




def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]=2_81_23					):


      '''simple docstring'''



      lowerCamelCase_							=   [1] * (limit + 1)

      for i in range(2     ,							int(limit**0.5					) + 1					):
            sum_divs[i * i] += i
            for k in range(i + 1     ,							limit // i + 1					):
                  sum_divs[k * i] += k + i

      lowerCamelCase_							=   set()
      lowerCamelCase_							=   0

      for n in range(1     ,							limit + 1					):
            if sum_divs[n] > n:
                  abundants.add(lowercase					)

            if not any((n - a in abundants) for a in abundants					):
                  res += n

      return res


if __name__ == "__main__":
 print(solution())









import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
 raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
lowerCamelCase    :		Tuple			   =       logging.get_logger(__name__)

lowerCamelCase    :		Union[str, Any]			   =       "Hello world! cécé herlolip"

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	bool					):


      '''simple docstring'''



      lowerCamelCase_							=   FairseqRobertaModel.from_pretrained(lowercase					)
      roberta.eval()  # disable dropout
      lowerCamelCase_							=   roberta.model.encoder.sentence_encoder
      lowerCamelCase_							=   XLMRobertaConfig(
          vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,							hidden_size=roberta.cfg.model.encoder_embed_dim     ,							num_hidden_layers=roberta.cfg.model.encoder_layers     ,							num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,							intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,							max_position_embeddings=5_14     ,							type_vocab_size=1     ,							layer_norm_eps=1e-5     ,							)
      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our RoBERTa config:'     ,							lowercase					)

      lowerCamelCase_							=   XLMRobertaXLForSequenceClassification(lowercase					) if classification_head else XLMRobertaXLForMaskedLM(lowercase					)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowerCamelCase_							=   roberta_sent_encoder.embed_tokens.weight
      lowerCamelCase_							=   roberta_sent_encoder.embed_positions.weight
      lowerCamelCase_							=   torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.

      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.weight
      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.bias

      for i in range(config.num_hidden_layers					):
            # Encoder: start of layer
            lowerCamelCase_							=   model.roberta.encoder.layer[i]
            lowerCamelCase_							=   roberta_sent_encoder.layers[i]

            lowerCamelCase_							=   layer.attention
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.bias

            # self attention
            lowerCamelCase_							=   layer.attention.self
            assert (
                roberta_layer.self_attn.k_proj.weight.data.shape
                == roberta_layer.self_attn.q_proj.weight.data.shape
                == roberta_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)					)
            )

            lowerCamelCase_							=   roberta_layer.self_attn.q_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.q_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.bias

            # self-attention output
            lowerCamelCase_							=   layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            lowerCamelCase_							=   roberta_layer.self_attn.out_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            lowerCamelCase_							=   roberta_layer.final_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.final_layer_norm.bias

            # intermediate
            lowerCamelCase_							=   layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCamelCase_							=   roberta_layer.fca.weight
            lowerCamelCase_							=   roberta_layer.fca.bias

            # output
            lowerCamelCase_							=   layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCamelCase_							=   roberta_layer.fca.weight
            lowerCamelCase_							=   roberta_layer.fca.bias
            # end of layer

      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].dense.weight
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].dense.bias
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.weight
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.bias
      else:
            # LM Head
            lowerCamelCase_							=   roberta.model.encoder.lm_head.dense.weight
            lowerCamelCase_							=   roberta.model.encoder.lm_head.dense.bias
            lowerCamelCase_							=   roberta.model.encoder.lm_head.layer_norm.weight
            lowerCamelCase_							=   roberta.model.encoder.lm_head.layer_norm.bias
            lowerCamelCase_							=   roberta.model.encoder.lm_head.weight
            lowerCamelCase_							=   roberta.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      lowerCamelCase_							=   roberta.encode(lowercase					).unsqueeze(0					)  # batch of size 1

      lowerCamelCase_							=   model(lowercase					)[0]
      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'](roberta.extract_features(lowercase					)					)
      else:
            lowerCamelCase_							=   roberta.model(lowercase					)[0]
      print(our_output.shape     ,							their_output.shape					)
      lowerCamelCase_							=   torch.max(torch.abs(our_output - their_output					)					).item()
      print(f"""max_absolute_diff = {max_absolute_diff}"""					)  # ~ 1e-7
      lowerCamelCase_							=   torch.allclose(lowercase     ,							lowercase     ,							atol=1e-3					)
      print('Do both models output the same tensors?'     ,							'🔥' if success else '💩'					)
      if not success:
            raise Exception('Something went wRoNg'					)

      pathlib.Path(lowercase					).mkdir(parents=lowercase     ,							exist_ok=lowercase					)
      print(f"""Saving model to {pytorch_dump_folder_path}"""					)
      model.save_pretrained(lowercase					)


if __name__ == "__main__":
 lowerCamelCase    :		Optional[Any]			   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--classification_head", action="store_true", help="Whether to convert a final classification head."
 )
 lowerCamelCase    :		List[Any]			   =       parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )



