






"""simple docstring"""


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

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


_UpperCamelCase				:    Dict     =						logging.get_logger(__name__)

_UpperCamelCase				:    str     =						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase				:    str     =						{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}






class 						snake_case  (  UpperCAmelCase       ):





    def __init__(							self	:					Dict				,		A	:					Any=None				,		A	:					Union[str, Any]=None				,		A	:					Optional[int]=False				,		A	:					List[Any]=True				,		A	:					Optional[int]=False				,		A	:					Any="<s>"				,		A	:					Optional[Any]="</s>"				,		A	:					Dict="<unk>"				,		A	:					List[Any]="<sep>"				,		A	:					List[Any]="<pad>"				,		A	:					Dict="<cls>"				,		A	:					List[Any]="<mask>"				,		A	:					Dict=["<eop>", "<eod>"]				,		**A	:					Tuple				,		):


      '''simple docstring'''




      a     :			Tuple				=   AddedToken(A				,		lstrip=A				,		rstrip=A			) if isinstance(A				,		A			) else mask_token

      super().__init__(
          vocab_file=A				,		tokenizer_file=A				,		do_lower_case=A				,		remove_space=A				,		keep_accents=A				,		bos_token=A				,		eos_token=A				,		unk_token=A				,		sep_token=A				,		pad_token=A				,		cls_token=A				,		mask_token=A				,		additional_special_tokens=A				,		**A				,		)

      a     :			Dict				=   3
      a     :			List[Any]				=   do_lower_case
      a     :			Dict				=   remove_space
      a     :			Dict				=   keep_accents
      a     :			int				=   vocab_file
      a     :			int				=   False if not self.vocab_file else True

      try:
        import jieba
      except ModuleNotFoundError as error:
        raise error.__class__(
            'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
            'See https://pypi.org/project/jieba/ for installation.'			)
      a     :			Optional[Any]				=   jieba
      a     :			Union[str, Any]				=   str.maketrans(' \n'				,		'\u2582\u2583'			)





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					List[int]				,		A	:					Optional[List[int]] = None			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Any				,		A	:					List[int]				,		A	:					Optional[List[int]] = None			):


      '''simple docstring'''




      a     :			Optional[int]				=   [self.sep_token_id]
      a     :			Optional[Any]				=   [2]

      if token_ids_a is None:
        return len(token_ids_a + sep			) * [0] + cls_segment_id
      return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id





    def 				lowerCamelCase__      (							self	:					Optional[Any]				,		A	:					str				,		A	:					Optional[str] = None			):


      '''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(A			):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
        return
      a     :			List[str]				=   os.path.join(
          A				,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

      return (out_vocab_file,)





    def 				lowerCamelCase__      (							self	:					Any				,		A	:					str				,		*A	:					str				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			Dict				=   [
          ' '.join([x.translate(self.translator			) for x in self.jieba.cut(A				,		cut_all=A			)]			)
          for text in batch_text_or_text_pairs
      ]
      return super()._batch_encode_plus(A				,		*A				,		**A			)





    def 				lowerCamelCase__      (							self	:					Any				,		*A	:					Optional[Any]				,		**A	:					Union[str, Any]			):


      '''simple docstring'''




      a     :			Optional[int]				=   super()._decode(*A				,		**A			)
      a     :			Dict				=   text.replace(' '				,		''			).replace('\u2582'				,		' '			).replace('\u2583'				,		'\n'			)
      return text












"""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 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   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=A				,		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]				,		A				,		atol=1E-4			)			)




