






'''simple docstring'''







from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_					=				logging.get_logger(__name__)




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    int				       =					["input_ids", "attention_mask"]




      def __init__(					self  ,		_UpperCAmelCase="[PAD]"  ,		_UpperCAmelCase="[BOS]"  ,		_UpperCAmelCase="[EOS]"  ,		_UpperCAmelCase="[MASK]"  ,		_UpperCAmelCase="[CLS]"  ,		_UpperCAmelCase="[SEP]"  ,		_UpperCAmelCase=2048  ,		**_UpperCAmelCase  ,		):
             UpperCAmelCase__	:   List[str]												=	AddedToken(_UpperCAmelCase  ,		lstrip=_UpperCAmelCase  ,		rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) else pad_token
             UpperCAmelCase__	:   Union[str, Any]												=	AddedToken(_UpperCAmelCase  ,		lstrip=_UpperCAmelCase  ,		rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) else bos_token
             UpperCAmelCase__	:   List[Any]												=	AddedToken(_UpperCAmelCase  ,		lstrip=_UpperCAmelCase  ,		rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) else eos_token
             UpperCAmelCase__	:   str												=	AddedToken(_UpperCAmelCase  ,		lstrip=_UpperCAmelCase  ,		rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) else mask_token
             UpperCAmelCase__	:   str												=	AddedToken(_UpperCAmelCase  ,		lstrip=_UpperCAmelCase  ,		rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) else cls_token
             UpperCAmelCase__	:   Tuple												=	AddedToken(_UpperCAmelCase  ,		lstrip=_UpperCAmelCase  ,		rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) else sep_token

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

             UpperCAmelCase__	:   Dict												=	2**8  # utf is 8 bits

             # define special tokens dict
             UpperCAmelCase__	:   Dict[str, int]												=	{
                 self.pad_token: 0,
                 self.bos_token: 1,
                 self.eos_token: 2,
                 self.mask_token: 3,
                 self.cls_token: 4,
                 self.sep_token: 5,
             }
             UpperCAmelCase__	:   Optional[int]												=	len(self.special_tokens_encoder						)
             UpperCAmelCase__	:   Dict[int, str]												=	{v: k for k, v in self.special_tokens_encoder.items()}




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	self.special_tokens_encoder.copy()
             vocab.update(self.added_tokens_encoder						)
             for i in range(self._utf_vocab_size						):
                    UpperCAmelCase__	:   List[Any]												=	chr(_UpperCAmelCase						)
                    UpperCAmelCase__	:   List[Any]												=	i + len(self.special_tokens_encoder						)
             return vocab




      @property
      def 							lowerCamelCase     (					self						):
             return self._utf_vocab_size + self._num_special_tokens




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = False						):
             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=_UpperCAmelCase  ,		token_ids_a=_UpperCAmelCase  ,		already_has_special_tokens=_UpperCAmelCase						)

             # normal case: some special tokens
             if token_ids_a is None:
                    return [1] + [0] * len(_UpperCAmelCase						) + [1]
             return [1] + ([0] * len(_UpperCAmelCase						)) + [1] + ([0] * len(_UpperCAmelCase						)) + [1]




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None						):
             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             else:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   List[Any]												=	[chr(_UpperCAmelCase						) for i in text.encode('''utf-8'''						)]
             return tokens




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             if token in self.special_tokens_encoder:
                    UpperCAmelCase__	:   str												=	self.special_tokens_encoder[token]
             elif token in self.added_tokens_encoder:
                    UpperCAmelCase__	:   Optional[int]												=	self.added_tokens_encoder[token]
             elif len(_UpperCAmelCase						) != 1:
                    UpperCAmelCase__	:   Dict												=	self.unk_token_id
             else:
                    UpperCAmelCase__	:   Optional[Any]												=	ord(_UpperCAmelCase						) + self._num_special_tokens
             return token_id




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             if index in self.special_tokens_decoder:
                    UpperCAmelCase__	:   str												=	self.special_tokens_decoder[index]
             elif index in self.added_tokens_decoder:
                    UpperCAmelCase__	:   int												=	self.added_tokens_decoder[index]
             else:
                    UpperCAmelCase__	:   Dict												=	chr(index - self._num_special_tokens						)
             return token




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Optional[int]												=	B''''''
             for token in tokens:
                    if token in self.special_tokens_decoder:
                           UpperCAmelCase__	:   Any												=	self.special_tokens_decoder[token].encode('''utf-8'''						)
                    elif token in self.added_tokens_decoder:
                           UpperCAmelCase__	:   Optional[Any]												=	self.special_tokens_decoder[token].encode('''utf-8'''						)
                    elif token in self.special_tokens_encoder:
                           UpperCAmelCase__	:   Any												=	token.encode('''utf-8'''						)
                    elif token in self.added_tokens_encoder:
                           UpperCAmelCase__	:   Union[str, Any]												=	token.encode('''utf-8'''						)
                    else:
                           UpperCAmelCase__	:   Union[str, Any]												=	bytes([ord(_UpperCAmelCase						)]						)
                    bstring += tok_string
             UpperCAmelCase__	:   List[str]												=	bstring.decode('''utf-8'''  ,		errors='''replace'''						)
             return string




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None						):
             return ()







'''simple docstring'''







import unittest

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


if is_torch_available():
  import torch

  from transformers import CamembertModel




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

      '''simple docstring'''





      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	CamembertModel.from_pretrained('''camembert-base'''						)
             model.to(_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	torch.tensor(
                 [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,		device=_UpperCAmelCase  ,		dtype=torch.long  ,		)  # J'aime le camembert !
             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	model(_UpperCAmelCase						)['''last_hidden_state''']
             UpperCAmelCase__	:   List[str]												=	torch.Size((1, 10, 768)						)
             self.assertEqual(output.shape  ,		_UpperCAmelCase						)
             # compare the actual values for a slice.
             UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                 [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]  ,		device=_UpperCAmelCase  ,		dtype=torch.float  ,		)
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		_UpperCAmelCase  ,		atol=1E-4						)						)