






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							:   str							=  logging.get_logger(__name__)

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


class 					_snake_case						(			_lowercase ):
	_A          =   '''conditional_detr'''
	_A          =   ['''past_key_values''']
	_A          =   {
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}






	def __init__(		self					,UpperCamelCase=True					,UpperCamelCase=None					,UpperCamelCase=3					,UpperCamelCase=300					,UpperCamelCase=6					,UpperCamelCase=2_048					,UpperCamelCase=8					,UpperCamelCase=6					,UpperCamelCase=2_048					,UpperCamelCase=8					,UpperCamelCase=0.0					,UpperCamelCase=0.0					,UpperCamelCase=True					,UpperCamelCase="relu"					,UpperCamelCase=256					,UpperCamelCase=0.1					,UpperCamelCase=0.0					,UpperCamelCase=0.0					,UpperCamelCase=0.02					,UpperCamelCase=1.0					,UpperCamelCase=False					,UpperCamelCase="sine"					,UpperCamelCase="resnet50"					,UpperCamelCase=True					,UpperCamelCase=False					,UpperCamelCase=2					,UpperCamelCase=5					,UpperCamelCase=2					,UpperCamelCase=1					,UpperCamelCase=1					,UpperCamelCase=2					,UpperCamelCase=5					,UpperCamelCase=2					,UpperCamelCase=0.25					,**UpperCamelCase					,)						->    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."       )
													snake_case__      :List[Any]         =   CONFIG_MAPPING["resnet"](out_features=["stage4"]       )
									elif isinstance(A_					,A_       ):
													snake_case__      :Tuple         =   backbone_config.get("model_type"       )
													snake_case__      :Dict         =   CONFIG_MAPPING[backbone_model_type]
													snake_case__      :Tuple         =   config_class.from_dict(A_       )

					snake_case__      :Dict         =   use_timm_backbone
					snake_case__      :Dict         =   backbone_config
					snake_case__      :Optional[int]         =   num_channels
					snake_case__      :Union[str, Any]         =   num_queries
					snake_case__      :List[str]         =   d_model
					snake_case__      :str         =   encoder_ffn_dim
					snake_case__      :Optional[int]         =   encoder_layers
					snake_case__      :Any         =   encoder_attention_heads
					snake_case__      :Tuple         =   decoder_ffn_dim
					snake_case__      :Any         =   decoder_layers
					snake_case__      :List[str]         =   decoder_attention_heads
					snake_case__      :List[Any]         =   dropout
					snake_case__      :int         =   attention_dropout
					snake_case__      :List[Any]         =   activation_dropout
					snake_case__      :Dict         =   activation_function
					snake_case__      :Optional[int]         =   init_std
					snake_case__      :int         =   init_xavier_std
					snake_case__      :List[Any]         =   encoder_layerdrop
					snake_case__      :Tuple         =   decoder_layerdrop
					snake_case__      :Union[str, Any]         =   encoder_layers
					snake_case__      :List[str]         =   auxiliary_loss
					snake_case__      :Optional[Any]         =   position_embedding_type
					snake_case__      :Tuple         =   backbone
					snake_case__      :Optional[int]         =   use_pretrained_backbone
					snake_case__      :Optional[Any]         =   dilation
					# Hungarian matcher
					snake_case__      :List[Any]         =   class_cost
					snake_case__      :Tuple         =   bbox_cost
					snake_case__      :Any         =   giou_cost
					# Loss coefficients
					snake_case__      :Optional[int]         =   mask_loss_coefficient
					snake_case__      :Any         =   dice_loss_coefficient
					snake_case__      :Optional[int]         =   cls_loss_coefficient
					snake_case__      :List[Any]         =   bbox_loss_coefficient
					snake_case__      :Dict         =   giou_loss_coefficient
					snake_case__      :Dict         =   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       )						->    Optional[Any]:
					snake_case__      :Optional[int]         =   copy.deepcopy(self.__dict__       )
					if self.backbone_config is not None:
									snake_case__      :str         =   self.backbone_config.to_dict()
					snake_case__      :str         =   self.__class__.model_type
					return output




class 					_snake_case						(			_lowercase ):
	_A          =   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







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__UpperCAmelCase							:   Optional[Any]							=  logging.get_logger(__name__)





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   b.T
				snake_case__      :Optional[Any]         =   np.sum(np.square(__snake_case      )	,	axis=1      )
				snake_case__      :Tuple         =   np.sum(np.square(__snake_case      )	,	axis=0      )
				snake_case__      :Union[str, Any]         =   np.matmul(__snake_case	,	__snake_case      )
				snake_case__      :Union[str, Any]         =   aa[:, None] - 2 * ab + ba[None, :]
				return d





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							int      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   x.reshape(-1	,	3      )
				snake_case__      :List[str]         =   squared_euclidean_distance(__snake_case	,	__snake_case      )
				return np.argmin(__snake_case	,	axis=1      )


class 					_snake_case						(			_A ):
	_A          =   ['pixel_values']






	def __init__(		self					,UpperCamelCase = None					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = True					,UpperCamelCase = True					,**UpperCamelCase					,)						->    None:
					super().__init__(**UpperCamelCase       )
					snake_case__      :List[Any]         =   size if size is not None else {"height": 256, "width": 256}
					snake_case__      :str         =   get_size_dict(UpperCamelCase       )
					snake_case__      :Dict         =   np.array(UpperCamelCase       ) if clusters is not None else None
					snake_case__      :str         =   do_resize
					snake_case__      :List[str]         =   size
					snake_case__      :List[Any]         =   resample
					snake_case__      :Union[str, Any]         =   do_normalize
					snake_case__      :int         =   do_color_quantize






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :List[str]         =   get_size_dict(UpperCamelCase       )
					if "height" not in size or "width" not in size:
									raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'       )
					return resize(
					    UpperCamelCase					,size=(size["height"], size["width"])					,resample=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,)						->    np.ndarray:
					snake_case__      :Tuple         =   rescale(image=UpperCamelCase					,scale=1 / 127.5					,data_format=UpperCamelCase       )
					snake_case__      :List[Any]         =   image - 1
					return image






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,**UpperCamelCase					,)						->    PIL.Image.Image:
					snake_case__      :Optional[int]         =   do_resize if do_resize is not None else self.do_resize
					snake_case__      :int         =   size if size is not None else self.size
					snake_case__      :Tuple         =   get_size_dict(UpperCamelCase       )
					snake_case__      :str         =   resample if resample is not None else self.resample
					snake_case__      :Dict         =   do_normalize if do_normalize is not None else self.do_normalize
					snake_case__      :Tuple         =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					snake_case__      :List[Any]         =   clusters if clusters is not None else self.clusters
					snake_case__      :str         =   np.array(UpperCamelCase       )

					snake_case__      :int         =   make_list_of_images(UpperCamelCase       )

					if not valid_images(UpperCamelCase       ):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray."       )

					if do_resize and size is None or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True."       )

					if do_color_quantize and clusters is None:
									raise ValueError("Clusters must be specified if do_color_quantize is True."       )

					# All transformations expect numpy arrays.
					snake_case__      :Union[str, Any]         =   [to_numpy_array(UpperCamelCase       ) for image in images]

					if do_resize:
									snake_case__      :int         =   [self.resize(image=UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase       ) for image in images]

					if do_normalize:
									snake_case__      :Any         =   [self.normalize(image=UpperCamelCase       ) for image in images]

					if do_color_quantize:
									snake_case__      :Optional[Any]         =   [to_channel_dimension_format(UpperCamelCase					,ChannelDimension.LAST       ) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									snake_case__      :Union[str, Any]         =   np.array(UpperCamelCase       )
									snake_case__      :Optional[int]         =   color_quantize(UpperCamelCase					,UpperCamelCase       ).reshape(images.shape[:-1]       )

									# flatten to (batch_size, height*width)
									snake_case__      :List[Any]         =   images.shape[0]
									snake_case__      :str         =   images.reshape(UpperCamelCase					,-1       )

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									snake_case__      :Any         =   list(UpperCamelCase       )
					else:
									snake_case__      :List[str]         =   [to_channel_dimension_format(UpperCamelCase					,UpperCamelCase       ) for image in images]

					snake_case__      :List[str]         =   {"input_ids": images}
					return BatchFeature(data=UpperCamelCase					,tensor_type=UpperCamelCase       )