
'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		DebertaTokenizer
    lowerCAmelCase_				    =		True
    lowerCAmelCase_				    =		DebertaTokenizerFast



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          _lowerCamelCase				:   int								=   [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "[UNK]",
          ]
          _lowerCamelCase				:   str								=   dict(zip(__A,range(len(__A     )     )     )     )
          _lowerCamelCase				:   List[Any]								=   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          _lowerCamelCase				:   List[Any]								=   {"unk_token": "[UNK]"}

          _lowerCamelCase				:   List[Any]								=   os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["vocab_file"]     )
          _lowerCamelCase				:   Any								=   os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["merges_file"]     )
          with open(self.vocab_file,"w",encoding="utf-8"     ) as fp:
                fp.write(json.dumps(__A     ) + "\n"     )
          with open(self.merges_file,"w",encoding="utf-8"     ) as fp:
                fp.write("\n".join(__A     )     )



    def   lowerCamelCase_  (	self      :       str,**__A      :       Union[str, Any]     ):
          kwargs.update(self.special_tokens_map     )
          return self.tokenizer_class.from_pretrained(self.tmpdirname,**__A     )



    def   lowerCamelCase_  (	self      :       str,__A      :       Optional[Any]     ):
          _lowerCamelCase				:   Optional[Any]								=   "lower newer"
          _lowerCamelCase				:   Any								=   "lower newer"
          return input_text, output_text



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   Any								=   self.get_tokenizer()
          _lowerCamelCase				:   Any								=   "lower newer"
          _lowerCamelCase				:   Optional[Any]								=   ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
          _lowerCamelCase				:   List[Any]								=   tokenizer.tokenize(__A     )
          self.assertListEqual(__A,__A     )

          _lowerCamelCase				:   Dict								=   tokens + [tokenizer.unk_token]
          _lowerCamelCase				:   List[Any]								=   [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(__A     ),__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Tuple								=   self.get_tokenizer()
          _lowerCamelCase				:   List[Any]								=   tokenizer("Hello","World"     )
          _lowerCamelCase				:   Union[str, Any]								=   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
          self.assertListEqual(tokd["token_type_ids"],__A     )



    @slow
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   Optional[int]								=   self.tokenizer_class.from_pretrained("microsoft/deberta-base"     )

          _lowerCamelCase				:   int								=   tokenizer.encode("sequence builders",add_special_tokens=__A     )
          _lowerCamelCase				:   int								=   tokenizer.encode("multi-sequence build",add_special_tokens=__A     )

          _lowerCamelCase				:   Any								=   tokenizer.encode(
              "sequence builders",add_special_tokens=__A,add_prefix_space=__A     )
          _lowerCamelCase				:   Optional[int]								=   tokenizer.encode(
              "sequence builders","multi-sequence build",add_special_tokens=__A,add_prefix_space=__A     )

          _lowerCamelCase				:   str								=   tokenizer.build_inputs_with_special_tokens(__A     )
          _lowerCamelCase				:   Tuple								=   tokenizer.build_inputs_with_special_tokens(__A,__A     )

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode



    @slow
    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   Optional[int]								=   [self.tokenizer_class]
          if self.test_rust_tokenizer:
                tokenizer_classes.append(self.rust_tokenizer_class     )

          for tokenizer_class in tokenizer_classes:
                _lowerCamelCase				:   List[str]								=   tokenizer_class.from_pretrained("microsoft/deberta-base"     )

                _lowerCamelCase				:   str								=   [
                    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                    "ALBERT incorporates two parameter reduction techniques",
                    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                    " vocabulary embedding.",
                ]

                _lowerCamelCase				:   Dict								=   tokenizer(__A,padding=__A     )
                _lowerCamelCase				:   Optional[int]								=   [tokenizer.decode(__A,skip_special_tokens=__A     ) for seq in encoding["input_ids"]]

                # fmt: off
                _lowerCamelCase				:   Tuple								=   {
                    "input_ids": [
                        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
                    ],
                    "token_type_ids": [
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                    ],
                    "attention_mask": [
                        [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, 0, 0, 0],
                        [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, 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]
                    ]
                }
                # fmt: on

                _lowerCamelCase				:   str								=   [
                    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                    "ALBERT incorporates two parameter reduction techniques",
                    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                    " vocabulary embedding.",
                ]

                self.assertDictEqual(encoding.data,__A     )

                for expected, decoded in zip(__A,__A     ):
                      self.assertEqual(__A,__A     )

'''simple docstring'''






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)


UpperCAmelCase_       :   Any   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   List[str]   =       {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

UpperCAmelCase_       :   List[Any]   =       {'facebook/blenderbot-3B': 128}

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



    def __init__(	self      :       Dict,__A      :       Optional[Any]=None,__A      :       List[str]=None,__A      :       Optional[Any]=None,__A      :       List[Any]="replace",__A      :       List[Any]="<s>",__A      :       str="</s>",__A      :       List[str]="</s>",__A      :       List[Any]="<s>",__A      :       Union[str, Any]="<unk>",__A      :       Optional[Any]="<pad>",__A      :       Dict="<mask>",__A      :       Any=False,__A      :       Tuple=True,**__A      :       Dict,):
          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				:   Tuple								=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
          if pre_tok_state.get("add_prefix_space",__A     ) != add_prefix_space:
                _lowerCamelCase				:   Optional[Any]								=   getattr(__A,pre_tok_state.pop("type"     )     )
                _lowerCamelCase				:   List[Any]								=   add_prefix_space
                _lowerCamelCase				:   Optional[Any]								=   pre_tok_class(**__A     )

          _lowerCamelCase				:   Any								=   add_prefix_space

          _lowerCamelCase				:   Any								=   "post_processor"
          _lowerCamelCase				:   Optional[Any]								=   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				:   List[Any]								=   tuple(state["sep"]     )
                if "cls" in state:
                      _lowerCamelCase				:   Optional[int]								=   tuple(state["cls"]     )

                _lowerCamelCase				:   List[str]								=   False

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

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

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



    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          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      :       Optional[Any],__A      :       List[Any]     ):
          _lowerCamelCase				:   Any								=   AddedToken(__A,lstrip=__A,rstrip=__A     ) if isinstance(__A,__A     ) else value
          _lowerCamelCase				:   List[str]								=   value



    def   lowerCamelCase_  (	self      :       Optional[int],*__A      :       Optional[Any],**__A      :       List[str]     ):
          _lowerCamelCase				:   int								=   kwargs.get("is_split_into_words",__A     )
          assert self.add_prefix_space or not is_split_into_words, (
              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      :       str,*__A      :       Union[str, Any],**__A      :       List[Any]     ):
          _lowerCamelCase				:   Optional[int]								=   kwargs.get("is_split_into_words",__A     )

          assert self.add_prefix_space or not is_split_into_words, (
              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      :       str,__A      :       str,__A      :       Optional[str] = None     ):
          _lowerCamelCase				:   Optional[int]								=   self._tokenizer.model.save(__A,name=__A     )
          return tuple(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   int								=   [self.sep_token_id]
          _lowerCamelCase				:   str								=   [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      :       Dict,__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          return token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[str],__A      :       "Conversation"     ):
          _lowerCamelCase				:   Dict								=   []
          for is_user, text in conversation.iter_texts():
                if is_user:
                      # We need to space prefix as it's being done within blenderbot
                      inputs.append(" " + text     )
                else:
                      # Generated responses should contain them already.
                      inputs.append(__A     )

          _lowerCamelCase				:   List[Any]								=   "  ".join(__A     )
          _lowerCamelCase				:   List[str]								=   self.encode(__A     )
          if len(__A     ) > self.model_max_length:
                _lowerCamelCase				:   Tuple								=   input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'     )
          return input_ids