"""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 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


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Any						)					->       int:
      '''simple docstring'''


      lowercase								=      b.T
      lowercase								=      np.sum(np.square(lowerCAmelCase__						)   ,						axis=1						)
      lowercase								=      np.sum(np.square(lowerCAmelCase__						)   ,						axis=0						)
      lowercase								=      np.matmul(lowerCAmelCase__   ,						lowerCAmelCase__						)
      lowercase								=      aa[:, None] - 2 * ab + ba[None, :]
      return d


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:str						)					->       Any:
      '''simple docstring'''


      lowercase								=      x.reshape(-1   ,						3						)
      lowercase								=      squared_euclidean_distance(lowerCAmelCase__   ,						lowerCAmelCase__						)
      return np.argmin(lowerCAmelCase__   ,						axis=1						)






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      ['pixel_values']

      def __init__(		self						,			__lowerCAmelCase = None						,			__lowerCAmelCase = True						,			__lowerCAmelCase = None						,			__lowerCAmelCase = PILImageResampling.BILINEAR						,			__lowerCAmelCase = True						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)
            lowercase								=      size if size is not None else {"""height""": 256, """width""": 256}
            lowercase								=      get_size_dict(__lowerCAmelCase		)
            lowercase								=      np.array(__lowerCAmelCase		) if clusters is not None else None
            lowercase								=      do_resize
            lowercase								=      size
            lowercase								=      resample
            lowercase								=      do_normalize
            lowercase								=      do_color_quantize

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = PILImageResampling.BILINEAR						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      get_size_dict(__lowerCAmelCase		)
            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(
                __lowerCAmelCase						,			size=(size["""height"""], size["""width"""])						,			resample=__lowerCAmelCase						,			data_format=__lowerCAmelCase						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			):







            """simple docstring"""



            lowercase								=      rescale(image=__lowerCAmelCase						,			scale=1 / 1_2_7.5						,			data_format=__lowerCAmelCase		)
            lowercase								=      image - 1
            return image







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = ChannelDimension.FIRST						,			**__lowerCAmelCase						,			):







            """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(__lowerCAmelCase		)
            lowercase								=      resample if resample is not None else self.resample
            lowercase								=      do_normalize if do_normalize is not None else self.do_normalize
            lowercase								=      do_color_quantize if do_color_quantize is not None else self.do_color_quantize
            lowercase								=      clusters if clusters is not None else self.clusters
            lowercase								=      np.array(__lowerCAmelCase		)

            lowercase								=      make_list_of_images(__lowerCAmelCase		)

            if not valid_images(__lowerCAmelCase		):
                  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.
            lowercase								=      [to_numpy_array(__lowerCAmelCase		) for image in images]

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

            if do_normalize:
                  lowercase								=      [self.normalize(image=__lowerCAmelCase		) for image in images]

            if do_color_quantize:
                  lowercase								=      [to_channel_dimension_format(__lowerCAmelCase						,			ChannelDimension.LAST		) for image in images]
                  # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
                  lowercase								=      np.array(__lowerCAmelCase		)
                  lowercase								=      color_quantize(__lowerCAmelCase						,			__lowerCAmelCase		).reshape(images.shape[:-1]		)

                  # flatten to (batch_size, height*width)
                  lowercase								=      images.shape[0]
                  lowercase								=      images.reshape(__lowerCAmelCase						,			-1		)

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

            lowercase								=      {"""input_ids""": images}
            return BatchFeature(data=__lowerCAmelCase						,			tensor_type=__lowerCAmelCase		)




"""simple docstring"""






import copy

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


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)

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





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'deta'
      snake_case__     :  Dict				    =      {
          '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=0.0_2						,			__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=0.2_5						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if backbone_config is None:
                  logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                  lowercase								=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
            else:
                  if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                        lowercase								=      backbone_config.pop("""model_type"""		)
                        lowercase								=      CONFIG_MAPPING[backbone_model_type]
                        lowercase								=      config_class.from_dict(__lowerCAmelCase		)

            lowercase								=      backbone_config
            lowercase								=      num_queries
            lowercase								=      max_position_embeddings
            lowercase								=      d_model
            lowercase								=      encoder_ffn_dim
            lowercase								=      encoder_layers
            lowercase								=      encoder_attention_heads
            lowercase								=      decoder_ffn_dim
            lowercase								=      decoder_layers
            lowercase								=      decoder_attention_heads
            lowercase								=      dropout
            lowercase								=      attention_dropout
            lowercase								=      activation_dropout
            lowercase								=      activation_function
            lowercase								=      init_std
            lowercase								=      init_xavier_std
            lowercase								=      encoder_layerdrop
            lowercase								=      auxiliary_loss
            lowercase								=      position_embedding_type
            # deformable attributes
            lowercase								=      num_feature_levels
            lowercase								=      encoder_n_points
            lowercase								=      decoder_n_points
            lowercase								=      two_stage
            lowercase								=      two_stage_num_proposals
            lowercase								=      with_box_refine
            lowercase								=      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
            lowercase								=      class_cost
            lowercase								=      bbox_cost
            lowercase								=      giou_cost
            # Loss coefficients
            lowercase								=      mask_loss_coefficient
            lowercase								=      dice_loss_coefficient
            lowercase								=      bbox_loss_coefficient
            lowercase								=      giou_loss_coefficient
            lowercase								=      eos_coefficient
            lowercase								=      focal_alpha
            super().__init__(is_encoder_decoder=__lowerCAmelCase						,			**__lowerCAmelCase		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.encoder_attention_heads

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.d_model







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      copy.deepcopy(self.__dict__		)
            lowercase								=      self.backbone_config.to_dict()
            lowercase								=      self.__class__.model_type
            return output



