

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


UpperCamelCase__  :      Dict			=    logging.get_logger(__name__)
logging.set_verbosity_info()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    if "xprophetnet" in prophetnet_checkpoint_path:
        SCREAMING_SNAKE_CASE_									=						XLMProphetNetForConditionalGenerationOld.from_pretrained(_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						XLMProphetNetForConditionalGeneration.from_pretrained(
            _SCREAMING_SNAKE_CASE ,   output_loading_info=_SCREAMING_SNAKE_CASE       )
    else:
        SCREAMING_SNAKE_CASE_									=						ProphetNetForConditionalGenerationOld.from_pretrained(_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						ProphetNetForConditionalGeneration.from_pretrained(
            _SCREAMING_SNAKE_CASE ,   output_loading_info=_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						['key_proj', 'value_proj', 'query_proj']

    SCREAMING_SNAKE_CASE_									=						{
        '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"]:
        SCREAMING_SNAKE_CASE_									=						key.split('.'       )

        if attributes[0] == "lm_head":
            SCREAMING_SNAKE_CASE_									=						prophet
            SCREAMING_SNAKE_CASE_									=						prophet_old
        else:
            SCREAMING_SNAKE_CASE_									=						prophet.prophetnet
            SCREAMING_SNAKE_CASE_									=						prophet_old.model

        SCREAMING_SNAKE_CASE_									=						False
        for attribute in attributes:
            if attribute in mapping:
                SCREAMING_SNAKE_CASE_									=						mapping[attribute]
                if not hasattr(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ) and len(_SCREAMING_SNAKE_CASE       ) > 0:
                    SCREAMING_SNAKE_CASE_									=						attribute
            elif hasattr(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
                SCREAMING_SNAKE_CASE_									=						attribute

            if attribute == "weight":
                assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                SCREAMING_SNAKE_CASE_									=						old_model.weight
                logger.info(f"""{attribute} is initialized."""       )
                SCREAMING_SNAKE_CASE_									=						True
                break
            elif attribute == "bias":
                assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                SCREAMING_SNAKE_CASE_									=						old_model.bias
                logger.info(f"""{attribute} is initialized"""       )
                SCREAMING_SNAKE_CASE_									=						True
                break
            elif attribute in special_keys and hasattr(_SCREAMING_SNAKE_CASE ,   'in_proj_weight'       ):
                SCREAMING_SNAKE_CASE_									=						old_model.in_proj_weight.shape[0] // 3
                SCREAMING_SNAKE_CASE_									=						getattr(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                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":
                    SCREAMING_SNAKE_CASE_									=						nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
                    SCREAMING_SNAKE_CASE_									=						nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

                elif attribute == "key_proj":
                    SCREAMING_SNAKE_CASE_									=						nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
                    SCREAMING_SNAKE_CASE_									=						nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
                elif attribute == "value_proj":
                    SCREAMING_SNAKE_CASE_									=						nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
                    SCREAMING_SNAKE_CASE_									=						nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
                SCREAMING_SNAKE_CASE_									=						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."
                SCREAMING_SNAKE_CASE_									=						nn.Parameter(old_model.embed_positions.weight[:512, :]       )
                SCREAMING_SNAKE_CASE_									=						True
                break

            if attribute.isdigit():
                SCREAMING_SNAKE_CASE_									=						model[int(_SCREAMING_SNAKE_CASE       )]
                SCREAMING_SNAKE_CASE_									=						old_model[int(_SCREAMING_SNAKE_CASE       )]
            else:
                SCREAMING_SNAKE_CASE_									=						getattr(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

                if old_attribute == "":
                    SCREAMING_SNAKE_CASE_									=						old_model
                else:
                    if not hasattr(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""       )
                    SCREAMING_SNAKE_CASE_									=						getattr(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

        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(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       UpperCamelCase__  :      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."
       )
       UpperCamelCase__  :      Any			=    parser.parse_args()
       convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    for n in range(1 ,   1_000_000       ):
        yield n * (n + 1) // 2


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						1
    SCREAMING_SNAKE_CASE_									=						2
    while i * i <= n:
        SCREAMING_SNAKE_CASE_									=						0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    return next(i for i in triangle_number_generator() if count_divisors(_SCREAMING_SNAKE_CASE       ) > 500       )


if __name__ == "__main__":
       print(solution())


