCamelCase__				, UpperCamelCase__:	Optional[Any]					=     ProphetNetForConditionalGeneration.from_pretrained(
            A_    ,output_loading_info=A_)

    UpperCamelCase__:	int					=     ["key_proj", "value_proj", "query_proj"]

    UpperCamelCase__:	Union[str, 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"]:
        UpperCamelCase__:	Optional[int]					=     key.split(".")

        if attributes[0] == "lm_head":
            UpperCamelCase__:	str					=     prophet
            UpperCamelCase__:	List[str]					=     prophet_old
        else:
            UpperCamelCase__:	List[Any]					=     prophet.prophetnet
            UpperCamelCase__:	Any					=     prophet_old.model

        UpperCamelCase__:	Any					=     False
        for attribute in attributes:
            if attribute in mapping:
                UpperCamelCase__:	Tuple					=     mapping[attribute]
                if not hasattr(A_    ,A_) and len(A_) > 0:
                    UpperCamelCase__:	int					=     attribute
            elif hasattr(A_    ,A_):
                UpperCamelCase__:	Optional[int]					=     attribute

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

                elif attribute == "key_proj":
                    UpperCamelCase__:	Any					=     nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
                    UpperCamelCase__:	List[str]					=     nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
                elif attribute == "value_proj":
                    UpperCamelCase__:	Dict					=     nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
                    UpperCamelCase__:	Optional[int]					=     nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
                UpperCamelCase__:	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] == 5_12, "We want 512 position_embeddings."
                UpperCamelCase__:	str					=     nn.Parameter(old_model.embed_positions.weight[:5_12, :])
                UpperCamelCase__:	Optional[int]					=     True
                break

            if attribute.isdigit():
                UpperCamelCase__:	int					=     model[int(A_)]
                UpperCamelCase__:	List[Any]					=     old_model[int(A_)]
            else:
                UpperCamelCase__:	int					=     getattr(A_    ,A_)

                if old_attribute == "":
                    UpperCamelCase__:	Optional[Any]					=     old_model
                else:
                    if not hasattr(A_    ,A_):
                        raise ValueError(F"{old_model} does not have {old_attribute}")
                    UpperCamelCase__:	int					=     getattr(A_    ,A_)

        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(A_)


if __name__ == "__main__":
   A__:    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.'''
   )
   A__:    List[Any]  	=	parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	str					=     [1]
    for i in range(2    ,A_):
        factorials.append(factorials[-1] * i)
    assert 0 <= k < factorials[-1] * n, "k out of bounds"

    UpperCamelCase__:	Optional[int]					=     []
    UpperCamelCase__:	Optional[Any]					=     list(range(A_))

    # Find permutation
    while factorials:
        UpperCamelCase__:	Dict					=     factorials.pop()
        UpperCamelCase__				, UpperCamelCase__:	Optional[int]					=     divmod(A_    ,A_)
        permutation.append(elements[number])
        elements.remove(elements[number])
    permutation.append(elements[0])

    return permutation


if __name__ == "__main__":
   import doctest

   doctest.testmod()

