





from math import factorial

_lowerCamelCase      							=   {str(digit): factorial(digit) for digit in range(10)}




def  __UpperCAmelCase(   lowercase_   ):
       if not isinstance(lowercase_			,     lowercase_   ):
              raise TypeError('''Parameter number must be int'''   )

       if number < 0:
              raise ValueError('''Parameter number must be greater than or equal to 0'''   )

       # Converts number in string to iterate on its digits and adds its factorial.
       return sum(DIGIT_FACTORIAL[digit] for digit in str(lowercase_   )   )




def  __UpperCAmelCase(   lowercase_ = 60			,     lowercase_ = 1_00_00_00   ):

       if not isinstance(lowercase_			,     lowercase_   ) or not isinstance(lowercase_			,     lowercase_   ):
              raise TypeError('''Parameters chain_length and number_limit must be int'''   )

       if chain_length <= 0 or number_limit <= 0:
              raise ValueError(
                  '''Parameters chain_length and number_limit must be greater than 0'''   )

       # the counter for the chains with the exact desired length
       _lowerCamelCase				:  List[Any]						=		0
       # the cached sizes of the previous chains
       _lowerCamelCase				:  dict[int, int]						=		{}

       for start_chain_element in range(1			,     lowercase_   ):
              # The temporary set will contain the elements of the chain
              _lowerCamelCase				:  List[Any]						=		set()
              _lowerCamelCase				:  List[Any]						=		0

              # Stop computing the chain when you find a cached size, a repeating item or the
              # length is greater then the desired one.
              _lowerCamelCase				:  Any						=		start_chain_element
              while (
                  chain_element not in chain_sets_lengths
                  and chain_element not in chain_set
                  and chain_set_length <= chain_length
              ):
                     chain_set.add(lowercase_   )
                     chain_set_length += 1
                     _lowerCamelCase				:  Dict						=		digit_factorial_sum(lowercase_   )

              if chain_element in chain_sets_lengths:
                     chain_set_length += chain_sets_lengths[chain_element]

              _lowerCamelCase				:  Optional[Any]						=		chain_set_length

              # If chain contains the exact amount of elements increase the counter
              if chain_set_length == chain_length:
                     chains_counter += 1

       return chains_counter


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(F'''{solution()}''')








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)

