

"""simple docstring"""




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


_lowercase   :	int												=						logging.get_logger(__name__)

_lowercase   :	Any												=						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Optional[Any]					=					"conditional_detr"
	a__		: Tuple					=					["past_key_values"]
	a__		: Optional[Any]					=					{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}



	def __init__(  self     :       Dict			,    _lowercase     :       Optional[int]=True			,    _lowercase     :       Union[str, Any]=None			,    _lowercase     :       Union[str, Any]=3			,    _lowercase     :       Optional[Any]=3_00			,    _lowercase     :       Tuple=6			,    _lowercase     :       Optional[int]=20_48			,    _lowercase     :       List[str]=8			,    _lowercase     :       Dict=6			,    _lowercase     :       Any=20_48			,    _lowercase     :       str=8			,    _lowercase     :       Any=0.0			,    _lowercase     :       Optional[int]=0.0			,    _lowercase     :       Tuple=True			,    _lowercase     :       Optional[Any]="relu"			,    _lowercase     :       str=2_56			,    _lowercase     :       List[Any]=0.1			,    _lowercase     :       List[Any]=0.0			,    _lowercase     :       str=0.0			,    _lowercase     :       Optional[int]=0.02			,    _lowercase     :       Tuple=1.0			,    _lowercase     :       List[str]=False			,    _lowercase     :       Tuple="sine"			,    _lowercase     :       Any="resnet50"			,    _lowercase     :       Tuple=True			,    _lowercase     :       Optional[Any]=False			,    _lowercase     :       Dict=2			,    _lowercase     :       str=5			,    _lowercase     :       Union[str, Any]=2			,    _lowercase     :       List[str]=1			,    _lowercase     :       Union[str, Any]=1			,    _lowercase     :       Tuple=2			,    _lowercase     :       str=5			,    _lowercase     :       Optional[Any]=2			,    _lowercase     :       Optional[int]=0.25			,    **_lowercase     :       Tuple			,    ):
		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.'''						)
				__UpperCAmelCase      		= CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']						)
			elif isinstance(_lowercase			,    _lowercase						):
				__UpperCAmelCase      		= backbone_config.get('''model_type'''						)
				__UpperCAmelCase      		= CONFIG_MAPPING[backbone_model_type]
				__UpperCAmelCase      		= config_class.from_dict(_lowercase						)

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



	@property
	def        a  (  self     :       Tuple						):
		return self.encoder_attention_heads



	@property
	def        a  (  self     :       Optional[int]						):
		return self.d_model



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






class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Optional[Any]					=					version.parse("1.11"       )



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



	@property
	def        a  (  self     :       Optional[Any]						):
		return 1E-5



	@property
	def        a  (  self     :       Optional[int]						):
		return 12




from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__    :Any       =				logging.get_logger(__name__)

lowerCAmelCase__    :Union[str, Any]       =				{
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Any           =      'xmod'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=30522     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=3072     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE="absolute"     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=("en_XX",)     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     type_vocab_size
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     position_embedding_type
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     classifier_dropout
									_UpperCAmelCase											=     pre_norm
									_UpperCAmelCase											=     adapter_reduction_factor
									_UpperCAmelCase											=     adapter_layer_norm
									_UpperCAmelCase											=     adapter_reuse_layer_norm
									_UpperCAmelCase											=     ln_before_adapter
									_UpperCAmelCase											=     list(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     default_language



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
