



"""simple docstring"""






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   lowerCAmelCase__      =    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''')
   lowerCAmelCase__      =    parser.parse_args()

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

   lowerCAmelCase__      =    model.state_dict()
   lowerCAmelCase__      =    {}

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

    # Transformer Blocks #
   lowerCAmelCase__      =    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"]:
               lowerCAmelCase__      =    state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         lowerCAmelCase__      =    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"]:
               lowerCAmelCase__      =    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"]:
         lowerCAmelCase__      =    state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            lowerCAmelCase__      =    state_dict[F"""lm_head.dense.{w}"""]
            lowerCAmelCase__      =    state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         lowerCAmelCase__      =    state_dict[F"""{prefix}.ln_f.{w}"""]
      lowerCAmelCase__      =    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)







"""simple docstring"""






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return 1 if digit in (0, 1) else (digit * factorial(digit - 1						))






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    str         =			0
       _lowerCamelCase						:    Any         =			number
       while duplicate > 0:
              _lowerCamelCase      ,   _lowerCamelCase						:    Union[str, Any]         =			divmod(A_,    10						)
              fact_sum += factorial(A_						)
       return fact_sum == number


if __name__ == "__main__":
   print('''Program to check whether a number is a Krisnamurthy Number or not.''')
   lowerCAmelCase__      =    int(input('''Enter number: ''').strip())
   print(
       F"""{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number."""
   )


