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_    :		Any							=	logging.get_logger(__name__)


lowerCamelCase_    :		Any							=	{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase_    :		Any							=	{
    """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_    :		List[Any]							=	{"""facebook/blenderbot-3B""": 128}


class 						a__							(       __snake_case		):
			A__    :       int        =      VOCAB_FILES_NAMES
			A__    :       Any        =      PRETRAINED_VOCAB_FILES_MAP
			A__    :       Optional[Any]        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__    :       str        =      ['input_ids', 'attention_mask']
			A__    :       List[str]        =      BlenderbotTokenizer



			def __init__(						self				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase="replace"				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="<unk>"				,						UpperCAmelCase="<pad>"				,						UpperCAmelCase="<mask>"				,						UpperCAmelCase=False				,						UpperCAmelCase=True				,						**UpperCAmelCase				,						)			->   str:
								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   )			->   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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
								__a		 =       AddedToken(UpperCAmelCase				,						lstrip=UpperCAmelCase				,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else value
								__a		 =       value



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   BatchEncoding:
								__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				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   BatchEncoding:
								__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				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[str]:
								__a		 =       self._tokenizer.model.save(UpperCAmelCase				,						name=UpperCAmelCase   )
								return tuple(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[int]:
								__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				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Union[str, Any]:
								return token_ids_a + [self.eos_token_id]







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[int]:
								__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





from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




