
"""simple docstring"""


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

_lowercase							      = 3






def 					_snake_case (						snake_case__      :   int   ):
	print('Generating primitive root of p'   )
	while True:
		A        =      random.randrange(3		,					snake_case__   )
		if pow(snake_case__		,					2		,					snake_case__   ) == 1:
			continue
		if pow(snake_case__		,					snake_case__		,					snake_case__   ) == 1:
			continue
		return g






def 					_snake_case (						snake_case__      :   int   ):
	print('Generating prime p...'   )
	A        =      rabin_miller.generate_large_prime(snake_case__   )  # select large prime number.
	A        =      primitive_root(snake_case__   )  # one primitive root on modulo p.
	A        =      random.randrange(3		,					snake_case__   )  # private_key -> have to be greater than 2 for safety.
	A        =      cryptomath.find_mod_inverse(pow(snake_case__		,					snake_case__		,					snake_case__   )		,					snake_case__   )

	A        =      (key_size, e_a, e_a, p)
	A        =      (key_size, d)

	return public_key, private_key






def 					_snake_case (						snake_case__      :   str		,					snake_case__      :   int   ):
	if os.path.exists(F'{name}_pubkey.txt'   ) or os.path.exists(F'{name}_privkey.txt'   ):
		print('\nWARNING:'   )
		print(
		    F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
		    'Use a different name or delete these files and re-run this program.'   )
		sys.exit()

	A      ,					A        =      generate_key(snake_case__   )
	print(F'\nWriting public key to file {name}_pubkey.txt...'   )
	with open(F'{name}_pubkey.txt'		,					'w'   ) as fo:
		fo.write(F'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'   )

	print(F'Writing private key to file {name}_privkey.txt...'   )
	with open(F'{name}_privkey.txt'		,					'w'   ) as fo:
		fo.write(F'{private_key[0]},{private_key[1]}'   )






def 					_snake_case (						):
	print('Making key files...'   )
	make_key_files('elgamal'		,					2048   )
	print('Key files generation successful'   )


if __name__ == "__main__":
		main()
'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """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      UpperCamelCase_						(					snake_case_       :       Optional[Any]      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						emb.weight.shape
 __lowerCAmelCase											=						nn.Linear(snake_case_						,		snake_case_						,		bias=snake_case_      )
 __lowerCAmelCase											=						emb.weight.data
 return lin_layer







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowerCAmelCase											=						mam_aaa["""model"""]
 remove_ignore_keys_(snake_case_      )
 __lowerCAmelCase											=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase											=						MaMaaaConfig(
     vocab_size=snake_case_						,		max_position_embeddings=10_24						,		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"""						,		)

 __lowerCAmelCase											=						state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase											=						MaMaaaForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 __lowerCAmelCase											=						make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
 _A			:      Union[str, Any]					      =				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.''')
 _A			:      str					      =				parser.parse_args()
 _A			:      Optional[int]					      =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






