






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


A:      List[Any]  				=				logging.get_logger(__name__)

A:      Optional[Any]  				=				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}






class 							__magic_name__     (	__lowercase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[Any]        =  'conditional_detr'
	SCREAMING_SNAKE_CASE_					:      Dict        =  ['past_key_values']
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  {
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	}
	def __init__(							self  ,      _lowercase=True  ,      _lowercase=None  ,      _lowercase=3  ,      _lowercase=300  ,      _lowercase=6  ,      _lowercase=2048  ,      _lowercase=8  ,      _lowercase=6  ,      _lowercase=2048  ,      _lowercase=8  ,      _lowercase=0.0  ,      _lowercase=0.0  ,      _lowercase=True  ,      _lowercase="relu"  ,      _lowercase=256  ,      _lowercase=0.1  ,      _lowercase=0.0  ,      _lowercase=0.0  ,      _lowercase=0.02  ,      _lowercase=1.0  ,      _lowercase=False  ,      _lowercase="sine"  ,      _lowercase="resnet50"  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=2  ,      _lowercase=5  ,      _lowercase=2  ,      _lowercase=1  ,      _lowercase=1  ,      _lowercase=2  ,      _lowercase=5  ,      _lowercase=2  ,      _lowercase=0.25  ,      **_lowercase  ,      )						->  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.'       )
																			lowercase_      :   Union[str, Any]        =			CONFIG_MAPPING['resnet'](out_features=['stage4']       )
													elif isinstance(__A  ,      __A       ):
																			lowercase_      :   List[Any]        =			backbone_config.get('model_type'       )
																			lowercase_      :   int        =			CONFIG_MAPPING[backbone_model_type]
																			lowercase_      :   Dict        =			config_class.from_dict(__A       )

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







	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   Dict        =			copy.deepcopy(self.__dict__       )
							if self.backbone_config is not None:
													lowercase_      :   List[Any]        =			self.backbone_config.to_dict()
							lowercase_      :   Any        =			self.__class__.model_type
							return output






class 							__magic_name__     (	__lowercase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  version.parse('1.11'				)
	@property
	def        lowerCamelCase__		(							self       )						->  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       )						->  float:
							return 1E-5







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








'''simple docstring'''

from __future__ import annotations






def 	_UpperCAmelCase    (  a		:	int = 4							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   Tuple        =			abs(a							) or 4
						return [[1 + x + y * row_size for x in range(a							)] for y in range(a							)]






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						return reverse_row(transpose(a							)							)
						# OR.. transpose(reverse_column(matrix))






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						return reverse_row(reverse_column(a							)							)
						# OR.. reverse_column(reverse_row(matrix))






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						return reverse_column(transpose(a							)							)
						# OR.. transpose(reverse_row(matrix))






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   Any        =			[list(a							) for x in zip(*a							)]
						return matrix






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			matrix[::-1]
						return matrix






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			list[list[int]]:





						"""simple docstring"""
						lowercase_      :   str        =			[x[::-1] for x in matrix]
						return matrix






def 	_UpperCAmelCase    (  a		:	list[list[int]]							)			->			None:





						"""simple docstring"""
						for i in matrix:
												print(*a							)


if __name__ == "__main__":
						A:      Dict  				=				make_matrix()
						print("\norigin:\n")
						print_matrix(matrix)
						print("\nrotate 90 counterclockwise:\n")
						print_matrix(rotate_aa(matrix))

						A:      List[Any]  				=				make_matrix()
						print("\norigin:\n")
						print_matrix(matrix)
						print("\nrotate 180:\n")
						print_matrix(rotate_aaa(matrix))

						A:      List[str]  				=				make_matrix()
						print("\norigin:\n")
						print_matrix(matrix)
						print("\nrotate 270 counterclockwise:\n")
						print_matrix(rotate_aaa(matrix))
