


'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)










'''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


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =			{'vocab_file': 'sentencepiece.model'}

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

SCREAMING_SNAKE_CASE_      =			{
    'google/rembert': 2_56,
}


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Tuple												=VOCAB_FILES_NAMES
  a_       :Tuple												=PRETRAINED_VOCAB_FILES_MAP
  a_       :Optional[int]												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

  def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple=False				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=True				,							SCREAMING_SNAKE_CASE__     :							int="[CLS]"				,							SCREAMING_SNAKE_CASE__     :							Optional[int]="[SEP]"				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]="[UNK]"				,							SCREAMING_SNAKE_CASE__     :							Any="[SEP]"				,							SCREAMING_SNAKE_CASE__     :							Dict="[PAD]"				,							SCREAMING_SNAKE_CASE__     :							Dict="[CLS]"				,							SCREAMING_SNAKE_CASE__     :							Dict="[MASK]"				,							**SCREAMING_SNAKE_CASE__     :							List[str]				,							):





       '''simple docstring'''

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

       __a       =   do_lower_case
       __a       =   remove_space
       __a       =   keep_accents
       __a       =   vocab_file

       __a       =   spm.SentencePieceProcessor()
       self.sp_model.Load(SCREAMING_SNAKE_CASE__						)

  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       return len(self.sp_model						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab

  def __getstate__(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   self.__dict__.copy()
       __a       =   None
       return state

  def __setstate__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       __a       =   d
       __a       =   spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[str]=False						):





       '''simple docstring'''

       __a       =   self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE__						)
       return pieces

  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       __a       =   self.sp_model.decode_pieces(SCREAMING_SNAKE_CASE__						)
       return out_string

  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							Optional[List[int]] = None						):





       '''simple docstring'''

       __a       =   [self.sep_token_id]
       __a       =   [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 __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							Optional[List[int]] = None				,							SCREAMING_SNAKE_CASE__     :							bool = False						):





       '''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(SCREAMING_SNAKE_CASE__						)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__						)) + [1]
       return [1] + ([0] * len(SCREAMING_SNAKE_CASE__						)) + [1]

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							Optional[List[int]] = None						):





       '''simple docstring'''

       __a       =   [self.sep_token_id]
       __a       =   [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 __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = None						):





       '''simple docstring'''

       if not os.path.isdir(SCREAMING_SNAKE_CASE__						):
            logger.error("""Vocabulary path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__						)						)
            return
       __a       =   os.path.join(
           SCREAMING_SNAKE_CASE__				,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

       return (out_vocab_file,)






