
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

__lowerCAmelCase					:			List[Any]			      =		logging.get_logger(__name__)


__lowerCAmelCase					:			Optional[int]			      =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowerCAmelCase					:			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"
    },
}

__lowerCAmelCase					:			Any			      =		{"facebook/blenderbot-3B": 128}


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Union[str, Any]        =    VOCAB_FILES_NAMES
      A__	:			Optional[Any]        =    PRETRAINED_VOCAB_FILES_MAP
      A__	:			str        =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__	:			Union[str, Any]        =    ['''input_ids''', '''attention_mask''']
      A__	:			List[Any]        =    BlenderbotTokenizer




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

          __lowercase		:			int	    =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
          if pre_tok_state.get('''add_prefix_space'''				,       _snake_case				) != add_prefix_space:
              __lowercase		:			Tuple	    =       getattr(_snake_case				,       pre_tok_state.pop('''type'''				)				)
              __lowercase		:			Optional[Any]	    =       add_prefix_space
              __lowercase		:			Dict	    =       pre_tok_class(**_snake_case				)

          __lowercase		:			Union[str, Any]	    =       add_prefix_space

          __lowercase		:			Any	    =       '''post_processor'''
          __lowercase		:			Optional[Any]	    =       getattr(self.backend_tokenizer				,       _snake_case				,       _snake_case				)
          if tokenizer_component_instance:
              __lowercase		:			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:
                  __lowercase		:			Optional[int]	    =       tuple(state['''sep''']				)
              if "cls" in state:
                  __lowercase		:			Optional[int]	    =       tuple(state['''cls''']				)

              __lowercase		:			Dict	    =       False

              if state.get('''add_prefix_space'''				,       _snake_case				) != add_prefix_space:
                  __lowercase		:			Union[str, Any]	    =       add_prefix_space
                  __lowercase		:			Any	    =       True

              if state.get('''trim_offsets'''				,       _snake_case				) != trim_offsets:
                  __lowercase		:			Tuple	    =       trim_offsets
                  __lowercase		:			List[Any]	    =       True

              if changes_to_apply:
                  __lowercase		:			Dict	    =       getattr(_snake_case				,       state.pop('''type'''				)				)
                  __lowercase		:			Tuple	    =       component_class(**_snake_case				)
                  setattr(self.backend_tokenizer				,       _snake_case				,       _snake_case				)




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




      def 		snake_case_			(      self		:       Union[str, Any]				,       *_snake_case		:       Dict				,       **_snake_case		:       Any				):
          __lowercase		:			str	    =       kwargs.get('''is_split_into_words'''				,       _snake_case				)
          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(*_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				,       *_snake_case		:       Tuple				,       **_snake_case		:       str				):
          __lowercase		:			Union[str, Any]	    =       kwargs.get('''is_split_into_words'''				,       _snake_case				)

          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(*_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       str				,       _snake_case		:       Optional[str] = None				):
          __lowercase		:			List[str]	    =       self._tokenizer.model.save(_snake_case				,       name=_snake_case				)
          return tuple(_snake_case				)




      def 		snake_case_			(      self		:       str				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				):
          __lowercase		:			str	    =       [self.sep_token_id]
          __lowercase		:			int	    =       [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 		snake_case_			(      self		:       Any				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				):
          return token_ids_a + [self.eos_token_id]




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       "Conversation"				):
          __lowercase		:			Any	    =       []
          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(_snake_case				)

          __lowercase		:			Dict	    =       '''  '''.join(_snake_case				)
          __lowercase		:			List[str]	    =       self.encode(_snake_case				)
          if len(_snake_case				) > self.model_max_length:
              __lowercase		:			List[Any]	    =       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




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Dict        =    None
      A__	:			Any        =    BloomTokenizerFast
      A__	:			Optional[Any]        =    BloomTokenizerFast
      A__	:			List[Any]        =    True
      A__	:			int        =    False
      A__	:			Any        =    '''tokenizer_file'''
      A__	:			Optional[int]        =    {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




      def 		snake_case_			(      self		:       Any				):
          super().setUp()
          __lowercase		:			int	    =       BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''				)
          tokenizer.save_pretrained(self.tmpdirname				)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       Any				):
          kwargs.update(self.special_tokens_map				)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname				,       **_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Dict	    =       self.get_rust_tokenizer()

          __lowercase		:			Optional[int]	    =       ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
          __lowercase		:			Union[str, Any]	    =       [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

          __lowercase		:			Optional[int]	    =       tokenizer.batch_encode_plus(_snake_case				)['''input_ids''']
          self.assertListEqual(_snake_case				,       _snake_case				)

          __lowercase		:			List[Any]	    =       tokenizer.batch_decode(_snake_case				)
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       str				,       _snake_case		:       int=6				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                  __lowercase		:			Dict	    =       self.rust_tokenizer_class.from_pretrained(_snake_case				,       **_snake_case				)
                  # tokenizer_r.pad_token = None # Hotfixing padding = None
                  # Simple input
                  __lowercase		:			List[Any]	    =       '''This is a simple input'''
                  __lowercase		:			List[str]	    =       ['''This is a simple input 1''', '''This is a simple input 2''']
                  __lowercase		:			Optional[Any]	    =       ('''This is a simple input''', '''This is a pair''')
                  __lowercase		:			Any	    =       [
                      ('''This is a simple input 1''', '''This is a simple input 2'''),
                      ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                  ]

                  # Simple input tests
                  try:
                      tokenizer_r.encode(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.encode_plus(_snake_case				,       max_length=_snake_case				)

                      tokenizer_r.batch_encode_plus(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.encode(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.batch_encode_plus(_snake_case				,       max_length=_snake_case				)
                  except ValueError:
                      self.fail('''Bloom Tokenizer should be able to deal with padding'''				)

                  __lowercase		:			Dict	    =       None  # Hotfixing padding = None
                  self.assertRaises(_snake_case				,       tokenizer_r.encode				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Simple input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Simple input
                  self.assertRaises(
                      _snake_case				,       tokenizer_r.batch_encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				,       )

                  # Pair input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Pair input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Pair input
                  self.assertRaises(
                      _snake_case				,       tokenizer_r.batch_encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				,       )




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[Any]	    =       self.get_rust_tokenizer()
          __lowercase		:			int	    =       load_dataset('''xnli'''				,       '''all_languages'''				,       split='''test'''				,       streaming=_snake_case				)

          __lowercase		:			Optional[Any]	    =       next(iter(_snake_case				)				)['''premise''']  # pick up one data
          __lowercase		:			List[str]	    =       list(sample_data.values()				)

          __lowercase		:			List[str]	    =       list(map(tokenizer.encode				,       _snake_case				)				)
          __lowercase		:			Tuple	    =       [tokenizer.decode(_snake_case				,       clean_up_tokenization_spaces=_snake_case				) for x in output_tokens]
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
          # any sequence length constraints. This test of the parent class will fail since it relies on the
          # maximum sequence length of the positoonal embeddings.
          self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)				,       1				)
          self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)				,       1				)


