


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


lowerCAmelCase			=				logging.get_logger(__name__)
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		def constraint_to_multiple_of(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=0		,							SCREAMING_SNAKE_CASE=None	):
				lowercase__				= round(val / multiple	) * multiple

				if max_val is not None and x > max_val:
						lowercase__				= math.floor(val / multiple	) * multiple

				if x < min_val:
						lowercase__				= math.ceil(val / multiple	) * multiple

				return x

		lowercase__				= (output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	) else output_size

		lowercase__	,						lowercase__				= get_image_size(SCREAMING_SNAKE_CASE	)
		lowercase__	,						lowercase__				= output_size

		# determine new height and width
		lowercase__				= output_height / input_height
		lowercase__				= output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width	) < abs(1 - scale_height	):
						# fit width
						lowercase__				= scale_width
				else:
						# fit height
						lowercase__				= scale_height

		lowercase__				= constraint_to_multiple_of(scale_height * input_height		,							multiple=SCREAMING_SNAKE_CASE	)
		lowercase__				= constraint_to_multiple_of(scale_width * input_width		,							multiple=SCREAMING_SNAKE_CASE	)

		return (new_height, new_width)


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     Optional[Any]     =					['''pixel_values''']

							def __init__(				self:      Optional[Any]       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Dict[str, int] = None       ,		UpperCamelCase_:      PILImageResampling = PILImageResampling.BILINEAR       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      int = 1       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Union[int, float] = 1 / 255       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		**UpperCamelCase_:      Tuple       ,		)     ->  None:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_				)
									lowercase__				= size if size is not None else {'''height''': 384, '''width''': 384}
									lowercase__				= get_size_dict(UpperCamelCase_				)
									lowercase__				= do_resize
									lowercase__				= size
									lowercase__				= keep_aspect_ratio
									lowercase__				= ensure_multiple_of
									lowercase__				= resample
									lowercase__				= do_rescale
									lowercase__				= rescale_factor
									lowercase__				= do_normalize
									lowercase__				= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase__				= image_std if image_std is not None else IMAGENET_STANDARD_STD

							def  lowerCamelCase_					(				self:      Any       ,		UpperCamelCase_:      np.ndarray       ,		UpperCamelCase_:      Dict[str, int]       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      int = 1       ,		UpperCamelCase_:      PILImageResampling = PILImageResampling.BICUBIC       ,		UpperCamelCase_:      Optional[Union[str, ChannelDimension]] = None       ,		**UpperCamelCase_:      int       ,		)     ->  np.ndarray:





									"""simple docstring"""







									lowercase__				= get_size_dict(UpperCamelCase_				)
									if "height" not in size or "width" not in size:
											raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'				)
									lowercase__				= get_resize_output_image_size(
									    UpperCamelCase_       ,		output_size=(size['''height'''], size['''width'''])       ,		keep_aspect_ratio=UpperCamelCase_       ,		multiple=UpperCamelCase_       ,		)
									return resize(UpperCamelCase_       ,		size=UpperCamelCase_       ,		resample=UpperCamelCase_       ,		data_format=UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Optional[Any]       ,		UpperCamelCase_:      np.ndarray       ,		UpperCamelCase_:      Union[int, float]       ,		UpperCamelCase_:      Optional[Union[str, ChannelDimension]] = None       ,		**UpperCamelCase_:      Any       ,		)     ->  List[Any]:





									"""simple docstring"""







									return rescale(UpperCamelCase_       ,		scale=UpperCamelCase_       ,		data_format=UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      np.ndarray       ,		UpperCamelCase_:      Union[float, List[float]]       ,		UpperCamelCase_:      Union[float, List[float]]       ,		UpperCamelCase_:      Optional[Union[str, ChannelDimension]] = None       ,		**UpperCamelCase_:      Tuple       ,		)     ->  np.ndarray:





									"""simple docstring"""







									return normalize(UpperCamelCase_       ,		mean=UpperCamelCase_       ,		std=UpperCamelCase_       ,		data_format=UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      ImageInput       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      int = None       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      int = None       ,		UpperCamelCase_:      PILImageResampling = None       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      float = None       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		UpperCamelCase_:      Optional[Union[str, TensorType]] = None       ,		UpperCamelCase_:      ChannelDimension = ChannelDimension.FIRST       ,		**UpperCamelCase_:      List[Any]       ,		)     ->  PIL.Image.Image:





									"""simple docstring"""







									lowercase__				= do_resize if do_resize is not None else self.do_resize
									lowercase__				= size if size is not None else self.size
									lowercase__				= get_size_dict(UpperCamelCase_				)
									lowercase__				= keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									lowercase__				= ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									lowercase__				= resample if resample is not None else self.resample
									lowercase__				= do_rescale if do_rescale is not None else self.do_rescale
									lowercase__				= rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__				= do_normalize if do_normalize is not None else self.do_normalize
									lowercase__				= image_mean if image_mean is not None else self.image_mean
									lowercase__				= image_std if image_std is not None else self.image_std

									lowercase__				= 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_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

									if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

									# All transformations expect numpy arrays.
									lowercase__				= [to_numpy_array(UpperCamelCase_				) for image in images]

									if do_resize:
											lowercase__				= [self.resize(image=UpperCamelCase_       ,		size=UpperCamelCase_       ,		resample=UpperCamelCase_				) for image in images]

									if do_rescale:
											lowercase__				= [self.rescale(image=UpperCamelCase_       ,		scale=UpperCamelCase_				) for image in images]

									if do_normalize:
											lowercase__				= [self.normalize(image=UpperCamelCase_       ,		mean=UpperCamelCase_       ,		std=UpperCamelCase_				) for image in images]

									lowercase__				= [to_channel_dimension_format(UpperCamelCase_       ,		UpperCamelCase_				) for image in images]

									lowercase__				= {'''pixel_values''': images}
									return BatchFeature(data=UpperCamelCase_       ,		tensor_type=UpperCamelCase_				)


							def  lowerCamelCase_					(				self:      List[Any]       ,		UpperCamelCase_:      str       ,		UpperCamelCase_:      List[Tuple] = None				)     ->  int:





									"""simple docstring"""







									lowercase__				= outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
											if len(UpperCamelCase_				) != len(UpperCamelCase_				):
													raise ValueError(
													    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

											if is_torch_tensor(UpperCamelCase_				):
													lowercase__				= target_sizes.numpy()

											lowercase__				= []

											for idx in range(len(UpperCamelCase_				)				):
													lowercase__				= torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0				)       ,		size=target_sizes[idx]       ,		mode='''bilinear'''       ,		align_corners=UpperCamelCase_				)
													lowercase__				= resized_logits[0].argmax(dim=0				)
													semantic_segmentation.append(UpperCamelCase_				)
									else:
											lowercase__				= logits.argmax(dim=1				)
											lowercase__				= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

									return semantic_segmentation




'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

lowercase     :							int       						=     {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''deta'''
     __magic_name__          =						{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }





     def __init__(				self		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=900		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=1024		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=256		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE="sine"		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=300		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.25		,     **SCREAMING_SNAKE_CASE		,     )					->							Any:






         """simple docstring"""






         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
             A			:  Optional[Any]   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']      )
         else:
             if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 A			:  List[Any]   =		backbone_config.pop('''model_type'''      )
                 A			:  List[Any]   =		CONFIG_MAPPING[backbone_model_type]
                 A			:  Optional[int]   =		config_class.from_dict(SCREAMING_SNAKE_CASE      )

         A			:  str   =		backbone_config
         A			:  Optional[int]   =		num_queries
         A			:  Dict   =		max_position_embeddings
         A			:  Optional[Any]   =		d_model
         A			:  Optional[Any]   =		encoder_ffn_dim
         A			:  List[str]   =		encoder_layers
         A			:  Tuple   =		encoder_attention_heads
         A			:  Optional[Any]   =		decoder_ffn_dim
         A			:  Optional[int]   =		decoder_layers
         A			:  List[str]   =		decoder_attention_heads
         A			:  Union[str, Any]   =		dropout
         A			:  str   =		attention_dropout
         A			:  Any   =		activation_dropout
         A			:  Optional[int]   =		activation_function
         A			:  Tuple   =		init_std
         A			:  Any   =		init_xavier_std
         A			:  Optional[Any]   =		encoder_layerdrop
         A			:  int   =		auxiliary_loss
         A			:  Dict   =		position_embedding_type
         # deformable attributes
         A			:  str   =		num_feature_levels
         A			:  Optional[int]   =		encoder_n_points
         A			:  Any   =		decoder_n_points
         A			:  Tuple   =		two_stage
         A			:  Dict   =		two_stage_num_proposals
         A			:  List[str]   =		with_box_refine
         A			:  List[str]   =		assign_first_stage
         if two_stage is True and with_box_refine is False:
             raise ValueError('''If two_stage is True, with_box_refine must be True.'''      )
         # Hungarian matcher
         A			:  Dict   =		class_cost
         A			:  Optional[int]   =		bbox_cost
         A			:  Optional[Any]   =		giou_cost
         # Loss coefficients
         A			:  int   =		mask_loss_coefficient
         A			:  int   =		dice_loss_coefficient
         A			:  Tuple   =		bbox_loss_coefficient
         A			:  int   =		giou_loss_coefficient
         A			:  Dict   =		eos_coefficient
         A			:  Optional[Any]   =		focal_alpha
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.encoder_attention_heads





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.d_model





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Any   =		copy.deepcopy(self.__dict__      )
         A			:  Dict   =		self.backbone_config.to_dict()
         A			:  List[Any]   =		self.__class__.model_type
         return output




