

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

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

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "conditional_detr"
						_lowercase        =     ["past_key_values"]
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}


						def __init__(    self  ,   A_=True  ,   A_=None  ,   A_=3  ,   A_=300  ,   A_=6  ,   A_=2048  ,   A_=8  ,   A_=6  ,   A_=2048  ,   A_=8  ,   A_=0.0  ,   A_=0.0  ,   A_=True  ,   A_="relu"  ,   A_=256  ,   A_=0.1  ,   A_=0.0  ,   A_=0.0  ,   A_=0.02  ,   A_=1.0  ,   A_=False  ,   A_="sine"  ,   A_="resnet50"  ,   A_=True  ,   A_=False  ,   A_=2  ,   A_=5  ,   A_=2  ,   A_=1  ,   A_=1  ,   A_=2  ,   A_=5  ,   A_=2  ,   A_=0.25  ,   **A_  ,   ):







									'''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    :						Tuple							=      CONFIG_MAPPING["resnet"](out_features=["stage4"]		)
												elif isinstance(A_  ,   A_		):
															_UpperCAmelCase    :						Tuple							=      backbone_config.get("model_type"		)
															_UpperCAmelCase    :						str							=      CONFIG_MAPPING[backbone_model_type]
															_UpperCAmelCase    :						Tuple							=      config_class.from_dict(A_		)

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


						@property
						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									return self.encoder_attention_heads


						@property
						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									return self.d_model





						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


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







class 			a					(		UpperCAmelCase			):
						_lowercase        =     version.parse("1.11"			)


						@property
						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


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


						@property
						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									return 1e-5





						@property
						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									return 12





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


