
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

A		: Any								=     logging.get_logger(__name__)


A		: List[str]								=     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

A		: Dict								=     {
    '''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'''
    },
}

A		: Dict								=     {'''facebook/blenderbot-3B''': 128}



class    a_ (			_a    ):
			a	:			Dict					   =							VOCAB_FILES_NAMES
			a	:			Optional[Any]					   =							PRETRAINED_VOCAB_FILES_MAP
			a	:			Optional[int]					   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a	:			Dict					   =							['''input_ids''', '''attention_mask''']
			a	:			Optional[Any]					   =							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				,			):
									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				,			)

									_lowercase					       =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
									if pre_tok_state.get("""add_prefix_space"""				,			__UpperCamelCase  ) != add_prefix_space:
															_lowercase					       =					getattr(__UpperCamelCase				,			pre_tok_state.pop("""type"""  )  )
															_lowercase					       =					add_prefix_space
															_lowercase					       =					pre_tok_class(**__UpperCamelCase  )

									_lowercase					       =					add_prefix_space

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

															_lowercase					       =					False

															if state.get("""add_prefix_space"""				,			__UpperCamelCase  ) != add_prefix_space:
																					_lowercase					       =					add_prefix_space
																					_lowercase					       =					True

															if state.get("""trim_offsets"""				,			__UpperCamelCase  ) != trim_offsets:
																					_lowercase					       =					trim_offsets
																					_lowercase					       =					True

															if changes_to_apply:
																					_lowercase					       =					getattr(__UpperCamelCase				,			state.pop("""type"""  )  )
																					_lowercase					       =					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    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					AddedToken(__UpperCamelCase				,			lstrip=__UpperCamelCase				,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase				,			__UpperCamelCase  ) else value
									_lowercase					       =					value






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					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    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									_lowercase					       =					self._tokenizer.model.save(__UpperCamelCase				,			name=__UpperCamelCase  )
									return tuple(__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									_lowercase					       =					[self.sep_token_id]
									_lowercase					       =					[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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									return token_ids_a + [self.eos_token_id]






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					[]
									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  )

									_lowercase					       =					"""  """.join(__UpperCamelCase  )
									_lowercase					       =					self.encode(__UpperCamelCase  )
									if len(__UpperCamelCase  ) > self.model_max_length:
															_lowercase					       =					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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""  )

									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class