


"""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


UpperCAmelCase    :     List[str]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     int   						=		{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							"""conditional_detr"""
					__a	       =							["""past_key_values"""]
					__a	       =							{
					    """hidden_size""": """d_model""",
					    """num_attention_heads""": """encoder_attention_heads""",
					}






					def __init__(       self    : Tuple ,       UpperCamelCase    : int=True ,       UpperCamelCase    : Union[str, Any]=None ,       UpperCamelCase    : int=3 ,       UpperCamelCase    : List[str]=300 ,       UpperCamelCase    : Optional[Any]=6 ,       UpperCamelCase    : int=2_048 ,       UpperCamelCase    : List[Any]=8 ,       UpperCamelCase    : Any=6 ,       UpperCamelCase    : str=2_048 ,       UpperCamelCase    : Optional[int]=8 ,       UpperCamelCase    : Union[str, Any]=0.0 ,       UpperCamelCase    : Dict=0.0 ,       UpperCamelCase    : Any=True ,       UpperCamelCase    : Optional[int]="relu" ,       UpperCamelCase    : Any=256 ,       UpperCamelCase    : List[Any]=0.1 ,       UpperCamelCase    : str=0.0 ,       UpperCamelCase    : List[Any]=0.0 ,       UpperCamelCase    : Any=0.02 ,       UpperCamelCase    : List[str]=1.0 ,       UpperCamelCase    : List[str]=False ,       UpperCamelCase    : List[str]="sine" ,       UpperCamelCase    : Any="resnet50" ,       UpperCamelCase    : Union[str, Any]=True ,       UpperCamelCase    : Optional[int]=False ,       UpperCamelCase    : Optional[Any]=2 ,       UpperCamelCase    : Tuple=5 ,       UpperCamelCase    : Union[str, Any]=2 ,       UpperCamelCase    : List[Any]=1 ,       UpperCamelCase    : Any=1 ,       UpperCamelCase    : int=2 ,       UpperCamelCase    : List[Any]=5 ,       UpperCamelCase    : Optional[int]=2 ,       UpperCamelCase    : Dict=0.25 ,       **UpperCamelCase    : Union[str, Any] ,       ):
												'''simple docstring'''






												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   :						List[Any]      							=					CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]  )
																			elif isinstance(UpperCamelCase ,       UpperCamelCase  ):
																										__UpperCAmelCase   :						Any      							=					backbone_config.get("""model_type"""  )
																										__UpperCAmelCase   :						List[Any]      							=					CONFIG_MAPPING[backbone_model_type]
																										__UpperCAmelCase   :						Dict      							=					config_class.from_dict(UpperCamelCase  )

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






					@property
					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												return self.encoder_attention_heads






					@property
					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												return self.d_model






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					copy.deepcopy(self.__dict__  )
												if self.backbone_config is not None:
																			__UpperCAmelCase   :						Any      							=					self.backbone_config.to_dict()
												__UpperCAmelCase   :						Optional[int]      							=					self.__class__.model_type
												return output






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							version.parse("""1.11"""				)






					@property
					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												return OrderedDict(
												    [
												        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
												        ("""pixel_mask""", {0: """batch"""}),
												    ]  )






					@property
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												return 1e-5






					@property
					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												return 12




"""simple docstring"""






import numpy as np

UpperCAmelCase    :     Optional[Any]   						=		[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					np.array(UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					np.where(letter == self.SQUARE  )
												__UpperCAmelCase   :						Optional[Any]      							=					np.concatenate([indexa + 1, indexa + 1]  )
												return indexes






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.SQUARE[indexa - 1, indexa - 1]
												return letter






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					message.lower()
												__UpperCAmelCase   :						List[Any]      							=					message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						List[Any]      							=					message.replace("""j""" ,       """i"""  )

												__UpperCAmelCase   :						Optional[int]      							=					np.empty((2, len(UpperCamelCase  ))  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						List[Any]      							=					self.letter_to_numbers(message[letter_index]  )

																			__UpperCAmelCase   :						str      							=					numbers[0]
																			__UpperCAmelCase   :						int      							=					numbers[1]

												__UpperCAmelCase   :						Union[str, Any]      							=					first_step.reshape(2 * len(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					int(second_step[numbers_index * 2]  )
																			__UpperCAmelCase   :						Any      							=					int(second_step[(numbers_index * 2) + 1]  )
																			__UpperCAmelCase   :						str      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[Any]      							=					encoded_message + letter

												return encoded_message






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					message.lower()
												message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						int      							=					np.empty(2 * len(UpperCamelCase  )  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					self.letter_to_numbers(message[letter_index]  )
																			__UpperCAmelCase   :						Any      							=					numbers[0]
																			__UpperCAmelCase   :						Dict      							=					numbers[1]

												__UpperCAmelCase   :						str      							=					first_step.reshape((2, len(UpperCamelCase  ))  )
												__UpperCAmelCase   :						Union[str, Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Optional[int]      							=					int(second_step[0, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					int(second_step[1, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					decoded_message + letter

												return decoded_message
