






"""simple docstring"""
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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase         =							logging.get_logger(__name__)



class      SCREAMING_SNAKE_CASE  (				__A						):





	"""simple docstring"""




	_A       :				Optional[Any]    =				["""pixel_values"""]





	def __init__(self     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = PILImageResampling.BILINEAR     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = 1 / 255     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		super().__init__(**UpperCamelCase__							)
		A_		:						Union[str, Any]	    =   size if size is not None else {'shortest_edge': 256}
		A_		:						Union[str, Any]	    =   get_size_dict(UpperCamelCase__     ,     default_to_square=UpperCamelCase__							)
		A_		:						str	    =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
		A_		:						List[Any]	    =   get_size_dict(UpperCamelCase__							)
		A_		:						Dict	    =   do_resize
		A_		:						Optional[int]	    =   size
		A_		:						Optional[int]	    =   resample
		A_		:						Any	    =   do_center_crop
		A_		:						List[Any]	    =   crop_size
		A_		:						Optional[Any]	    =   do_rescale
		A_		:						int	    =   rescale_factor
		A_		:						Any	    =   do_normalize
		A_		:						List[Any]	    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		A_		:						Dict	    =   image_std if image_std is not None else IMAGENET_STANDARD_STD





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = PILImageResampling.BICUBIC     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		A_		:						Tuple	    =   get_size_dict(UpperCamelCase__     ,     default_to_square=UpperCamelCase__							)
		if "shortest_edge" not in size:
			raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
		A_		:						Optional[int]	    =   get_resize_output_image_size(UpperCamelCase__     ,     size=size["""shortest_edge"""]     ,     default_to_square=UpperCamelCase__							)
		return resize(UpperCamelCase__     ,     size=UpperCamelCase__     ,     resample=UpperCamelCase__     ,     data_format=UpperCamelCase__     ,     **UpperCamelCase__							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		A_		:						Union[str, Any]	    =   get_size_dict(UpperCamelCase__							)
		return center_crop(UpperCamelCase__     ,     size=(size["""height"""], size["""width"""])     ,     data_format=UpperCamelCase__     ,     **UpperCamelCase__							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_							):
		return rescale(UpperCamelCase__     ,     scale=UpperCamelCase__     ,     data_format=UpperCamelCase__     ,     **UpperCamelCase__							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		return normalize(UpperCamelCase__     ,     mean=UpperCamelCase__     ,     std=UpperCamelCase__     ,     data_format=UpperCamelCase__     ,     **UpperCamelCase__							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = ChannelDimension.FIRST     ,     **lowerCAmelCase_     ,     ):
		A_		:						str	    =   do_resize if do_resize is not None else self.do_resize
		A_		:						List[Any]	    =   size if size is not None else self.size
		A_		:						int	    =   get_size_dict(UpperCamelCase__     ,     default_to_square=UpperCamelCase__							)
		A_		:						int	    =   resample if resample is not None else self.resample
		A_		:						Optional[Any]	    =   do_center_crop if do_center_crop is not None else self.do_center_crop
		A_		:						List[str]	    =   crop_size if crop_size is not None else self.crop_size
		A_		:						Dict	    =   get_size_dict(UpperCamelCase__							)
		A_		:						Dict	    =   do_rescale if do_rescale is not None else self.do_rescale
		A_		:						int	    =   rescale_factor if rescale_factor is not None else self.rescale_factor
		A_		:						Dict	    =   do_normalize if do_normalize is not None else self.do_normalize
		A_		:						Tuple	    =   image_mean if image_mean is not None else self.image_mean
		A_		:						Any	    =   image_std if image_std is not None else self.image_std

		A_		:						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:
			raise ValueError("""Size must be specified if do_resize is True."""							)

		if do_center_crop and crop_size is None:
			raise ValueError("""Crop size must be specified if do_center_crop 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.
		A_		:						Optional[int]	    =   [to_numpy_array(UpperCamelCase__							) for image in images]

		if do_resize:
			A_		:						Optional[int]	    =   [self.resize(image=UpperCamelCase__     ,     size=UpperCamelCase__     ,     resample=UpperCamelCase__							) for image in images]

		if do_center_crop:
			A_		:						Dict	    =   [self.center_crop(image=UpperCamelCase__     ,     size=UpperCamelCase__							) for image in images]

		if do_rescale:
			A_		:						Union[str, Any]	    =   [self.rescale(image=UpperCamelCase__     ,     scale=UpperCamelCase__							) for image in images]

		if do_normalize:
			A_		:						Optional[int]	    =   [self.normalize(image=UpperCamelCase__     ,     mean=UpperCamelCase__     ,     std=UpperCamelCase__							) for image in images]

		A_		:						Optional[Any]	    =   [to_channel_dimension_format(UpperCamelCase__     ,     UpperCamelCase__							) for image in images]

		A_		:						Dict	    =   {'pixel_values': images}
		return BatchFeature(data=UpperCamelCase__     ,     tensor_type=UpperCamelCase__							)









"""simple docstring"""
import copy

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_="relu"     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   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_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return self.encoder_attention_heads





	@property
	def    lowerCamelCase(self							):
		return self.d_model





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

