




"""simple docstring"""







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


a      =							logging.get_logger(__name__)

a      =							{
    '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					):
						_a    						=						'detr'
						_a    						=						['past_key_values']
						_a    						=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						}


						def __init__(							self				:			Optional[int]						,						lowerCAmelCase				:			Optional[Any]=True						,						lowerCAmelCase				:			Union[str, Any]=None						,						lowerCAmelCase				:			Optional[int]=3						,						lowerCAmelCase				:			Optional[int]=100						,						lowerCAmelCase				:			str=6						,						lowerCAmelCase				:			List[str]=2048						,						lowerCAmelCase				:			List[str]=8						,						lowerCAmelCase				:			str=6						,						lowerCAmelCase				:			str=2048						,						lowerCAmelCase				:			Optional[int]=8						,						lowerCAmelCase				:			Any=0.0						,						lowerCAmelCase				:			str=0.0						,						lowerCAmelCase				:			Any=True						,						lowerCAmelCase				:			str="relu"						,						lowerCAmelCase				:			Union[str, Any]=256						,						lowerCAmelCase				:			Union[str, Any]=0.1						,						lowerCAmelCase				:			Dict=0.0						,						lowerCAmelCase				:			Optional[int]=0.0						,						lowerCAmelCase				:			List[str]=0.02						,						lowerCAmelCase				:			Dict=1.0						,						lowerCAmelCase				:			Tuple=False						,						lowerCAmelCase				:			List[Any]="sine"						,						lowerCAmelCase				:			List[str]="resnet50"						,						lowerCAmelCase				:			int=True						,						lowerCAmelCase				:			Optional[Any]=False						,						lowerCAmelCase				:			Tuple=1						,						lowerCAmelCase				:			Optional[int]=5						,						lowerCAmelCase				:			List[str]=2						,						lowerCAmelCase				:			int=1						,						lowerCAmelCase				:			List[str]=1						,						lowerCAmelCase				:			Optional[Any]=5						,						lowerCAmelCase				:			List[str]=2						,						lowerCAmelCase				:			Dict=0.1						,						**lowerCAmelCase				:			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."""					)
																		lowerCAmelCase  =				CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
														elif isinstance(lowerCAmelCase						,						lowerCAmelCase					):
																		lowerCAmelCase  =				backbone_config.get("""model_type"""					)
																		lowerCAmelCase  =				CONFIG_MAPPING[backbone_model_type]
																		lowerCAmelCase  =				config_class.from_dict(lowerCAmelCase					)
														# 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=lowerCAmelCase						,						**lowerCAmelCase					)


						@property
						def   __lowercase (							self				:			List[Any]					):
										return self.encoder_attention_heads


						@property
						def   __lowercase (							self				:			Optional[Any]					):
										return self.d_model


						@classmethod
						def   __lowercase (							cls				:			str						,						lowerCAmelCase				:			PretrainedConfig						,						**lowerCAmelCase				:			Dict					):
										return cls(backbone_config=lowerCAmelCase						,						**lowerCAmelCase					)




						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				copy.deepcopy(self.__dict__					)
										if output["backbone_config"] is not None:
														lowerCAmelCase  =				self.backbone_config.to_dict()
										lowerCAmelCase  =				self.__class__.model_type
										return output






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						version.parse('1.11'					)


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


						@property
						def   __lowercase (							self				:			Any					):
										return 1e-5




						@property
						def   __lowercase (							self				:			Optional[Any]					):
										return 12











"""simple docstring"""







from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      =							logging.get_logger(__name__)

a      =							{
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'autoformer'
						_a    						=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			str = "student_t"						,						lowerCAmelCase				:			str = "nll"						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			List[int] = [1, 2, 3, 4, 5, 6, 7]						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			int = 64						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			str = "gelu"						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			int = 100						,						lowerCAmelCase				:			float = 0.02						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			Tuple=True						,						lowerCAmelCase				:			int = 10						,						lowerCAmelCase				:			int = 25						,						lowerCAmelCase				:			int = 3						,						**lowerCAmelCase				:			Tuple						,						):
										# time series specific configuration
										lowerCAmelCase  =				prediction_length
										lowerCAmelCase  =				context_length if context_length is not None else prediction_length
										lowerCAmelCase  =				distribution_output
										lowerCAmelCase  =				loss
										lowerCAmelCase  =				input_size
										lowerCAmelCase  =				num_time_features
										lowerCAmelCase  =				lags_sequence
										lowerCAmelCase  =				scaling
										lowerCAmelCase  =				num_dynamic_real_features
										lowerCAmelCase  =				num_static_real_features
										lowerCAmelCase  =				num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(lowerCAmelCase					) != num_static_categorical_features:
																		raise ValueError(
																		    """The cardinality should be a list of the same length as `num_static_categorical_features`"""					)
														lowerCAmelCase  =				cardinality
										else:
														lowerCAmelCase  =				[0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(lowerCAmelCase					) != num_static_categorical_features:
																		raise ValueError(
																		    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""					)
														lowerCAmelCase  =				embedding_dimension
										else:
														lowerCAmelCase  =				[min(50						,						(cat + 1) // 2					) for cat in self.cardinality]
										lowerCAmelCase  =				num_parallel_samples

										# Transformer architecture configuration
										lowerCAmelCase  =				input_size * len(self.lags_sequence					) + self._number_of_features
										lowerCAmelCase  =				d_model
										lowerCAmelCase  =				encoder_attention_heads
										lowerCAmelCase  =				decoder_attention_heads
										lowerCAmelCase  =				encoder_ffn_dim
										lowerCAmelCase  =				decoder_ffn_dim
										lowerCAmelCase  =				encoder_layers
										lowerCAmelCase  =				decoder_layers

										lowerCAmelCase  =				dropout
										lowerCAmelCase  =				attention_dropout
										lowerCAmelCase  =				activation_dropout
										lowerCAmelCase  =				encoder_layerdrop
										lowerCAmelCase  =				decoder_layerdrop

										lowerCAmelCase  =				activation_function
										lowerCAmelCase  =				init_std

										lowerCAmelCase  =				use_cache

										# Autoformer
										lowerCAmelCase  =				label_length
										lowerCAmelCase  =				moving_average
										lowerCAmelCase  =				autocorrelation_factor

										super().__init__(is_encoder_decoder=lowerCAmelCase						,						**lowerCAmelCase					)




						@property
						def   __lowercase (							self				:			Tuple					):
										return (
										    sum(self.embedding_dimension					)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)





