
"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Tuple     							=					[
        '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__	:      Tuple     							=					list(s_dict.keys() )
    for key in keys:
        if "transformer_layers" in key:
            UpperCAmelCase__	:      int     							=					s_dict.pop(_snake_case )
        elif "subsample" in key:
            UpperCAmelCase__	:      Union[str, Any]     							=					s_dict.pop(_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      Optional[Any]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Tuple     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      List[str]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Union[str, Any]     							=					mam_aaa['args']
    UpperCAmelCase__	:      List[Any]     							=					mam_aaa['model']
    UpperCAmelCase__	:      str     							=					state_dict['decoder.output_projection.weight']

    remove_ignore_keys_(_snake_case )
    rename_keys(_snake_case )

    UpperCAmelCase__	:      Union[str, Any]     							=					state_dict['decoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      Optional[int]     							=					args.share_decoder_input_output_embed

    UpperCAmelCase__	:      Tuple     							=					[int(_snake_case ) for i in args.conv_kernel_sizes.split(',' )]
    UpperCAmelCase__	:      List[Any]     							=					SpeechaTextConfig(
        vocab_size=_snake_case    ,max_source_positions=args.max_source_positions    ,max_target_positions=args.max_target_positions    ,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    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,num_conv_layers=len(_snake_case )    ,conv_channels=args.conv_channels    ,conv_kernel_sizes=_snake_case    ,input_feat_per_channel=args.input_feat_per_channel    ,input_channels=args.input_channels    ,tie_word_embeddings=_snake_case    ,num_beams=5    ,max_length=200    ,use_cache=_snake_case    ,decoder_start_token_id=2    ,early_stopping=_snake_case    ,)

    UpperCAmelCase__	:      Optional[Any]     							=					SpeechaTextForConditionalGeneration(_snake_case )
    UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    if len(_snake_case ) > 0 and not set(_snake_case ) <= {
        "encoder.embed_positions.weights",
        "decoder.embed_positions.weights",
    }:
        raise ValueError(
            'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
            F''' but all the following weights are missing {missing}''' )

    if tie_embeds:
        UpperCAmelCase__	:      Any     							=					make_linear_from_emb(model.model.decoder.embed_tokens )
    else:
        UpperCAmelCase__	:      Union[str, Any]     							=					lm_head_weights

    model.save_pretrained(_snake_case )


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    UpperCamelCase__          =	parser.parse_args()
    convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)



"""simple docstring"""



import random
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Tuple     							=					num - 1
    UpperCAmelCase__	:      Dict     							=					0

    while s % 2 == 0:
        UpperCAmelCase__	:      Optional[int]     							=					s // 2
        t += 1

    for _ in range(5 ):
        UpperCAmelCase__	:      Optional[int]     							=					random.randrange(2    ,num - 1 )
        UpperCAmelCase__	:      List[str]     							=					pow(_snake_case    ,_snake_case    ,_snake_case )
        if v != 1:
            UpperCAmelCase__	:      List[Any]     							=					0
            while v != (num - 1):
                if i == t - 1:
                    return False
                else:
                    UpperCAmelCase__	:      Optional[Any]     							=					i + 1
                    UpperCAmelCase__	:      Any     							=					(v**2) % num
    return True
def 					lowerCamelCase   (     _snake_case ):
    if num < 2:
        return False

    UpperCAmelCase__	:      str     							=					[
        2,
        3,
        5,
        7,
        11,
        13,
        17,
        19,
        23,
        29,
        31,
        37,
        41,
        43,
        47,
        53,
        59,
        61,
        67,
        71,
        73,
        79,
        83,
        89,
        97,
        101,
        103,
        107,
        109,
        113,
        127,
        131,
        137,
        139,
        149,
        151,
        157,
        163,
        167,
        173,
        179,
        181,
        191,
        193,
        197,
        199,
        211,
        223,
        227,
        229,
        233,
        239,
        241,
        251,
        257,
        263,
        269,
        271,
        277,
        281,
        283,
        293,
        307,
        311,
        313,
        317,
        331,
        337,
        347,
        349,
        353,
        359,
        367,
        373,
        379,
        383,
        389,
        397,
        401,
        409,
        419,
        421,
        431,
        433,
        439,
        443,
        449,
        457,
        461,
        463,
        467,
        479,
        487,
        491,
        499,
        503,
        509,
        521,
        523,
        541,
        547,
        557,
        563,
        569,
        571,
        577,
        587,
        593,
        599,
        601,
        607,
        613,
        617,
        619,
        631,
        641,
        643,
        647,
        653,
        659,
        661,
        673,
        677,
        683,
        691,
        701,
        709,
        719,
        727,
        733,
        739,
        743,
        751,
        757,
        761,
        769,
        773,
        787,
        797,
        809,
        811,
        821,
        823,
        827,
        829,
        839,
        853,
        857,
        859,
        863,
        877,
        881,
        883,
        887,
        907,
        911,
        919,
        929,
        937,
        941,
        947,
        953,
        967,
        971,
        977,
        983,
        991,
        997,
    ]

    if num in low_primes:
        return True

    for prime in low_primes:
        if (num % prime) == 0:
            return False

    return rabin_miller(_snake_case )
def 					lowerCamelCase   (     _snake_case = 1024 ):
    while True:
        UpperCAmelCase__	:      List[Any]     							=					random.randrange(2 ** (keysize - 1)    ,2 ** (keysize) )
        if is_prime_low_num(_snake_case ):
            return num


if __name__ == "__main__":
    UpperCamelCase__          =	generate_large_prime()
    print(('Prime number:', num))
    print(('is_prime_low_num:', is_prime_low_num(num)))

