






from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


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

__a:     str   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [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 + sep + token_ids_a + sep							) * [0]







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 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			List[str]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-base"""							)
         _UpperCAmelCase							=    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

         _UpperCAmelCase							=    torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
         #  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():
                _UpperCAmelCase							=    model(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-large"""							)
         _UpperCAmelCase							=    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

         _UpperCAmelCase							=    torch.Size((1, 12, 1024)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
         #  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():
                _UpperCAmelCase							=    model(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)