
"""simple docstring"""



from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

UpperCamelCase__          =	typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
UpperCamelCase__          =	typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    return np.sqrt(np.sum((np.asarray(_snake_case ) - np.asarray(_snake_case )) ** 2 ) )
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    return sum((va - va) ** 2 for va, va in zip(_snake_case    ,_snake_case ) ) ** (1 / 2)


if __name__ == "__main__":
    def 					lowerCamelCase   (     ):
        from timeit import timeit

        print('Without Numpy' )
        print(
            timeit(
                'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'    ,number=10000    ,globals=globals()    ,) )
        print('With Numpy' )
        print(
            timeit(
                'euclidean_distance([1, 2, 3], [4, 5, 6])'    ,number=10000    ,globals=globals()    ,) )

    benchmark()



"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      int     							=					[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      List[str]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Any     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Any     							=					mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase__	:      Optional[int]     							=					mam_aaa['model']
    remove_ignore_keys_(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      List[str]     							=					MaMaaaConfig(
        vocab_size=_snake_case    ,max_position_embeddings=1024    ,encoder_layers=args.encoder_layers    ,decoder_layers=args.decoder_layers    ,encoder_attention_heads=args.encoder_attention_heads    ,decoder_attention_heads=args.decoder_attention_heads    ,encoder_ffn_dim=args.encoder_ffn_embed_dim    ,decoder_ffn_dim=args.decoder_ffn_embed_dim    ,d_model=args.encoder_embed_dim    ,encoder_layerdrop=args.encoder_layerdrop    ,decoder_layerdrop=args.decoder_layerdrop    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,)

    UpperCAmelCase__	:      Optional[int]     							=					state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					MaMaaaForConditionalGeneration(_snake_case )
    model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    UpperCamelCase__          =	parser.parse_args()
    UpperCamelCase__          =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)

