


'''simple docstring'''




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


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Optional[int]		=     '''table-transformer'''
     lowerCamelCase				:	List[str]		=     ['''past_key_values''']
     lowerCamelCase				:	List[Any]		=     {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      List[Any]=None    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      Tuple=1_0_0    ,      UpperCAmelCase__       :      Any=6    ,      UpperCAmelCase__       :      int=2_0_4_8    ,      UpperCAmelCase__       :      List[str]=8    ,      UpperCAmelCase__       :      Union[str, Any]=6    ,      UpperCAmelCase__       :      Tuple=2_0_4_8    ,      UpperCAmelCase__       :      int=8    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      List[Any]="relu"    ,      UpperCAmelCase__       :      Dict=2_5_6    ,      UpperCAmelCase__       :      Any=0.1    ,      UpperCAmelCase__       :      Union[str, Any]=0.0    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      Optional[Any]=0.02    ,      UpperCAmelCase__       :      Union[str, Any]=1.0    ,      UpperCAmelCase__       :      Any=False    ,      UpperCAmelCase__       :      List[Any]="sine"    ,      UpperCAmelCase__       :      Optional[int]="resnet50"    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      UpperCAmelCase__       :      Any=False    ,      UpperCAmelCase__       :      int=1    ,      UpperCAmelCase__       :      Dict=5    ,      UpperCAmelCase__       :      Tuple=2    ,      UpperCAmelCase__       :      List[Any]=1    ,      UpperCAmelCase__       :      Optional[int]=1    ,      UpperCAmelCase__       :      Dict=5    ,      UpperCAmelCase__       :      List[Any]=2    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      **UpperCAmelCase__       :      Union[str, Any]    ,      )				->  Optional[int]:
        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.'    )
              lowerCAmelCase     =			CONFIG_MAPPING['resnet'](out_features=['stage4']    )
           elif isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			backbone_config.get('model_type'    )
              lowerCAmelCase     =			CONFIG_MAPPING[backbone_model_type]
              lowerCAmelCase     =			config_class.from_dict(UpperCAmelCase__    )
           # set timm attributes to None
           lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			None, None, None

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





     @property
     def 							__UpperCAmelCase      ( self       :      int    )				->  int:
        return self.encoder_attention_heads





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  int:
        return self.d_model




class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Dict		=     version.parse('''1.11'''		)





     @property
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ('pixel_mask', {0: 'batch'}),
            ]    )





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  float:
        return 1E-5





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        return 1_2









'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

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

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[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]





