

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[str]  	=      logging.get_logger(__name__)


A_	:			int  	=      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A_	:			List[str]  	=      {
    "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    _a						(__magic_name__     ):


					'''simple docstring'''






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






					def __init__(			self   ,	A__=None   ,	A__=None   ,	A__=None   ,	A__="replace"   ,	A__="<s>"   ,	A__="</s>"   ,	A__="</s>"   ,	A__="<s>"   ,	A__="<unk>"   ,	A__="<pad>"   ,	A__="<mask>"   ,	A__=False   ,	A__=True   ,	**A__   ,	):
						super().__init__(
						    _a   ,	_a   ,	tokenizer_file=_a   ,	errors=_a   ,	bos_token=_a   ,	eos_token=_a   ,	sep_token=_a   ,	cls_token=_a   ,	unk_token=_a   ,	pad_token=_a   ,	mask_token=_a   ,	add_prefix_space=_a   ,	trim_offsets=_a   ,	**_a   ,	)

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

						A__							:   Dict					       =		add_prefix_space

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

							A__							:   Optional[int]					       =		False

							if state.get("""add_prefix_space"""   ,	_a      ) != add_prefix_space:
								A__							:   Optional[int]					       =		add_prefix_space
								A__							:   Any					       =		True

							if state.get("""trim_offsets"""   ,	_a      ) != trim_offsets:
								A__							:   List[str]					       =		trim_offsets
								A__							:   List[str]					       =		True

							if changes_to_apply:
								A__							:   Any					       =		getattr(_a   ,	state.pop("""type"""      )      )
								A__							:   Any					       =		component_class(**_a      )
								setattr(self.backend_tokenizer   ,	_a   ,	_a      )






					@property
					# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
					def   __A      (			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   __A      (			self   ,	A__      ):
						A__							:   Tuple					       =		AddedToken(_a   ,	lstrip=_a   ,	rstrip=_a      ) if isinstance(_a   ,	_a      ) else value
						A__							:   Any					       =		value






					def   __A      (			self   ,	*A__   ,	**A__      ):
						A__							:   Any					       =		kwargs.get("""is_split_into_words"""   ,	_a      )
						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(*_a   ,	**_a      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						A__							:   Optional[Any]					       =		kwargs.get("""is_split_into_words"""   ,	_a      )

						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(*_a   ,	**_a      )






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						A__							:   str					       =		self._tokenizer.model.save(_a   ,	name=_a      )
						return tuple(_a      )






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						A__							:   List[Any]					       =		[self.sep_token_id]
						A__							:   Union[str, Any]					       =		[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   __A      (			self   ,	A__   ,	A__ = None      ):
						return token_ids_a + [self.eos_token_id]






					def   __A      (			self   ,	A__      ):
						A__							:   Optional[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(_a      )

						A__							:   List[str]					       =		"  ".join(_a      )
						A__							:   Optional[Any]					       =		self.encode(_a      )
						if len(_a      ) > self.model_max_length:
							A__							:   List[str]					       =		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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



