




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			:	Union[str, Any]							 =     logging.get_logger(__name__)


_lowerCamelCase			:	Union[str, Any]							 =     {
    """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			:	Dict							 =     {"""facebook/blenderbot-3B""": 128}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']
    UpperCAmelCase__													=   BlenderbotTokenizer


    def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			str=None      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Dict="replace"      ,				UpperCAmelCase__   :			int="<s>"      ,				UpperCAmelCase__   :			str="</s>"      ,				UpperCAmelCase__   :			Any="</s>"      ,				UpperCAmelCase__   :			List[Any]="<s>"      ,				UpperCAmelCase__   :			Any="<unk>"      ,				UpperCAmelCase__   :			Optional[Any]="<pad>"      ,				UpperCAmelCase__   :			Any="<mask>"      ,				UpperCAmelCase__   :			Any=False      ,				UpperCAmelCase__   :			str=True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->Optional[Any]:





           '''simple docstring'''

           super().__init__(
               UpperCAmelCase__      ,				UpperCAmelCase__      ,				tokenizer_file=UpperCAmelCase__      ,				errors=UpperCAmelCase__      ,				bos_token=UpperCAmelCase__      ,				eos_token=UpperCAmelCase__      ,				sep_token=UpperCAmelCase__      ,				cls_token=UpperCAmelCase__      ,				unk_token=UpperCAmelCase__      ,				pad_token=UpperCAmelCase__      ,				mask_token=UpperCAmelCase__      ,				add_prefix_space=UpperCAmelCase__      ,				trim_offsets=UpperCAmelCase__      ,				**UpperCAmelCase__      ,				)

           A__   				=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('''add_prefix_space'''      ,				UpperCAmelCase__) != add_prefix_space:
                  A__   				=					getattr(UpperCAmelCase__      ,				pre_tok_state.pop('''type'''))
                  A__   				=					add_prefix_space
                  A__   				=					pre_tok_class(**UpperCAmelCase__)

           A__   				=					add_prefix_space

           A__   				=					'''post_processor'''
           A__   				=					getattr(self.backend_tokenizer      ,				UpperCAmelCase__      ,				UpperCAmelCase__)
           if tokenizer_component_instance:
                  A__   				=					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:
                         A__   				=					tuple(state['''sep'''])
                  if "cls" in state:
                         A__   				=					tuple(state['''cls'''])

                  A__   				=					False

                  if state.get('''add_prefix_space'''      ,				UpperCAmelCase__) != add_prefix_space:
                         A__   				=					add_prefix_space
                         A__   				=					True

                  if state.get('''trim_offsets'''      ,				UpperCAmelCase__) != trim_offsets:
                         A__   				=					trim_offsets
                         A__   				=					True

                  if changes_to_apply:
                         A__   				=					getattr(UpperCAmelCase__      ,				state.pop('''type'''))
                         A__   				=					component_class(**UpperCAmelCase__)
                         setattr(self.backend_tokenizer      ,				UpperCAmelCase__      ,				UpperCAmelCase__)


    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->str:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any])		->Any:





           '''simple docstring'''

           A__   				=					AddedToken(UpperCAmelCase__      ,				lstrip=UpperCAmelCase__      ,				rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__) else value
           A__   				=					value


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				*UpperCAmelCase__   :			List[Any]      ,				**UpperCAmelCase__   :			Any)		->BatchEncoding:





           '''simple docstring'''

           A__   				=					kwargs.get('''is_split_into_words'''      ,				UpperCAmelCase__)
           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(*UpperCAmelCase__      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				*UpperCAmelCase__   :			Optional[Any]      ,				**UpperCAmelCase__   :			Optional[int])		->BatchEncoding:





           '''simple docstring'''

           A__   				=					kwargs.get('''is_split_into_words'''      ,				UpperCAmelCase__)

           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(*UpperCAmelCase__      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

           A__   				=					self._tokenizer.model.save(UpperCAmelCase__      ,				name=UpperCAmelCase__)
           return tuple(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           A__   				=					[self.sep_token_id]
           A__   				=					[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  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->int:





           '''simple docstring'''

           return token_ids_a + [self.eos_token_id]





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			"Conversation")		->List[int]:





           '''simple docstring'''

           A__   				=					[]
           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(UpperCAmelCase__)

           A__   				=					'''  '''.join(UpperCAmelCase__)
           A__   				=					self.encode(UpperCAmelCase__)
           if len(UpperCAmelCase__) > self.model_max_length:
                  A__   				=					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 transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


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

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


