
'''simple docstring'''






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCAmelCase_							: str =							logging.get_logger(__name__)


UpperCAmelCase_							: List[Any] =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_							: Union[str, Any] =							{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_							: List[str] =							{
    'allenai/led-base-16384': 1_6384,
}

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		VOCAB_FILES_NAMES
    lowerCAmelCase_					     =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_					     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_					     =		LEDTokenizer
    lowerCAmelCase_					     =		['input_ids', 'attention_mask']



    def __init__(						self   :							Union[str, Any],__A   :							List[Any]=None,__A   :							str=None,__A   :							str=None,__A   :							Optional[int]="replace",__A   :							Union[str, Any]="<s>",__A   :							Union[str, Any]="</s>",__A   :							Any="</s>",__A   :							Optional[int]="<s>",__A   :							List[str]="<unk>",__A   :							str="<pad>",__A   :							Tuple="<mask>",__A   :							Union[str, Any]=False,__A   :							Optional[int]=True,**__A   :							Optional[int],):
          super().__init__(
              __A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)

          _lowerCamelCase					: List[str]		    = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
          if pre_tok_state.get("add_prefix_space",__A  ) != add_prefix_space:
                _lowerCamelCase					: str		    = getattr(__A,pre_tok_state.pop("type"  )  )
                _lowerCamelCase					: List[Any]		    = add_prefix_space
                _lowerCamelCase					: Tuple		    = pre_tok_class(**__A  )

          _lowerCamelCase					: Optional[int]		    = add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _lowerCamelCase					: List[str]		    = "post_processor"
          _lowerCamelCase					: int		    = getattr(self.backend_tokenizer,__A,__A  )
          if tokenizer_component_instance:
                _lowerCamelCase					: Tuple		    = json.loads(tokenizer_component_instance.__getstate__()  )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                      _lowerCamelCase					: str		    = tuple(state["sep"]  )
                if "cls" in state:
                      _lowerCamelCase					: List[str]		    = tuple(state["cls"]  )

                _lowerCamelCase					: Dict		    = False

                if state.get("add_prefix_space",__A  ) != add_prefix_space:
                      _lowerCamelCase					: List[str]		    = add_prefix_space
                      _lowerCamelCase					: List[Any]		    = True

                if state.get("trim_offsets",__A  ) != trim_offsets:
                      _lowerCamelCase					: List[str]		    = trim_offsets
                      _lowerCamelCase					: List[str]		    = True

                if changes_to_apply:
                      _lowerCamelCase					: Tuple		    = getattr(__A,state.pop("type"  )  )
                      _lowerCamelCase					: Any		    = component_class(**__A  )
                      setattr(self.backend_tokenizer,__A,__A  )



    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def 	lowerCamelCase_	(						self   :							str  ):
          if self._mask_token is None:
                if self.verbose:
                      logger.error("Using mask_token, but it is not set yet."  )
                return None
          return str(self._mask_token  )



    @mask_token.setter
    def 	lowerCamelCase_	(						self   :							List[str],__A   :							str  ):
          _lowerCamelCase					: Optional[Any]		    = AddedToken(__A,lstrip=__A,rstrip=__A  ) if isinstance(__A,__A  ) else value
          _lowerCamelCase					: str		    = value



    def 	lowerCamelCase_	(						self   :							List[str],*__A   :							List[Any],**__A   :							int  ):
          _lowerCamelCase					: List[str]		    = kwargs.get("is_split_into_words",__A  )

          if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                    "to use it with pretokenized inputs."  )

          return super()._batch_encode_plus(*__A,**__A  )



    def 	lowerCamelCase_	(						self   :							Optional[int],*__A   :							Optional[Any],**__A   :							Union[str, Any]  ):
          _lowerCamelCase					: List[Any]		    = kwargs.get("is_split_into_words",__A  )

          if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                    "to use it with pretokenized inputs."  )

          return super()._encode_plus(*__A,**__A  )



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							str,__A   :							Optional[str] = None  ):
          _lowerCamelCase					: List[str]		    = self._tokenizer.model.save(__A,name=__A  )
          return tuple(__A  )



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							Optional[Any],__A   :							List[str]=None  ):
          _lowerCamelCase					: Optional[Any]		    = [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   :							Dict,__A   :							List[int],__A   :							Optional[List[int]] = None  ):
          _lowerCamelCase					: Tuple		    = [self.sep_token_id]
          _lowerCamelCase					: Union[str, Any]		    = [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 	lowerCamelCase_	(						self   :							Any,__A   :							Union[Dict[str, EncodedInput], BatchEncoding],__A   :							Optional[int] = None,__A   :							PaddingStrategy = PaddingStrategy.DO_NOT_PAD,__A   :							Optional[int] = None,__A   :							Optional[bool] = None,):
          _lowerCamelCase					: List[str]		    = super()._pad(
              encoded_inputs=__A,max_length=__A,padding_strategy=__A,pad_to_multiple_of=__A,return_attention_mask=__A,)

          # Load from model defaults
          if return_attention_mask is None:
                _lowerCamelCase					: Any		    = "attention_mask" in self.model_input_names

          if return_attention_mask and "global_attention_mask" in encoded_inputs:
                _lowerCamelCase					: Union[str, Any]		    = encoded_inputs[self.model_input_names[0]]
                # `global_attention_mask` need to have the same length as other (sequential) inputs.
                _lowerCamelCase					: Optional[Any]		    = len(encoded_inputs["global_attention_mask"]  ) != len(__A  )

                if needs_to_be_padded:
                      _lowerCamelCase					: str		    = len(__A  ) - len(encoded_inputs["global_attention_mask"]  )

                      if self.padding_side == "right":
                            # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                            _lowerCamelCase					: Tuple		    = (
                                encoded_inputs["global_attention_mask"] + [-1] * difference
                            )
                      elif self.padding_side == "left":
                            _lowerCamelCase					: int		    = [-1] * difference + encoded_inputs[
                                "global_attention_mask"
                            ]
                      else:
                            raise ValueError("Invalid padding strategy:" + str(self.padding_side  )  )

          return encoded_inputs



"""simple docstring"""




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__						: Dict    =				get_tests_dir('fixtures/test_sentencepiece.model')
__magic_name__						: int    =				get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__magic_name__						: Any    =				'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       CamembertTokenizer
							__a					 =       CamembertTokenizerFast
							__a					 =       True
							__a					 =       True




							def   __a   (     self:    List[Any]   ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      """<pad>"""
										__lowerCamelCase          =      1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_   )     ,							A_   )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_   )     ,							A_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      list(self.get_tokenizer().get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]     ,							"""<s>NOTUSED"""   )
										self.assertEqual(vocab_keys[1]     ,							"""<pad>"""   )
										self.assertEqual(vocab_keys[-1]     ,							"""<mask>"""   )
										self.assertEqual(len(A_   )     ,							10_04   )




							def   __a   (     self:    Any   ):
										self.assertEqual(self.get_tokenizer().vocab_size     ,							10_05   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_     ,							add_special_tokens=A_   )
										self.assertListEqual(A_     ,							A_   )

										# <unk> tokens are not the same for `rust` than for `slow`.
										# Because spm gives back raw token instead of `unk` in EncodeAsPieces
										# tokens = tokenizer.tokenize(sequence)
										__lowerCamelCase          =      tokenizer.convert_ids_to_tokens(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )




							def   __a   (     self:    List[str]   ):
										if not self.test_rust_tokenizer:
													return

										__lowerCamelCase          =      self.get_tokenizer()
										__lowerCamelCase          =      self.get_rust_tokenizer()

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.tokenize(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_     ,							add_special_tokens=A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      self.get_rust_tokenizer()
										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )




							@slow
							def   __a   (     self:    Optional[Any]   ):
										# fmt: off
										__lowerCamelCase          =      {"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
										# fmt: on

										# camembert is a french model. So we also use french texts.
										__lowerCamelCase          =      [
										    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
										    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
										    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
										    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
										    """telles que la traduction et la synthèse de texte.""",
										]

										self.tokenizer_integration_test_util(
										    expected_encoding=A_     ,							model_name="""camembert-base"""     ,							revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""     ,							sequences=A_     ,							)




