





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__: Dict							   =						logging.get_logger(__name__)


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

a__: Dict							   =						{
    '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__: Any							   =						{'facebook/blenderbot-3B': 128}




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							=      PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							=      ['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							=      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,):
        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,)

        A__		   =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
        if pre_tok_state.get('''add_prefix_space''',__lowerCamelCase					) != add_prefix_space:
               A__		   =		getattr(__lowerCamelCase,pre_tok_state.pop('''type'''					)					)
               A__		   =		add_prefix_space
               A__		   =		pre_tok_class(**__lowerCamelCase					)

        A__		   =		add_prefix_space

        A__		   =		'''post_processor'''
        A__		   =		getattr(self.backend_tokenizer,__lowerCamelCase,__lowerCamelCase					)
        if tokenizer_component_instance:
               A__		   =		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__		   =		tuple(state['''sep''']					)
               if "cls" in state:
                      A__		   =		tuple(state['''cls''']					)

               A__		   =		False

               if state.get('''add_prefix_space''',__lowerCamelCase					) != add_prefix_space:
                      A__		   =		add_prefix_space
                      A__		   =		True

               if state.get('''trim_offsets''',__lowerCamelCase					) != trim_offsets:
                      A__		   =		trim_offsets
                      A__		   =		True

               if changes_to_apply:
                      A__		   =		getattr(__lowerCamelCase,state.pop('''type'''					)					)
                      A__		   =		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 							UpperCamelCase     (				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 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__		   =		AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase,__lowerCamelCase					) else value
        A__		   =		value



 def 							UpperCamelCase     (				self,*__lowerCamelCase,**__lowerCamelCase					):
        A__		   =		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 							UpperCamelCase     (				self,*__lowerCamelCase,**__lowerCamelCase					):
        A__		   =		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 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase = None					):
        A__		   =		self._tokenizer.model.save(__lowerCamelCase,name=__lowerCamelCase					)
        return tuple(__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase = None					):
        A__		   =		[self.sep_token_id]
        A__		   =		[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 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase = None					):
        return token_ids_a + [self.eos_token_id]




 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__		   =		[]
        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					)

        A__		   =		'''  '''.join(__lowerCamelCase					)
        A__		   =		self.encode(__lowerCamelCase					)
        if len(__lowerCamelCase					) > self.model_max_length:
               A__		   =		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












import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

a__: Any							   =						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''conditional_detr'''
 __SCREAMING_SNAKE_CASE							=      ['''past_key_values''']
 __SCREAMING_SNAKE_CASE							=      {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }



 def __init__(				self,__lowerCamelCase=True,__lowerCamelCase=None,__lowerCamelCase=3,__lowerCamelCase=300,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=1.0,__lowerCamelCase=False,__lowerCamelCase="sine",__lowerCamelCase="resnet50",__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=1,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=0.25,**__lowerCamelCase,):
        if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''					)

        if not use_timm_backbone:
               if backbone_config is None:
                      logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''					)
                      A__		   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
               elif isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		backbone_config.get('''model_type'''					)
                      A__		   =		CONFIG_MAPPING[backbone_model_type]
                      A__		   =		config_class.from_dict(__lowerCamelCase					)

        A__		   =		use_timm_backbone
        A__		   =		backbone_config
        A__		   =		num_channels
        A__		   =		num_queries
        A__		   =		d_model
        A__		   =		encoder_ffn_dim
        A__		   =		encoder_layers
        A__		   =		encoder_attention_heads
        A__		   =		decoder_ffn_dim
        A__		   =		decoder_layers
        A__		   =		decoder_attention_heads
        A__		   =		dropout
        A__		   =		attention_dropout
        A__		   =		activation_dropout
        A__		   =		activation_function
        A__		   =		init_std
        A__		   =		init_xavier_std
        A__		   =		encoder_layerdrop
        A__		   =		decoder_layerdrop
        A__		   =		encoder_layers
        A__		   =		auxiliary_loss
        A__		   =		position_embedding_type
        A__		   =		backbone
        A__		   =		use_pretrained_backbone
        A__		   =		dilation
        # Hungarian matcher
        A__		   =		class_cost
        A__		   =		bbox_cost
        A__		   =		giou_cost
        # Loss coefficients
        A__		   =		mask_loss_coefficient
        A__		   =		dice_loss_coefficient
        A__		   =		cls_loss_coefficient
        A__		   =		bbox_loss_coefficient
        A__		   =		giou_loss_coefficient
        A__		   =		focal_alpha
        super().__init__(is_encoder_decoder=__lowerCamelCase,**__lowerCamelCase					)



 @property
 def 							UpperCamelCase     (				self					):
        return self.encoder_attention_heads



 @property
 def 							UpperCamelCase     (				self					):
        return self.d_model




 def 							UpperCamelCase     (				self					):
        A__		   =		copy.deepcopy(self.__dict__					)
        if self.backbone_config is not None:
               A__		   =		self.backbone_config.to_dict()
        A__		   =		self.__class__.model_type
        return output




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      version.parse('''1.11''' )



 @property
 def 							UpperCamelCase     (				self					):
        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ('''pixel_mask''', {0: '''batch'''}),
            ]					)



 @property
 def 							UpperCamelCase     (				self					):
        return 1E-5




 @property
 def 							UpperCamelCase     (				self					):
        return 12





