






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		Tuple	  =						{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "detr"
							_UpperCAmelCase     =       ["past_key_values"]
							_UpperCAmelCase     =       {
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}
							def __init__(     self:      List[Any]       ,	UpperCamelCase:      List[Any]=True       ,	UpperCamelCase:      List[str]=None       ,	UpperCamelCase:      Union[str, Any]=3       ,	UpperCamelCase:      List[Any]=1_00       ,	UpperCamelCase:      Dict=6       ,	UpperCamelCase:      int=20_48       ,	UpperCamelCase:      Tuple=8       ,	UpperCamelCase:      Optional[Any]=6       ,	UpperCamelCase:      List[str]=20_48       ,	UpperCamelCase:      Any=8       ,	UpperCamelCase:      Optional[Any]=0.0       ,	UpperCamelCase:      str=0.0       ,	UpperCamelCase:      Any=True       ,	UpperCamelCase:      Optional[int]="relu"       ,	UpperCamelCase:      List[Any]=2_56       ,	UpperCamelCase:      List[Any]=0.1       ,	UpperCamelCase:      Tuple=0.0       ,	UpperCamelCase:      Dict=0.0       ,	UpperCamelCase:      Any=0.02       ,	UpperCamelCase:      List[Any]=1.0       ,	UpperCamelCase:      Optional[int]=False       ,	UpperCamelCase:      int="sine"       ,	UpperCamelCase:      str="resnet50"       ,	UpperCamelCase:      Any=True       ,	UpperCamelCase:      str=False       ,	UpperCamelCase:      Tuple=1       ,	UpperCamelCase:      int=5       ,	UpperCamelCase:      int=2       ,	UpperCamelCase:      List[str]=1       ,	UpperCamelCase:      Optional[int]=1       ,	UpperCamelCase:      Union[str, Any]=5       ,	UpperCamelCase:      Optional[Any]=2       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	**UpperCamelCase:      Union[str, Any]       ,	)		->		Optional[Any]:
											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.'						)
																			snake_case__									=     CONFIG_MAPPING['resnet'](out_features=['stage4']						)
															elif isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     backbone_config.get('model_type'						)
																			snake_case__									=     CONFIG_MAPPING[backbone_model_type]
																			snake_case__									=     config_class.from_dict(UpperCamelCase						)
															# set timm attributes to None
															snake_case__							,						snake_case__							,						snake_case__									=     None, None, None

											snake_case__									=     use_timm_backbone
											snake_case__									=     backbone_config
											snake_case__									=     num_channels
											snake_case__									=     num_queries
											snake_case__									=     d_model
											snake_case__									=     encoder_ffn_dim
											snake_case__									=     encoder_layers
											snake_case__									=     encoder_attention_heads
											snake_case__									=     decoder_ffn_dim
											snake_case__									=     decoder_layers
											snake_case__									=     decoder_attention_heads
											snake_case__									=     dropout
											snake_case__									=     attention_dropout
											snake_case__									=     activation_dropout
											snake_case__									=     activation_function
											snake_case__									=     init_std
											snake_case__									=     init_xavier_std
											snake_case__									=     encoder_layerdrop
											snake_case__									=     decoder_layerdrop
											snake_case__									=     encoder_layers
											snake_case__									=     auxiliary_loss
											snake_case__									=     position_embedding_type
											snake_case__									=     backbone
											snake_case__									=     use_pretrained_backbone
											snake_case__									=     dilation
											# Hungarian matcher
											snake_case__									=     class_cost
											snake_case__									=     bbox_cost
											snake_case__									=     giou_cost
											# Loss coefficients
											snake_case__									=     mask_loss_coefficient
											snake_case__									=     dice_loss_coefficient
											snake_case__									=     bbox_loss_coefficient
											snake_case__									=     giou_loss_coefficient
											snake_case__									=     eos_coefficient
											super().__init__(is_encoder_decoder=UpperCamelCase       ,	**UpperCamelCase						)
							@property
							def lowerCAmelCase_ (     self:      int						)		->		int:
											return self.encoder_attention_heads
							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		int:
											return self.d_model
							@classmethod
							def lowerCAmelCase_ (     cls:      List[Any]       ,	UpperCamelCase:      PretrainedConfig       ,	**UpperCamelCase:      List[Any]						)		->		int:
											return cls(backbone_config=UpperCamelCase       ,	**UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict[str, any]:
											snake_case__									=     copy.deepcopy(self.__dict__						)
											if output["backbone_config"] is not None:
															snake_case__									=     self.backbone_config.to_dict()
											snake_case__									=     self.__class__.model_type
											return output


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       version.parse("1.11"	)
							@property
							def lowerCAmelCase_ (     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 lowerCAmelCase_ (     self:      Dict						)		->		float:
											return 1e-5


							@property
							def lowerCAmelCase_ (     self:      Any						)		->		int:
											return 12












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase					:		str	  =						{"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""LayoutXLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
							from .processing_layoutxlm import LayoutXLMProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutxlm import LayoutXLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




