


'''simple docstring'''
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__          = logging.get_logger(__name__)

UpperCamelCase__          = [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]





def    a__			(   lowerCAmelCase__		)       ->      Optional[Any]:
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        UpperCAmelCase__     :						str						=							k.replace(lowerCAmelCase__    ,   lowerCAmelCase__		)

    if k.startswith('''encoder'''		):
        UpperCAmelCase__     :						Any						=							k.replace('''.attn'''    ,   '''.self_attn'''		)
        UpperCAmelCase__     :						Union[str, Any]						=							k.replace('''norm1'''    ,   '''self_attn_layer_norm'''		)
        UpperCAmelCase__     :						int						=							k.replace('''norm2'''    ,   '''final_layer_norm'''		)
    elif k.startswith('''decoder'''		):
        UpperCAmelCase__     :						Dict						=							k.replace('''norm1'''    ,   '''self_attn_layer_norm'''		)
        UpperCAmelCase__     :						Dict						=							k.replace('''norm2'''    ,   '''encoder_attn_layer_norm'''		)
        UpperCAmelCase__     :						str						=							k.replace('''norm3'''    ,   '''final_layer_norm'''		)
    return k





def    a__			(   lowerCAmelCase__		)       ->      Any:
    UpperCAmelCase__     :						Optional[Any]						=							[
        '''model.encoder.layernorm_embedding.weight''',
        '''model.encoder.layernorm_embedding.bias''',
        '''model.decoder.layernorm_embedding.weight''',
        '''model.decoder.layernorm_embedding.bias''',
    ]
    for k in keys:
        UpperCAmelCase__     :						str						=							sd.pop(lowerCAmelCase__		)
        UpperCAmelCase__     :						int						=							k.replace('''layernorm_embedding'''    ,   '''layer_norm'''		)
        assert new_k not in sd
        UpperCAmelCase__     :						Any						=							v


UpperCamelCase__          = ['''START''']





@torch.no_grad()
def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      List[Any]:
    UpperCAmelCase__     :						Optional[int]						=							torch.load(lowerCAmelCase__    ,   map_location='''cpu'''		)
    UpperCAmelCase__     :						Union[str, Any]						=							model['''model''']
    UpperCAmelCase__     :						Dict						=							BlenderbotConfig.from_json_file(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[str]						=							BlenderbotForConditionalGeneration(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[str]						=							m.model.state_dict().keys()
    UpperCAmelCase__     :						Union[str, Any]						=							[]
    UpperCAmelCase__     :						Union[str, Any]						=							{}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        UpperCAmelCase__     :						Union[str, Any]						=							rename_state_dict_key(lowerCAmelCase__		)
        if new_k not in valid_keys:
            failures.append([k, new_k]		)
        else:
            UpperCAmelCase__     :						Dict						=							v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(lowerCAmelCase__		)
    m.model.load_state_dict(lowerCAmelCase__    ,   strict=lowerCAmelCase__		)
    m.half()
    m.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
  UpperCamelCase__          = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
  parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
  parser.add_argument(
      '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
  )
  UpperCamelCase__          = parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCamelCase_						:     List[Any]	       =							3



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			int:






						'''simple docstring'''

						print("Generating primitive root of p"  )
						while True:
												_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )
												if pow(_UpperCAmelCase ,			2 ,			_UpperCAmelCase  ) == 1:
																		continue
												if pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) == 1:
																		continue
												return g



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			tuple[tuple[int, int, int, int], tuple[int, int]]:






						'''simple docstring'''

						print("Generating prime p..."  )
						_lowercase       :	Optional[int]           =   rabin_miller.generate_large_prime(_UpperCAmelCase  )  # select large prime number.
						_lowercase       :	Dict           =   primitive_root(_UpperCAmelCase  )  # one primitive root on modulo p.
						_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )  # private_key -> have to be greater than 2 for safety.
						_lowercase       :	Tuple           =   cryptomath.find_mod_inverse(pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) ,			_UpperCAmelCase  )

						_lowercase       :	List[Any]           =   (key_size, e_a, e_a, p)
						_lowercase       :	Union[str, Any]           =   (key_size, d)

						return public_key, private_key



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int  )   ->			None:






						'''simple docstring'''

						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()

						_lowercase      ,    _lowercase       :	Optional[Any]           =   generate_key(_UpperCAmelCase  )
						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 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						print("Making key files..."  )
						make_key_files("elgamal" ,			2048  )
						print("Key files generation successful"  )


if __name__ == "__main__":
							main()




