


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
   parser.add_argument('--model_name', default='roberta-large', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   _A			=						parser.parse_args()

   if args.model_type == "roberta":
      _A			=						RobertaForMaskedLM.from_pretrained(args.model_name)
      _A			=						'roberta'
   elif args.model_type == "gpt2":
      _A			=						GPTaLMHeadModel.from_pretrained(args.model_name)
      _A			=						'transformer'

   _A			=						model.state_dict()
   _A			=						{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         _A			=						state_dict[f"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         _A			=						f"""{prefix}.embeddings.{w}.weight"""
         _A			=						state_dict[param_name]
      for w in ["weight", "bias"]:
         _A			=						f"""{prefix}.embeddings.LayerNorm.{w}"""
         _A			=						state_dict[param_name]

    # Transformer Blocks #
   _A			=						0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               _A			=						state_dict[
                   f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         _A			=						state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               _A			=						state_dict[
                   f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         _A			=						state_dict[f"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            _A			=						state_dict[f"""lm_head.dense.{w}"""]
            _A			=						state_dict[f"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         _A			=						state_dict[f"""{prefix}.ln_f.{w}"""]
      _A			=						state_dict['lm_head.weight']

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)









import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812

def 						_UpperCAmelCase    (      ):
     print('Making key files...'    )
     make_key_files('rsa'      ,					10_24    )
     print('Key files generation successful.'    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     print('Generating prime p...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     print('Generating prime q...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=p * q

     print('Generating e that is relatively prime to (p - 1) * (q - 1)...'    )
     while True:
          __UpperCamelCase   							=random.randrange(2 ** (key_size - 1)      ,					2 ** (key_size)    )
          if cryptoMath.gcd(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    ) == 1:
               break

     print('Calculating d that is mod inverse of e...'    )
     __UpperCamelCase   							=cryptoMath.find_mod_inverse(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    )

     __UpperCamelCase   							=(n, e)
     __UpperCamelCase   							=(n, d)
     return (public_key, private_key)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_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()

     __UpperCamelCase							,				__UpperCamelCase   							=generate_key(SCREAMING_SNAKE_CASE__    )
     print(F'\nWriting public key to file {name}_pubkey.txt...'    )
     with open(F'{name}_pubkey.txt'      ,					'w'    ) as out_file:
          out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'    )

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


if __name__ == "__main__":
   main()





