



'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase      						=	{
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCAmelCase      						=	{
    '''yjernite/retribert-base-uncased''': 512,
}


_lowerCAmelCase      						=	{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Union[str, Any]				=      VOCAB_FILES_NAMES
  __lowercase							:      int				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Dict				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:      str				=      PRETRAINED_INIT_CONFIGURATION
  __lowercase							:      str				=      RetriBertTokenizer
  __lowercase							:      str				=      ['''input_ids''', '''attention_mask''']




  def __init__(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True  ,__UpperCAmelCase="[UNK]"  ,__UpperCAmelCase="[SEP]"  ,__UpperCAmelCase="[PAD]"  ,__UpperCAmelCase="[CLS]"  ,__UpperCAmelCase="[MASK]"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		Tuple:
      super().__init__(
          __UpperCAmelCase  ,tokenizer_file=__UpperCAmelCase  ,do_lower_case=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,sep_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,cls_token=__UpperCAmelCase  ,mask_token=__UpperCAmelCase  ,tokenize_chinese_chars=__UpperCAmelCase  ,strip_accents=__UpperCAmelCase  ,**__UpperCAmelCase  ,)

      lowerCAmelCase__ :  Optional[int]							      =					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
      if (
          normalizer_state.get("""lowercase"""  ,__UpperCAmelCase      ) != do_lower_case
          or normalizer_state.get("""strip_accents"""  ,__UpperCAmelCase      ) != strip_accents
          or normalizer_state.get("""handle_chinese_chars"""  ,__UpperCAmelCase      ) != tokenize_chinese_chars
      ):
          lowerCAmelCase__ :  Optional[Any]							      =					getattr(__UpperCAmelCase  ,normalizer_state.pop("""type"""      )      )
          lowerCAmelCase__ :  Union[str, Any]							      =					do_lower_case
          lowerCAmelCase__ :  List[Any]							      =					strip_accents
          lowerCAmelCase__ :  Optional[int]							      =					tokenize_chinese_chars
          lowerCAmelCase__ :  Optional[int]							      =					normalizer_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					do_lower_case




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=None      )    ->		List[Any]:
      lowerCAmelCase__ :  int							      =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

      return output




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  int							      =					[self.sep_token_id]
      lowerCAmelCase__ :  Optional[int]							      =					[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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      lowerCAmelCase__ :  Union[str, Any]							      =					self._tokenizer.model.save(__UpperCAmelCase  ,name=__UpperCAmelCase      )
      return tuple(__UpperCAmelCase      )







'''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 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Tuple							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-base"""      )
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  str							      =					torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Dict							      =					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():
          lowerCAmelCase__ :  Optional[int]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Union[str, Any]							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-large"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  Dict							      =					torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Union[str, Any]							      =					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():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )


