



"""simple docstring"""







import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


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

enable_full_determinism()







class 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					UNetaDModel
      _SCREAMING_SNAKE_CASE      =					"""sample"""



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Optional[int]								=			4
        lowerCAmelCase_       :    Optional[int]								=			3
        lowerCAmelCase_       :    List[Any]								=			(3_2, 3_2)

        lowerCAmelCase_       :    str								=			floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			torch.tensor([1_0]	).to(SCREAMING_SNAKE_CASE_	)

        return {"sample": noise, "timestep": time_step}



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        return (3, 3_2, 3_2)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        return (3, 3_2, 3_2)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_       :    List[Any]								=			{
            'block_out_channels': (3_2, 6_4),
            'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
            'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
            'attention_head_dim': 3,
            'out_channels': 3,
            'in_channels': 3,
            'layers_per_block': 2,
            'sample_size': 3_2,
        }
        lowerCAmelCase_       :    Any								=			self.dummy_input
        return init_dict, inputs_dict







class 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					UNetaDModel
      _SCREAMING_SNAKE_CASE      =					"""sample"""



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        lowerCAmelCase_       :    List[str]								=			4
        lowerCAmelCase_       :    Optional[Any]								=			4
        lowerCAmelCase_       :    int								=			(3_2, 3_2)

        lowerCAmelCase_       :    Optional[Any]								=			floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([1_0]	).to(SCREAMING_SNAKE_CASE_	)

        return {"sample": noise, "timestep": time_step}



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        return (4, 3_2, 3_2)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        return (4, 3_2, 3_2)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    List[str]								=			{
            'sample_size': 3_2,
            'in_channels': 4,
            'out_channels': 4,
            'layers_per_block': 2,
            'block_out_channels': (3_2, 6_4),
            'attention_head_dim': 3_2,
            'down_block_types': ('DownBlock2D', 'DownBlock2D'),
            'up_block_types': ('UpBlock2D', 'UpBlock2D'),
        }
        lowerCAmelCase_       :    Dict								=			self.dummy_input
        return init_dict, inputs_dict



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
        self.assertEqual(len(loading_info['missing_keys']	)   ,      0	)

        model.to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[Any]								=			model(**self.dummy_input	).sample

        assert image is not None, "Make sure output is not None"



      @unittest.skipIf(torch_device != 'cuda'   ,      'This test is supposed to run on GPU'	)
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_						,lowerCAmelCase_       :    int								=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    str								=			model(**self.dummy_input	).sample

        assert image is not None, "Make sure output is not None"



      @unittest.skipIf(torch_device != 'cuda'   ,      'This test is supposed to run on GPU'	)
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
        lowerCAmelCase_						,lowerCAmelCase_       :    Dict								=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)
        model_accelerate.to(SCREAMING_SNAKE_CASE_	)
        model_accelerate.eval()

        lowerCAmelCase_       :    Any								=			torch.randn(
            1   ,      model_accelerate.config.in_channels   ,      model_accelerate.config.sample_size   ,      model_accelerate.config.sample_size   ,      generator=torch.manual_seed(0	)   ,      )
        lowerCAmelCase_       :    List[str]								=			noise.to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([1_0] * noise.shape[0]	).to(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[Any]								=			model_accelerate(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)['sample']

        # two models don't need to stay in the device at the same time
        del model_accelerate
        torch.cuda.empty_cache()
        gc.collect()

        lowerCAmelCase_						,lowerCAmelCase_       :    int								=			UNetaDModel.from_pretrained(
            'fusing/unet-ldm-dummy-update'   ,      output_loading_info=SCREAMING_SNAKE_CASE_   ,      low_cpu_mem_usage=SCREAMING_SNAKE_CASE_	)
        model_normal_load.to(SCREAMING_SNAKE_CASE_	)
        model_normal_load.eval()
        lowerCAmelCase_       :    Union[str, Any]								=			model_normal_load(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)['sample']

        assert torch_all_close(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      rtol=1E-3	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    str								=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	)
        model.eval()
        model.to(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Tuple								=			torch.randn(
            1   ,      model.config.in_channels   ,      model.config.sample_size   ,      model.config.sample_size   ,      generator=torch.manual_seed(0	)   ,      )
        lowerCAmelCase_       :    List[Any]								=			noise.to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			torch.tensor([1_0] * noise.shape[0]	).to(SCREAMING_SNAKE_CASE_	)

        with torch.no_grad():
          lowerCAmelCase_       :    Optional[int]								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	).sample

        lowerCAmelCase_       :    Tuple								=			output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        lowerCAmelCase_       :    Any								=			torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]	)
        # fmt: on

        self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      rtol=1E-3	)	)







class 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					UNetaDModel
      _SCREAMING_SNAKE_CASE      =					"""sample"""



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=(3_2, 3_2)	):
        lowerCAmelCase_       :    Optional[Any]								=			4
        lowerCAmelCase_       :    Union[str, Any]								=			3

        lowerCAmelCase_       :    Optional[int]								=			floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor(batch_size * [1_0]	).to(dtype=torch.intaa   ,      device=SCREAMING_SNAKE_CASE_	)

        return {"sample": noise, "timestep": time_step}



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        return (3, 3_2, 3_2)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        return (3, 3_2, 3_2)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        lowerCAmelCase_       :    List[Any]								=			{
            'block_out_channels': [3_2, 6_4, 6_4, 6_4],
            'in_channels': 3,
            'layers_per_block': 1,
            'out_channels': 3,
            'time_embedding_type': 'fourier',
            'norm_eps': 1E-6,
            'mid_block_scale_factor': math.sqrt(2.0	),
            'norm_num_groups': None,
            'down_block_types': [
                'SkipDownBlock2D',
                'AttnSkipDownBlock2D',
                'SkipDownBlock2D',
                'SkipDownBlock2D',
            ],
            'up_block_types': [
                'SkipUpBlock2D',
                'SkipUpBlock2D',
                'AttnSkipUpBlock2D',
                'SkipUpBlock2D',
            ],
        }
        lowerCAmelCase_       :    Optional[int]								=			self.dummy_input
        return init_dict, inputs_dict



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_						,lowerCAmelCase_       :    List[str]								=			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'   ,      output_loading_info=SCREAMING_SNAKE_CASE_	)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
        self.assertEqual(len(loading_info['missing_keys']	)   ,      0	)

        model.to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			self.dummy_input
        lowerCAmelCase_       :    Dict								=			floats_tensor((4, 3) + (2_5_6, 2_5_6)	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    str								=			noise
        lowerCAmelCase_       :    Dict								=			model(**SCREAMING_SNAKE_CASE_	)

        assert image is not None, "Make sure output is not None"



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Optional[Any]								=			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'	)
        model.to(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[str]								=			4
        lowerCAmelCase_       :    Optional[int]								=			3
        lowerCAmelCase_       :    Tuple								=			(2_5_6, 2_5_6)

        lowerCAmelCase_       :    int								=			torch.ones((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			torch.tensor(batch_size * [1E-4]	).to(SCREAMING_SNAKE_CASE_	)

        with torch.no_grad():
          lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	).sample

        lowerCAmelCase_       :    Tuple								=			output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        lowerCAmelCase_       :    int								=			torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]	)
        # fmt: on

        self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      rtol=1E-2	)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    int								=			UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'	)
        model.to(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[int]								=			4
        lowerCAmelCase_       :    List[Any]								=			3
        lowerCAmelCase_       :    Any								=			(3_2, 3_2)

        lowerCAmelCase_       :    Tuple								=			torch.ones((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[str]								=			torch.tensor(batch_size * [1E-4]	).to(SCREAMING_SNAKE_CASE_	)

        with torch.no_grad():
          lowerCAmelCase_       :    Tuple								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	).sample

        lowerCAmelCase_       :    Any								=			output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        lowerCAmelCase_       :    Dict								=			torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]	)
        # fmt: on

        self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      rtol=1E-2	)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        # not required for this model
        pass










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    str						)						->       List[Any]:

  """simple docstring"""




  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    str=0						)						->       Union[str, Any]:

  """simple docstring"""




  return sorted(lowerCAmelCase__   ,       key=lambda lowerCAmelCase__						: x[column]						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]=float('inf'						)						)						->       Optional[int]:

  """simple docstring"""




  for i in range(points_counts - 1						):
    for j in range(i + 1   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Union[str, Any]								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Optional[int]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]=float('inf'						)						)						->       Dict:

  """simple docstring"""




  for i in range(min(6   ,       points_counts - 1						)   ,       lowerCAmelCase__						):
    for j in range(max(0   ,       i - 6						)   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Any								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Union[str, Any]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Optional[int]						)						->       Dict:

  """simple docstring"""




  if points_counts <= 3:
    return dis_between_closest_pair(lowerCAmelCase__   ,       lowerCAmelCase__						)

  # recursion
  lowerCAmelCase_       :    int								=			points_counts // 2
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[:mid]   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[mid:]   ,       points_counts - mid						)
  lowerCAmelCase_       :    Any								=			min(lowerCAmelCase__   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    str								=			[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
      cross_strip.append(lowerCAmelCase__						)

  lowerCAmelCase_       :    List[Any]								=			dis_between_closest_in_strip(
      lowerCAmelCase__   ,       len(lowerCAmelCase__						)   ,       lowerCAmelCase__						)
  return min(lowerCAmelCase__   ,       lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Tuple						)						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			column_based_sort(lowerCAmelCase__   ,       column=0						)
  lowerCAmelCase_       :    Dict								=			column_based_sort(lowerCAmelCase__   ,       column=1						)
  return (
      closest_pair_of_points_sqr(
          lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
  ) ** 0.5


if __name__ == "__main__":
     lowercase__		:    List[str]				   =     [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
     print("""Distance:""", closest_pair_of_points(points, len(points)))





