import math

from numpy import inf
from scipy.integrate import quad



def lowerCamelCase_  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if num <= 0:
        raise ValueError('''math domain error'''     )

    return quad(SCREAMING_SNAKE_CASE  ,      0  ,      SCREAMING_SNAKE_CASE  ,      args=(SCREAMING_SNAKE_CASE)     )[0]



def lowerCamelCase_  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    return math.pow(SCREAMING_SNAKE_CASE  ,      z - 1     ) * math.exp(-x     )


if __name__ == "__main__":
    from doctest import testmod

    testmod()






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[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(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      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					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.shared     )

    return model


if __name__ == "__main__":
    __lowercase										= 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.''')
    __lowercase										= parser.parse_args()
    __lowercase										= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





