


'''simple docstring'''

from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt

def _SCREAMING_SNAKE_CASE      (A       )       ->       bool:






       """simple docstring"""







       lowercase__      			= int(number**0.5       )
       return number == sq * sq

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A  , A  , A       )       ->       tuple[int, int]:






       """simple docstring"""







       lowercase__      			= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
       lowercase__      			= x_den * y_den * z_den
       lowercase__      			= gcd(A  , A       )
       top //= hcf
       bottom //= hcf
       return top, bottom

def _SCREAMING_SNAKE_CASE      (A = 35       )       ->       int:






       """simple docstring"""







       lowercase__      			= set()
       lowercase__      			= 42
       lowercase__      			= Fraction(0       )
       lowercase__      			= 42

       for x_num in range(1  , order + 1       ):
              for x_den in range(x_num + 1  , order + 1       ):
                     for y_num in range(1  , order + 1       ):
                            for y_den in range(y_num + 1  , order + 1       ):
                                   # n=1
                                   lowercase__      			= x_num * y_den + x_den * y_num
                                   lowercase__      			= x_den * y_den
                                   lowercase__      			= gcd(A  , A       )
                                   z_num //= hcf
                                   z_den //= hcf
                                   if 0 < z_num < z_den <= order:
                                          lowercase__      			= add_three(
                                              A  , A  , A  , A  , A  , A       )
                                          unique_s.add(A       )

                                   # n=2
                                   lowercase__      			= (
                                       x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                                   )
                                   lowercase__      			= x_den * x_den * y_den * y_den
                                   if is_sq(A       ) and is_sq(A       ):
                                          lowercase__      			= int(sqrt(A       )       )
                                          lowercase__      			= int(sqrt(A       )       )
                                          lowercase__      			= gcd(A  , A       )
                                          z_num //= hcf
                                          z_den //= hcf
                                          if 0 < z_num < z_den <= order:
                                                 lowercase__      			= add_three(
                                                     A  , A  , A  , A  , A  , A       )
                                                 unique_s.add(A       )

                    # n=-1
                                   lowercase__      			= x_num * y_num
                                   lowercase__      			= x_den * y_num + x_num * y_den
                                   lowercase__      			= gcd(A  , A       )
                                   z_num //= hcf
                                   z_den //= hcf
                                   if 0 < z_num < z_den <= order:
                                          lowercase__      			= add_three(
                                              A  , A  , A  , A  , A  , A       )
                                          unique_s.add(A       )

                                   # n=2
                                   lowercase__      			= x_num * x_num * y_num * y_num
                                   lowercase__      			= (
                                       x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                                   )
                                   if is_sq(A       ) and is_sq(A       ):
                                          lowercase__      			= int(sqrt(A       )       )
                                          lowercase__      			= int(sqrt(A       )       )
                                          lowercase__      			= gcd(A  , A       )
                                          z_num //= hcf
                                          z_den //= hcf
                                          if 0 < z_num < z_den <= order:
                                                 lowercase__      			= add_three(
                                                     A  , A  , A  , A  , A  , A       )
                                                 unique_s.add(A       )

       for num, den in unique_s:
              total += Fraction(A  , A       )

       return total.denominator + total.numerator


if __name__ == "__main__":
 print(f"""{solution() = }""")








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




