



"""simple docstring"""





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_barthez import BarthezTokenizer
else:
  lowerCamelCase       	=						None

lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase       	=						{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase       	=						{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

lowerCamelCase       	=						"""▁"""
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					VOCAB_FILES_NAMES
       UpperCamelCase						=					PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase						=					['''input_ids''', '''attention_mask''']
       UpperCamelCase						=					BarthezTokenizer



       def __init__(							self      :  str		,    _UpperCAmelCase      :  Union[str, Any]=None		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  Optional[int]="<s>"		,    _UpperCAmelCase      :  Optional[int]="</s>"		,    _UpperCAmelCase      :  Optional[int]="</s>"		,    _UpperCAmelCase      :  List[str]="<s>"		,    _UpperCAmelCase      :  Tuple="<unk>"		,    _UpperCAmelCase      :  str="<pad>"		,    _UpperCAmelCase      :  List[Any]="<mask>"		,    **_UpperCAmelCase      :  Dict		,    )			->						List[Any]:


            '''simple docstring'''




            UpperCAmelCase_      			=							AddedToken(_UpperCAmelCase		,    lstrip=_UpperCAmelCase		,    rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ) else mask_token

            super().__init__(
                _UpperCAmelCase		,    tokenizer_file=_UpperCAmelCase		,    bos_token=_UpperCAmelCase		,    eos_token=_UpperCAmelCase		,    unk_token=_UpperCAmelCase		,    sep_token=_UpperCAmelCase		,    cls_token=_UpperCAmelCase		,    pad_token=_UpperCAmelCase		,    mask_token=_UpperCAmelCase		,    **_UpperCAmelCase		,    )

            UpperCAmelCase_      			=							vocab_file
            UpperCAmelCase_      			=							False if not self.vocab_file else True



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


            '''simple docstring'''




            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            UpperCAmelCase_      			=							[self.cls_token_id]
            UpperCAmelCase_      			=							[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep



       def 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  List[int]		,    _UpperCAmelCase      :  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]







       def 					lowercase__    (							self      :  Union[str, Any]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Optional[str] = None   )			->						Tuple[str]:


            '''simple docstring'''




            if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                     "tokenizer."   )

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

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

            return (out_vocab_file,)









"""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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @slow
       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 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(_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 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 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(_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   )   )




