



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     :Union[str, Any]			       =logging.get_logger(__name__)

__snake_case     :List[str]			       ={
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     int								=     'table-transformer'
				A_   :     List[str]								=     ['past_key_values']
				A_   :     Optional[int]								=     {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  Tuple=True						, __UpperCamelCase      :  str=None						, __UpperCamelCase      :  Tuple=3						, __UpperCamelCase      :  Optional[Any]=100						, __UpperCamelCase      :  Dict=6						, __UpperCamelCase      :  Any=2_048						, __UpperCamelCase      :  Dict=8						, __UpperCamelCase      :  Dict=6						, __UpperCamelCase      :  Any=2_048						, __UpperCamelCase      :  List[Any]=8						, __UpperCamelCase      :  Any=0.0						, __UpperCamelCase      :  Optional[int]=0.0						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  List[Any]="relu"						, __UpperCamelCase      :  List[Any]=256						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  str=0.0						, __UpperCamelCase      :  Any=0.0						, __UpperCamelCase      :  Any=0.0_2						, __UpperCamelCase      :  Any=1.0						, __UpperCamelCase      :  Any=False						, __UpperCamelCase      :  Tuple="sine"						, __UpperCamelCase      :  int="resnet50"						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Optional[Any]=False						, __UpperCamelCase      :  Optional[Any]=1						, __UpperCamelCase      :  Any=5						, __UpperCamelCase      :  Optional[int]=2						, __UpperCamelCase      :  List[str]=1						, __UpperCamelCase      :  Dict=1						, __UpperCamelCase      :  Tuple=5						, __UpperCamelCase      :  Union[str, Any]=2						, __UpperCamelCase      :  List[Any]=0.1						, **__UpperCamelCase      :  Tuple						, )		->     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.'       )
																									A      						=    CONFIG_MAPPING['resnet'](out_features=['stage4']       )
																		elif isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																									A      						=    backbone_config.get('model_type'       )
																									A      						=    CONFIG_MAPPING[backbone_model_type]
																									A      						=    config_class.from_dict(__UpperCamelCase       )
																		# set timm attributes to None
																		A						,   A						,   A      						=    None, None, None

											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      						=    bbox_loss_coefficient
											A      						=    giou_loss_coefficient
											A      						=    eos_coefficient
											super().__init__(is_encoder_decoder=__UpperCamelCase						, **__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  List[Any]       )		->     int:
											return self.encoder_attention_heads


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





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Tuple								=     version.parse('1.11'       )


				@property
				def __UpperCamelCase						(							self      :  Union[str, 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      :  Dict       )		->     float:
											return 1e-5


				@property
				def __UpperCamelCase						(							self      :  Tuple       )		->     int:
											return 12






'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
  from .pipeline_kandinsky import KandinskyPipeline
  from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
  from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
  from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
  from .text_encoder import MultilingualCLIP



