





'''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

_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)


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

_SCREAMING_SNAKE_CASE   =      {
    '''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'''
    },
}

_SCREAMING_SNAKE_CASE   =      {'''facebook/blenderbot-3B''': 1_2_8}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Tuple     =    VOCAB_FILES_NAMES
	a      :			List[Any]     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			List[str]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			Optional[Any]     =    ["input_ids", "attention_mask"]
	a      :			Optional[int]     =    BlenderbotTokenizer


	def __init__(self       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase="replace"       ,_lowerCamelCase="<s>"       ,_lowerCamelCase="</s>"       ,_lowerCamelCase="</s>"       ,_lowerCamelCase="<s>"       ,_lowerCamelCase="<unk>"       ,_lowerCamelCase="<pad>"       ,_lowerCamelCase="<mask>"       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,**_lowerCamelCase       ,)							->							Tuple:







				'''simple docstring'''





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

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

				__lowercase			  =							add_prefix_space

				__lowercase			  =							'''post_processor'''
				__lowercase			  =							getattr(self.backend_tokenizer       ,_lowerCamelCase       ,_lowerCamelCase	)
				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'''       ,_lowerCamelCase	) != add_prefix_space:
										__lowercase			  =							add_prefix_space
										__lowercase			  =							True

							if state.get('''trim_offsets'''       ,_lowerCamelCase	) != trim_offsets:
										__lowercase			  =							trim_offsets
										__lowercase			  =							True

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


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







				'''simple docstring'''





				__lowercase			  =							AddedToken(_lowerCamelCase       ,lstrip=_lowerCamelCase       ,rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase       ,_lowerCamelCase	) else value
				__lowercase			  =							value


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							BatchEncoding:







				'''simple docstring'''





				__lowercase			  =							kwargs.get('''is_split_into_words'''       ,_lowerCamelCase	)
				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(*_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							BatchEncoding:







				'''simple docstring'''





				__lowercase			  =							kwargs.get('''is_split_into_words'''       ,_lowerCamelCase	)

				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(*_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''simple docstring'''





				__lowercase			  =							self._tokenizer.model.save(_lowerCamelCase       ,name=_lowerCamelCase	)
				return tuple(_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							List[int]:







				'''simple docstring'''





				__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       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							List[str]:







				'''simple docstring'''





				return token_ids_a + [self.eos_token_id]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							List[int]:







				'''simple docstring'''





				__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(_lowerCamelCase	)

				__lowercase			  =							'''  '''.join(_lowerCamelCase	)
				__lowercase			  =							self.encode(_lowerCamelCase	)
				if len(_lowerCamelCase	) > 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












'''simple docstring'''



import numpy as np




def 				_lowerCAmelCase  (			lowerCamelCase_     :					np.array			):
			return 1 / (1 + np.exp(-vector			))




def 				_lowerCAmelCase  (			lowerCamelCase_     :					np.array			):
			return vector * sigmoid(1.7_02 * vector			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





