






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

__magic_name__:					Optional[Any]							=     logging.get_logger(__name__)


__magic_name__:					List[str]							=     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__magic_name__:					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'''
    },
}

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





class 	snake_case__ (							a__				):
 lowercase__						:					Optional[Any]											=   VOCAB_FILES_NAMES
 lowercase__						:					Optional[int]											=   PRETRAINED_VOCAB_FILES_MAP
 lowercase__						:					Optional[Any]											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__						:					Optional[int]											=   ['input_ids', 'attention_mask']
 lowercase__						:					Union[str, Any]											=   BlenderbotTokenizer
 def __init__(    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__="replace"      ,       lowerCAmelCase__="<s>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="<s>"      ,       lowerCAmelCase__="<unk>"      ,       lowerCAmelCase__="<pad>"      ,       lowerCAmelCase__="<mask>"      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=True      ,       **lowerCAmelCase__      ,       )							->				Any:
       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      ,       )

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

       __magic_name__      :       Optional[Any]       						= add_prefix_space

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

             __magic_name__      :       Union[str, Any]       						= False

             if state.get("""add_prefix_space"""      ,       _lowerCamelCase			) != add_prefix_space:
                   __magic_name__      :       int       						= add_prefix_space
                   __magic_name__      :       Union[str, Any]       						= True

             if state.get("""trim_offsets"""      ,       _lowerCamelCase			) != trim_offsets:
                   __magic_name__      :       str       						= trim_offsets
                   __magic_name__      :       int       						= True

             if changes_to_apply:
                   __magic_name__      :       str       						= getattr(_lowerCamelCase      ,       state.pop("""type"""			)			)
                   __magic_name__      :       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       __magic_name__    (    self			)							->				str:
       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       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[str]:
       __magic_name__      :       str       						= AddedToken(_lowerCamelCase      ,       lstrip=_lowerCamelCase      ,       rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase      ,       _lowerCamelCase			) else value
       __magic_name__      :       Tuple       						= value
 def       __magic_name__    (    self      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				BatchEncoding:
       __magic_name__      :       int       						= 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       __magic_name__    (    self      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				BatchEncoding:
       __magic_name__      :       List[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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
       __magic_name__      :       int       						= self._tokenizer.model.save(_lowerCamelCase      ,       name=_lowerCamelCase			)
       return tuple(_lowerCamelCase			)
 def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
       __magic_name__      :       Optional[Any]       						= [self.sep_token_id]
       __magic_name__      :       Dict       						= [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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Optional[Any]:
       return token_ids_a + [self.eos_token_id]







 def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[int]:
       __magic_name__      :       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(_lowerCamelCase			)

       __magic_name__      :       int       						= '''  '''.join(_lowerCamelCase			)
       __magic_name__      :       List[Any]       						= self.encode(_lowerCamelCase			)
       if len(_lowerCamelCase			) > self.model_max_length:
             __magic_name__      :       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_torch_available, is_vision_available


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

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