






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      :    Any   		=   logging.get_logger(__name__)


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

__lowerCamelCase      :    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'
    },
}

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



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





     """simple docstring"""






     a_			=	VOCAB_FILES_NAMES
     a_			=	PRETRAINED_VOCAB_FILES_MAP
     a_			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a_			=	["input_ids", "attention_mask"]
     a_			=	BlenderbotTokenizer



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

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

      snake_case__				:   Any     =						add_prefix_space

      snake_case__				:   Any     =						"post_processor"
      snake_case__				:   Optional[Any]     =						getattr(self.backend_tokenizer							,	__A							,	__A )
      if tokenizer_component_instance:
       snake_case__				:   Tuple     =						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__				:   List[Any]     =						tuple(state["sep"] )
       if "cls" in state:
        snake_case__				:   Optional[int]     =						tuple(state["cls"] )

       snake_case__				:   List[str]     =						False

       if state.get("add_prefix_space"							,	__A ) != add_prefix_space:
        snake_case__				:   List[str]     =						add_prefix_space
        snake_case__				:   int     =						True

       if state.get("trim_offsets"							,	__A ) != trim_offsets:
        snake_case__				:   List[str]     =						trim_offsets
        snake_case__				:   Dict     =						True

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



     def  _lowercase  (      self  :						Optional[int]							,	*__A  :						Optional[Any]							,	**__A  :						List[str] ):
      snake_case__				:   int     =						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  _lowercase  (      self  :						str							,	*__A  :						Union[str, Any]							,	**__A  :						List[Any] ):
      snake_case__				:   Optional[int]     =						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  _lowercase  (      self  :						str							,	__A  :						str							,	__A  :						Optional[str] = None ):
      snake_case__				:   Optional[int]     =						self._tokenizer.model.save(__A							,	name=__A )
      return tuple(__A )



     def  _lowercase  (      self  :						Optional[int]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
      snake_case__				:   int     =						[self.sep_token_id]
      snake_case__				:   str     =						[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  _lowercase  (      self  :						Dict							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
      return token_ids_a + [self.eos_token_id]






     def  _lowercase  (      self  :						List[str]							,	__A  :						"Conversation" ):
      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(__A )

      snake_case__				:   List[Any]     =						"  ".join(__A )
      snake_case__				:   List[str]     =						self.encode(__A )
      if len(__A ) > self.model_max_length:
       snake_case__				:   Tuple     =						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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    int   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



