





from __future__ import annotations

import math




def  __UpperCAmelCase(   lowercase_   ):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5			,     int(math.sqrt(lowercase_   ) + 1   )			,     6   ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  str						=		str(lowercase_   )
       _lowerCamelCase				:  int						=		[n]
       for i in range(1			,     len(lowercase_   )   ):
              list_nums.append(int(str_num[i:]   )   )
              list_nums.append(int(str_num[:-i]   )   )
       return list_nums




def  __UpperCAmelCase(   lowercase_   ):
       if len(str(lowercase_   )   ) > 3:
              if not is_prime(int(str(lowercase_   )[-3:]   )   ) or not is_prime(int(str(lowercase_   )[:3]   )   ):
                     return False
       return True




def  __UpperCAmelCase(   lowercase_ = 11   ):
       _lowerCamelCase				:  list[int]						=		[]
       _lowerCamelCase				:  Dict						=		13
       while len(lowercase_   ) != count:
              if validate(lowercase_   ):
                     _lowerCamelCase				:  str						=		list_truncated_nums(lowercase_   )
                     if all(is_prime(lowercase_   ) for i in list_nums   ):
                            list_truncated_primes.append(lowercase_   )
              num += 2
       return list_truncated_primes




def  __UpperCAmelCase(   ):
       return sum(compute_truncated_primes(11   )   )


if __name__ == "__main__":
       print(F'''{sum(compute_truncated_primes(11)) = }''')








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)

