






"""simple docstring"""




class 		a__						:
   def __init__(      self    ):
          # numbermap keys are the number and its values are lists of indexes sorted
          # in ascending order
          lowercase   :    dict[int, list[int]]			=							{}
          # indexmap keys are an index and it's values are the number at that index
          lowercase   :    dict[int, int]			=							{}
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          if isinstance(_a     ,   (range, str)    ):
                 lowercase   :    int			=							list(_a    )
          elif not isinstance(_a     ,   _a    ):
                 raise TypeError(
                     "binary_search_delete() only accepts either a list, range or str"    )

          lowercase   :    Any			=							0
          lowercase   :    Union[str, Any]			=							len(_a    ) - 1

          while low <= high:
                 lowercase   :    str			=							(low + high) // 2
                 if array[mid] == item:
                        array.pop(_a    )
                        return array
                 elif array[mid] < item:
                        lowercase   :    Optional[Any]			=							mid + 1
                 else:
                        lowercase   :    int			=							mid - 1
          raise ValueError(
              "Either the item is not in the array or the array was unsorted"    )
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          if isinstance(_a     ,   (range, str)    ):
                 lowercase   :    Optional[Any]			=							list(_a    )
          elif not isinstance(_a     ,   _a    ):
                 raise TypeError(
                     "binary_search_insert() only accepts either a list, range or str"    )

          lowercase   :    Dict			=							0
          lowercase   :    List[Any]			=							len(_a    ) - 1

          while low <= high:
                 lowercase   :    List[Any]			=							(low + high) // 2
                 if array[mid] == index:
                        # If the item already exists in the array,
                        # insert it after the existing item
                        array.insert(mid + 1     ,   _a    )
                        return array
                 elif array[mid] < index:
                        lowercase   :    int			=							mid + 1
                 else:
                        lowercase   :    int			=							mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
          array.insert(_a     ,   _a    )
          return array
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          # Remove previous index
          if index in self.indexmap:
                 lowercase   :    Union[str, Any]			=							self.indexmap[index]
                 if len(self.numbermap[n]    ) == 1:
                        del self.numbermap[n]
                 else:
                        lowercase   :    Tuple			=							self.binary_search_delete(self.numbermap[n]     ,   _a    )

        # Set new index
          lowercase   :    Optional[Any]			=							number

          # Number not seen before or empty so insert number value
          if number not in self.numbermap:
                 lowercase   :    Dict			=							[index]

          # Here we need to perform a binary search insertion in order to insert
          # The item in the correct place
          else:
                 lowercase   :    Union[str, Any]			=							self.binary_search_insert(
                     self.numbermap[number]     ,   _a    )

   def       __magic_name__      (      self     ,   _a    ):
          return self.numbermap.get(_a     ,   [-1]    )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 		a__						(				unittest.TestCase						):
   @property
   def       __magic_name__      (      self    ):
          torch.manual_seed(0    )
          lowercase   :    int			=							UNetaDModel(
              block_out_channels=(32, 64)     ,   layers_per_block=2     ,   sample_size=32     ,   in_channels=3     ,   out_channels=3     ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,   )
          return model
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							"cpu"
          lowercase   :    Dict			=							self.dummy_uncond_unet
          lowercase   :    List[str]			=							DDPMScheduler()

          lowercase   :    Tuple			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Optional[int]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    Optional[int]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"     ,   return_dict=_a    )[0]

          lowercase   :    List[str]			=							image[0, -3:, -3:, -1]
          lowercase   :    Any			=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    List[str]			=							np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04]    )

          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2

   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							self.dummy_uncond_unet
          lowercase   :    Dict			=							DDPMScheduler(prediction_type="sample"    )

          lowercase   :    List[Any]			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Any			=							torch.manual_seed(0    )
          lowercase   :    int			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.manual_seed(0    )
          lowercase   :    Optional[Any]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    )[0]

          lowercase   :    Any			=							image[0, -3:, -3:, -1]
          lowercase   :    Dict			=							image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Tuple			=							1E-2 if torch_device != "mps" else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance


@slow
@require_torch_gpu
class 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							"google/ddpm-cifar10-32"

          lowercase   :    Dict			=							UNetaDModel.from_pretrained(_a    )
          lowercase   :    str			=							DDPMScheduler.from_pretrained(_a    )

          lowercase   :    int			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Dict			=							torch.manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   output_type="numpy"    ).images

          lowercase   :    int			=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Optional[Any]			=							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]    )
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


