






'''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

_A	:			List[Any]         =logging.get_logger(__name__)


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

_A	:			List[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'''
    },
}

_A	:			List[Any]         ={'''facebook/blenderbot-3B''': 128}

class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       PRETRAINED_VOCAB_FILES_MAP
							a				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       ["""input_ids""", """attention_mask"""]
							a				      =       BlenderbotTokenizer




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

														lowerCamelCase__					:	Optional[int]         =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
														if pre_tok_state.get("""add_prefix_space"""			,							UpperCamelCase__     ) != add_prefix_space:
																					lowerCamelCase__					:	List[str]         =       getattr(UpperCamelCase__			,							pre_tok_state.pop("""type"""     )     )
																					lowerCamelCase__					:	Union[str, Any]         =       add_prefix_space
																					lowerCamelCase__					:	Optional[int]         =       pre_tok_class(**UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       add_prefix_space

														lowerCamelCase__					:	Tuple         =       """post_processor"""
														lowerCamelCase__					:	Tuple         =       getattr(self.backend_tokenizer			,							UpperCamelCase__			,							UpperCamelCase__     )
														if tokenizer_component_instance:
																					lowerCamelCase__					:	str         =       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:
																												lowerCamelCase__					:	str         =       tuple(state["""sep"""]     )
																					if "cls" in state:
																												lowerCamelCase__					:	Optional[Any]         =       tuple(state["""cls"""]     )

																					lowerCamelCase__					:	Optional[int]         =       False

																					if state.get("""add_prefix_space"""			,							UpperCamelCase__     ) != add_prefix_space:
																												lowerCamelCase__					:	Tuple         =       add_prefix_space
																												lowerCamelCase__					:	Optional[Any]         =       True

																					if state.get("""trim_offsets"""			,							UpperCamelCase__     ) != trim_offsets:
																												lowerCamelCase__					:	int         =       trim_offsets
																												lowerCamelCase__					:	int         =       True

																					if changes_to_apply:
																												lowerCamelCase__					:	List[Any]         =       getattr(UpperCamelCase__			,							state.pop("""type"""     )     )
																												lowerCamelCase__					:	Any         =       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 		lowerCamelCase_				(							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 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       AddedToken(UpperCamelCase__			,							lstrip=UpperCamelCase__			,							rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else value
														lowerCamelCase__					:	int         =       value




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							*UpperCamelCase__:				Optional[Any]			,							**UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	List[str]         =       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 		lowerCamelCase_				(							self:				Optional[int]			,							*UpperCamelCase__:				List[Any]			,							**UpperCamelCase__:				int     ):
														lowerCamelCase__					:	List[str]         =       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 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[str] = None     ):
														lowerCamelCase__					:	Dict         =       self._tokenizer.model.save(UpperCamelCase__			,							name=UpperCamelCase__     )
														return tuple(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				Optional[List[int]] = None     ):
														lowerCamelCase__					:	List[Any]         =       [self.sep_token_id]
														lowerCamelCase__					:	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 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				Optional[List[int]] = None     ):
														return token_ids_a + [self.eos_token_id]




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				"Conversation"     ):
														lowerCamelCase__					:	List[str]         =       []
														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__     )

														lowerCamelCase__					:	str         =       """  """.join(UpperCamelCase__     )
														lowerCamelCase__					:	str         =       self.encode(UpperCamelCase__     )
														if len(UpperCamelCase__     ) > self.model_max_length:
																					lowerCamelCase__					:	List[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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)