

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()
snake_case_        =			logging.get_logger(__name__)

snake_case_        =			'''Hello world! cécé herlolip'''

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   bool      ):



     '''simple docstring'''





     lowercase__   :       Any				=	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
     roberta.eval()  # disable dropout
     lowercase__   :       Optional[int]				=	roberta.model.encoder.sentence_encoder
     lowercase__   :       Union[str, Any]				=	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=514     ,					type_vocab_size=1     ,					layer_norm_eps=1E-5     ,					)
     if classification_head:
          lowercase__   :       List[str]				=	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

     print('Our RoBERTa config:'     ,					SCREAMING_SNAKE_CASE_      )

     lowercase__   :       Any				=	XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE_      ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE_      )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     lowercase__   :       List[Any]				=	roberta_sent_encoder.embed_tokens.weight
     lowercase__   :       Optional[Any]				=	roberta_sent_encoder.embed_positions.weight
     lowercase__   :       Tuple				=	torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.

     lowercase__   :       Optional[int]				=	roberta_sent_encoder.layer_norm.weight
     lowercase__   :       List[Any]				=	roberta_sent_encoder.layer_norm.bias

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

          lowercase__   :       RobertaAttention				=	layer.attention
          lowercase__   :       str				=	roberta_layer.self_attn_layer_norm.weight
          lowercase__   :       Any				=	roberta_layer.self_attn_layer_norm.bias

          # self attention
          lowercase__   :       BertSelfAttention				=	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)      )
          )

          lowercase__   :       List[Any]				=	roberta_layer.self_attn.q_proj.weight
          lowercase__   :       Any				=	roberta_layer.self_attn.q_proj.bias
          lowercase__   :       Optional[Any]				=	roberta_layer.self_attn.k_proj.weight
          lowercase__   :       Tuple				=	roberta_layer.self_attn.k_proj.bias
          lowercase__   :       str				=	roberta_layer.self_attn.v_proj.weight
          lowercase__   :       Optional[int]				=	roberta_layer.self_attn.v_proj.bias

          # self-attention output
          lowercase__   :       BertSelfOutput				=	layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          lowercase__   :       Tuple				=	roberta_layer.self_attn.out_proj.weight
          lowercase__   :       Optional[Any]				=	roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          lowercase__   :       Dict				=	roberta_layer.final_layer_norm.weight
          lowercase__   :       Tuple				=	roberta_layer.final_layer_norm.bias

          # intermediate
          lowercase__   :       BertIntermediate				=	layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          lowercase__   :       Dict				=	roberta_layer.fca.weight
          lowercase__   :       Dict				=	roberta_layer.fca.bias

          # output
          lowercase__   :       BertOutput				=	layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          lowercase__   :       Optional[int]				=	roberta_layer.fca.weight
          lowercase__   :       Optional[Any]				=	roberta_layer.fca.bias
          # end of layer

     if classification_head:
          lowercase__   :       Union[str, Any]				=	roberta.model.classification_heads['mnli'].dense.weight
          lowercase__   :       List[str]				=	roberta.model.classification_heads['mnli'].dense.bias
          lowercase__   :       Optional[int]				=	roberta.model.classification_heads['mnli'].out_proj.weight
          lowercase__   :       Optional[Any]				=	roberta.model.classification_heads['mnli'].out_proj.bias
     else:
          # LM Head
          lowercase__   :       str				=	roberta.model.encoder.lm_head.dense.weight
          lowercase__   :       List[Any]				=	roberta.model.encoder.lm_head.dense.bias
          lowercase__   :       List[Any]				=	roberta.model.encoder.lm_head.layer_norm.weight
          lowercase__   :       str				=	roberta.model.encoder.lm_head.layer_norm.bias
          lowercase__   :       Optional[Any]				=	roberta.model.encoder.lm_head.weight
          lowercase__   :       Union[str, Any]				=	roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     lowercase__   :       torch.Tensor				=	roberta.encode(SCREAMING_SNAKE_CASE_      ).unsqueeze(0      )  # batch of size 1

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

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


if __name__ == "__main__":
       snake_case_        =			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.'''
       )
       snake_case_        =			parser.parse_args()
       convert_xlm_roberta_xl_checkpoint_to_pytorch(
           args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )




from __future__ import annotations

import math

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     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(SCREAMING_SNAKE_CASE_      ) + 1      )     ,					6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       int				=	str(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Union[str, Any]				=	[n]
     for i in range(1     ,					len(SCREAMING_SNAKE_CASE_      )      ):
          list_nums.append(int(str_num[i:]      )      )
          list_nums.append(int(str_num[:-i]      )      )
     return list_nums

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 11      ):



     '''simple docstring'''





     lowercase__   :       list[int]				=	[]
     lowercase__   :       Tuple				=	13
     while len(SCREAMING_SNAKE_CASE_      ) != count:
          if validate(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	list_truncated_nums(SCREAMING_SNAKE_CASE_      )
               if all(is_prime(SCREAMING_SNAKE_CASE_      ) for i in list_nums      ):
                    list_truncated_primes.append(SCREAMING_SNAKE_CASE_      )
          num += 2
     return list_truncated_primes

def      snake_case__      (   ):



     '''simple docstring'''





     return sum(compute_truncated_primes(11      )      )


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

