


"""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

UpperCAmelCase__					=logging.get_logger(__name__)


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

UpperCAmelCase__					={
    '''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'''
    },
}

UpperCAmelCase__					={'''facebook/blenderbot-3B''': 128}





class  lowerCamelCase__    (		_a     ):
      a			:  str           =		VOCAB_FILES_NAMES
      a			:  Dict           =		PRETRAINED_VOCAB_FILES_MAP
      a			:  Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a			:  Optional[Any]           =		['input_ids', 'attention_mask']
      a			:  Dict           =		BlenderbotTokenizer
      def __init__(		self						:						Any   ,							A_						:						List[Any]=None   ,							A_						:						Union[str, Any]=None   ,							A_						:						Dict=None   ,							A_						:						List[Any]="replace"   ,							A_						:						str="<s>"   ,							A_						:						List[str]="</s>"   ,							A_						:						Optional[Any]="</s>"   ,							A_						:						List[Any]="<s>"   ,							A_						:						Any="<unk>"   ,							A_						:						List[str]="<pad>"   ,							A_						:						Dict="<mask>"   ,							A_						:						int=False   ,							A_						:						Tuple=True   ,							**A_						:						int   ,							):






       '''simple docstring'''


       super().__init__(
           _SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							tokenizer_file=_SCREAMING_SNAKE_CASE   ,							errors=_SCREAMING_SNAKE_CASE   ,							bos_token=_SCREAMING_SNAKE_CASE   ,							eos_token=_SCREAMING_SNAKE_CASE   ,							sep_token=_SCREAMING_SNAKE_CASE   ,							cls_token=_SCREAMING_SNAKE_CASE   ,							unk_token=_SCREAMING_SNAKE_CASE   ,							pad_token=_SCREAMING_SNAKE_CASE   ,							mask_token=_SCREAMING_SNAKE_CASE   ,							add_prefix_space=_SCREAMING_SNAKE_CASE   ,							trim_offsets=_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE   ,							)

       __lowercase						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get("""add_prefix_space"""   ,							_SCREAMING_SNAKE_CASE				) != add_prefix_space:
        __lowercase						=						getattr(_SCREAMING_SNAKE_CASE   ,							pre_tok_state.pop("""type"""				)				)
        __lowercase						=						add_prefix_space
        __lowercase						=						pre_tok_class(**_SCREAMING_SNAKE_CASE				)

       __lowercase						=						add_prefix_space

       __lowercase						=						"""post_processor"""
       __lowercase						=						getattr(self.backend_tokenizer   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE				)
       if tokenizer_component_instance:
        __lowercase						=						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:
         __lowercase						=						tuple(state["""sep"""]				)
        if "cls" in state:
         __lowercase						=						tuple(state["""cls"""]				)

        __lowercase						=						False

        if state.get("""add_prefix_space"""   ,							_SCREAMING_SNAKE_CASE				) != add_prefix_space:
         __lowercase						=						add_prefix_space
         __lowercase						=						True

        if state.get("""trim_offsets"""   ,							_SCREAMING_SNAKE_CASE				) != trim_offsets:
         __lowercase						=						trim_offsets
         __lowercase						=						True

        if changes_to_apply:
         __lowercase						=						getattr(_SCREAMING_SNAKE_CASE   ,							state.pop("""type"""				)				)
         __lowercase						=						component_class(**_SCREAMING_SNAKE_CASE				)
         setattr(self.backend_tokenizer   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE				)
      @property
      # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''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 SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						AddedToken(_SCREAMING_SNAKE_CASE   ,							lstrip=_SCREAMING_SNAKE_CASE   ,							rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE				) else value
       __lowercase						=						value
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							*A_						:						List[str]   ,							**A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						kwargs.get("""is_split_into_words"""   ,							_SCREAMING_SNAKE_CASE				)
       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(*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							*A_						:						Union[str, Any]   ,							**A_						:						int				):






       '''simple docstring'''


       __lowercase						=						kwargs.get("""is_split_into_words"""   ,							_SCREAMING_SNAKE_CASE				)

       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(*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Dict   ,							A_						:						Tuple = None				):






       '''simple docstring'''


       __lowercase						=						self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,							name=_SCREAMING_SNAKE_CASE				)
       return tuple(_SCREAMING_SNAKE_CASE				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Tuple   ,							A_						:						Union[str, Any] = None				):






       '''simple docstring'''


       __lowercase						=						[self.sep_token_id]
       __lowercase						=						[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]   ,							A_						:						Any   ,							A_						:						str = None				):






       '''simple docstring'''


       return token_ids_a + [self.eos_token_id]






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						[]
       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(_SCREAMING_SNAKE_CASE				)

       __lowercase						=						"""  """.join(_SCREAMING_SNAKE_CASE				)
       __lowercase						=						self.encode(_SCREAMING_SNAKE_CASE				)
       if len(_SCREAMING_SNAKE_CASE				) > self.model_max_length:
        __lowercase						=						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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__    :Tuple       =				{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[Any]       =				['''DeiTFeatureExtractor''']
		lowerCAmelCase__    :Union[str, Any]       =				['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Tuple       =				[
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Union[str, Any]       =				[
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__    :List[str]       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
