

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							:  Dict            =     logging.get_logger(__name__)


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

lowerCAmelCase							:  Optional[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							:  Any            =     {'facebook/blenderbot-3B': 128}



class   __lowercase    (			SCREAMING_SNAKE_CASE__     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[int]      							=					VOCAB_FILES_NAMES
			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase       :							Any      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase       :							Union[str, Any]      							=					['''input_ids''', '''attention_mask''']
			_UpperCAmelCase       :							List[Any]      							=					BlenderbotTokenizer



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

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

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	add_prefix_space

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

															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	False

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

															if state.get("trim_offsets"       ,						lowerCAmelCase__) != trim_offsets:
																					SCREAMING_SNAKE_CASE_:			Dict  		=	trim_offsets
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	True

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



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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			str  		=	[self.sep_token_id]
									SCREAMING_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 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									return token_ids_a + [self.eos_token_id]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					"Conversation"):
									SCREAMING_SNAKE_CASE_:			Dict  		=	[]
									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__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	'  '.join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	self.encode(lowerCAmelCase__)
									if len(lowerCAmelCase__) > self.model_max_length:
															SCREAMING_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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]            =						{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Optional[int]            =						[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						lowerCAmelCase							:  Any            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






