




from __future__ import annotations

import math
from collections.abc import Callable




def       lowerCAmelCase_     (		lowercase:							Callable[[int | float], int | float]  ,					lowercase:							int | float  ,					lowercase:							int | float  ,					lowercase:							int = 100  ,					)    ->				float:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= x_start
 _UpperCamelCase:     Tuple							= fnc(lowercase		)
 _UpperCamelCase:     List[str]							= 0.0

 for _ in range(lowercase		):
  # Approximates curve as a sequence of linear lines and sums their length
  _UpperCamelCase:     Optional[int]							= (x_end - x_start) / steps + xa
  _UpperCamelCase:     Any							= fnc(lowercase		)
  length += math.hypot(xa - xa  ,					fxa - fxa		)

  # Increment step
  _UpperCamelCase:     int							= xa
  _UpperCamelCase:     Optional[Any]							= fxa

 return length


if __name__ == "__main__":




  def       lowerCAmelCase_     (		lowercase:							Optional[int]		)    ->				int:







   '''simple docstring'''
   return math.sin(10 * x		)

  print('''f(x) = sin(10 * x)''')
  print('''The length of the curve from x = -10 to x = 10 is:''')
  UpperCAmelCase_			    =							1_0
  while i <= 1_0_0_0_0_0:
    print(f"""With {i} steps: {line_length(f, -1_0, 1_0, i)}""")
    i *= 1_0
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 _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(lowercase  ,					lowercase		)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= emb.weight.shape
 _UpperCamelCase:     int							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Any							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							Optional[Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     Any							= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 _UpperCamelCase:     List[Any]							= mam_aaa['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Tuple							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     List[Any]							= MaMaaaConfig(
     vocab_size=lowercase  ,					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:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MaMaaaForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 _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)