


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											=       logging.get_logger(__name__)


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

lowerCamelCase											=       {
    "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											=       {"facebook/blenderbot-3B": 1_28}




class A  (   lowerCAmelCase__     ):
      UpperCamelCase__							:  int      =VOCAB_FILES_NAMES
      UpperCamelCase__							:  str      =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:  Union[str, Any]      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:  Union[str, Any]      =["""input_ids""", """attention_mask"""]
      UpperCamelCase__							:  List[str]      =BlenderbotTokenizer






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




             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			,   )

             _lowerCamelCase       :		Optional[int]		=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
             if pre_tok_state.get('add_prefix_space'			,   __a ) != add_prefix_space:
                    _lowerCamelCase       :		Optional[int]		=getattr(__a			,   pre_tok_state.pop('type' ) )
                    _lowerCamelCase       :		Union[str, Any]		=add_prefix_space
                    _lowerCamelCase       :		Tuple		=pre_tok_class(**__a )

             _lowerCamelCase       :		str		=add_prefix_space

             _lowerCamelCase       :		List[str]		='post_processor'
             _lowerCamelCase       :		Any		=getattr(self.backend_tokenizer			,   __a			,   __a )
             if tokenizer_component_instance:
                    _lowerCamelCase       :		Optional[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:
                           _lowerCamelCase       :		Union[str, Any]		=tuple(state['sep'] )
                    if "cls" in state:
                           _lowerCamelCase       :		Any		=tuple(state['cls'] )

                    _lowerCamelCase       :		Any		=False

                    if state.get('add_prefix_space'			,   __a ) != add_prefix_space:
                           _lowerCamelCase       :		int		=add_prefix_space
                           _lowerCamelCase       :		List[str]		=True

                    if state.get('trim_offsets'			,   __a ) != trim_offsets:
                           _lowerCamelCase       :		List[Any]		=trim_offsets
                           _lowerCamelCase       :		Dict		=True

                    if changes_to_apply:
                           _lowerCamelCase       :		Union[str, Any]		=getattr(__a			,   state.pop('type' ) )
                           _lowerCamelCase       :		Optional[int]		=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        lowerCamelCase       (					self						:     Tuple )					->						List[str]:
             """simple docstring"""




             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						:     List[str]			,   lowercase_						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=AddedToken(__a			,   lstrip=__a			,   rstrip=__a ) if isinstance(__a			,   __a ) else value
             _lowerCamelCase       :		Union[str, Any]		=value






      def        lowerCamelCase       (					self						:     Optional[int]			,   *lowercase_						:     List[Any]			,   **lowercase_						:     int )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=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        lowerCamelCase       (					self						:     List[Any]			,   *lowercase_						:     Optional[int]			,   **lowercase_						:     List[str] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=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        lowerCamelCase       (					self						:     str			,   lowercase_						:     Any			,   lowercase_						:     Optional[int] = None )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=self._tokenizer.model.save(__a			,   name=__a )
             return tuple(__a )






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Tuple			,   lowercase_						:     str = None )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=[self.sep_token_id]
             _lowerCamelCase       :		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        lowerCamelCase       (					self						:     str			,   lowercase_						:     Optional[Any]			,   lowercase_						:     int = None )					->						Union[str, Any]:
             """simple docstring"""




             return token_ids_a + [self.eos_token_id]






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     List[str] )					->						Any:
             """simple docstring"""




             _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(__a )

             _lowerCamelCase       :		Optional[Any]		='  '.join(__a )
             _lowerCamelCase       :		Any		=self.encode(__a )
             if len(__a ) > self.model_max_length:
                    _lowerCamelCase       :		Dict		=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






"""simple docstring"""


from typing import TYPE_CHECKING

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


A     :   Tuple			    =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BlenderbotForCausalLM",
	    "BlenderbotForConditionalGeneration",
	    "BlenderbotModel",
	    "BlenderbotPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "TFBlenderbotForConditionalGeneration",
	    "TFBlenderbotModel",
	    "TFBlenderbotPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxBlenderbotForConditionalGeneration",
	    "FlaxBlenderbotModel",
	    "FlaxBlenderbotPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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




