'''simple docstring'''

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()
UpperCamelCase__     :	int				=			logging.get_logger(__name__)

UpperCamelCase__     :	Union[str, Any]				=			'Hello world! cécé herlolip'







def __UpperCamelCase(   _A					:				str  ,			_A					:				str  ,			_A					:				bool    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  FairseqRobertaModel.from_pretrained(_A    )
   roberta.eval()  # disable dropout
   UpperCAmelCase__							:				Tuple   				=  roberta.model.encoder.sentence_encoder
   UpperCAmelCase__							:				Dict   				=  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:
      UpperCAmelCase__							:				Tuple   				=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

   print('''Our RoBERTa config:'''  ,			_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  XLMRobertaXLForSequenceClassification(_A    ) if classification_head else XLMRobertaXLForMaskedLM(_A    )
   model.eval()

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

   UpperCAmelCase__							:				Optional[Any]   				=  roberta_sent_encoder.layer_norm.weight
   UpperCAmelCase__							:				Optional[Any]   				=  roberta_sent_encoder.layer_norm.bias

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

      UpperCAmelCase__							:				RobertaAttention   				=  layer.attention
      UpperCAmelCase__							:				Tuple   				=  roberta_layer.self_attn_layer_norm.weight
      UpperCAmelCase__							:				Optional[Any]   				=  roberta_layer.self_attn_layer_norm.bias

      # self attention
      UpperCAmelCase__							:				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)    )
      )

      UpperCAmelCase__							:				Dict   				=  roberta_layer.self_attn.q_proj.weight
      UpperCAmelCase__							:				Union[str, Any]   				=  roberta_layer.self_attn.q_proj.bias
      UpperCAmelCase__							:				int   				=  roberta_layer.self_attn.k_proj.weight
      UpperCAmelCase__							:				str   				=  roberta_layer.self_attn.k_proj.bias
      UpperCAmelCase__							:				Optional[int]   				=  roberta_layer.self_attn.v_proj.weight
      UpperCAmelCase__							:				Any   				=  roberta_layer.self_attn.v_proj.bias

      # self-attention output
      UpperCAmelCase__							:				BertSelfOutput   				=  layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      UpperCAmelCase__							:				Any   				=  roberta_layer.self_attn.out_proj.weight
      UpperCAmelCase__							:				Any   				=  roberta_layer.self_attn.out_proj.bias

      # this one is final layer norm
      UpperCAmelCase__							:				int   				=  roberta_layer.final_layer_norm.weight
      UpperCAmelCase__							:				Dict   				=  roberta_layer.final_layer_norm.bias

      # intermediate
      UpperCAmelCase__							:				BertIntermediate   				=  layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      UpperCAmelCase__							:				Any   				=  roberta_layer.fca.weight
      UpperCAmelCase__							:				Optional[Any]   				=  roberta_layer.fca.bias

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

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

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

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

   pathlib.Path(_A    ).mkdir(parents=_A  ,			exist_ok=_A    )
   print(F'''Saving model to {pytorch_dump_folder_path}'''    )
   model.save_pretrained(_A    )


if __name__ == "__main__":
  UpperCamelCase__     :	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.'
  )
  UpperCamelCase__     :	Union[str, Any]				=			parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )





'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__     :	Union[str, Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	str				=			{'vocab_file': 'sentencepiece.model'}

UpperCamelCase__     :	int				=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCamelCase__     :	Any				=			{
    'google/rembert': 256,
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[Any]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      List[Any]      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=False					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[UNK]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[PAD]"					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[MASK]"					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         super().__init__(
             do_lower_case=lowerCamelCase_					,remove_space=lowerCamelCase_					,keep_accents=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,cls_token=lowerCamelCase_					,mask_token=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				Union[str, Any]   				=  do_lower_case
         UpperCAmelCase__							:				str   				=  remove_space
         UpperCAmelCase__							:				Optional[Any]   				=  keep_accents
         UpperCAmelCase__							:				List[Any]   				=  vocab_file

         UpperCAmelCase__							:				Dict   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         return len(self.sp_model							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  {self.convert_ids_to_tokens(lowerCamelCase_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




      def __getstate__(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				Any   				=  None
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  d
         UpperCAmelCase__							:				Any   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=False							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.sp_model.EncodeAsPieces(lowerCamelCase_							)
         return pieces




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.sp_model.PieceToId(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         return self.sp_model.IdToPiece(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.sp_model.decode_pieces(lowerCamelCase_							)
         return out_string




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]
         if token_ids_a is None:
            return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



         if already_has_special_tokens:
            if token_ids_a is not None:
               raise ValueError(
                   '''You should not supply a second sequence if the provided sequence of '''
                   '''ids is already formatted with special tokens for the model.'''							)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
            return [1] + ([0] * len(lowerCamelCase_							)) + [1] + ([0] * len(lowerCamelCase_							)) + [1]
         return [1] + ([0] * len(lowerCamelCase_							)) + [1]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]

         if token_ids_a is None:
            return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCamelCase_							):
            copyfile(self.vocab_file					,lowerCamelCase_							)

         return (out_vocab_file,)




