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


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

__SCREAMING_SNAKE_CASE						:   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'
    },
}

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



class        lowercase_   (					lowerCamelCase_		):
							_lowerCamelCase						=		VOCAB_FILES_NAMES
							_lowerCamelCase						=		PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase						=		["""input_ids""", """attention_mask"""]
							_lowerCamelCase						=		BlenderbotTokenizer



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

													_snake_case						:					str    =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
													if pre_tok_state.get("add_prefix_space"				,    lowerCAmelCase__    ) != add_prefix_space:
																			_snake_case						:					int    =   getattr(lowerCAmelCase__				,    pre_tok_state.pop("type"    )    )
																			_snake_case						:					str    =   add_prefix_space
																			_snake_case						:					Tuple    =   pre_tok_class(**lowerCAmelCase__    )

													_snake_case						:					str    =   add_prefix_space

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

																			_snake_case						:					List[str]    =   False

																			if state.get("add_prefix_space"				,    lowerCAmelCase__    ) != add_prefix_space:
																									_snake_case						:					Any    =   add_prefix_space
																									_snake_case						:					Tuple    =   True

																			if state.get("trim_offsets"				,    lowerCAmelCase__    ) != trim_offsets:
																									_snake_case						:					int    =   trim_offsets
																									_snake_case						:					Tuple    =   True

																			if changes_to_apply:
																									_snake_case						:					List[Any]    =   getattr(lowerCAmelCase__				,    state.pop("type"    )    )
																									_snake_case						:					Tuple    =   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    ):
													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				,    lowercase_    ):
													_snake_case						:					Tuple    =   AddedToken(lowerCAmelCase__				,    lstrip=lowerCAmelCase__				,    rstrip=lowerCAmelCase__    ) if isinstance(lowerCAmelCase__				,    lowerCAmelCase__    ) else value
													_snake_case						:					Tuple    =   value



							def 	UpperCamelCase	(						self				,    *lowercase_				,    **lowercase_    ):
													_snake_case						:					Union[str, Any]    =   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				,    *lowercase_				,    **lowercase_    ):
													_snake_case						:					Union[str, Any]    =   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				,    lowercase_				,    lowercase_ = None    ):
													_snake_case						:					Optional[Any]    =   self._tokenizer.model.save(lowerCAmelCase__				,    name=lowerCAmelCase__    )
													return tuple(lowerCAmelCase__    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													_snake_case						:					List[str]    =   [self.sep_token_id]
													_snake_case						:					Optional[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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													return token_ids_a + [self.eos_token_id]



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					int    =   []
													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__    )

													_snake_case						:					str    =   "  ".join(lowerCAmelCase__    )
													_snake_case						:					List[Any]    =   self.encode(lowerCAmelCase__    )
													if len(lowerCAmelCase__    ) > self.model_max_length:
																			_snake_case						:					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




def  snake_case						(__lowercase						)	->	bool:

						'''simple docstring'''



						_snake_case						:					Dict    =   0
						for ch in input_str:
												_snake_case						:					int    =   ord(__lowercase						)
												_snake_case						:					List[Any]    =   pow(2 ,      __lowercase						)

												# If we already turned on bit for current character's unicode
												if bitmap >> ch_unicode & 1 == 1:
																		return False
												bitmap |= ch_bit_index_on
						return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()