'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class        a__ (  A_				,  unittest.TestCase					):
     lowerCamelCase    :	List[str]				=DDIMPipeline
     lowerCamelCase    :	List[str]				=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
     lowerCamelCase    :	Optional[Any]				=PipelineTesterMixin.required_optional_params - {
         "num_images_per_prompt",
         "latents",
         "callback",
         "callback_steps",
     }
     lowerCamelCase    :	List[Any]				=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
     lowerCamelCase    :	Dict				=False






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         torch.manual_seed(0       )
         __lowerCamelCase            =    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''')    ,							)
         __lowerCamelCase            =    DDIMScheduler()
         __lowerCamelCase            =    {"unet": unet, "scheduler": scheduler}
         return components






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						List[Any]    ,							a						:						Optional[int]=0       ):





         """simple docstring"""



         if str(snake_case__       ).startswith('''mps'''       ):
             __lowerCamelCase            =    torch.manual_seed(snake_case__       )
         else:
             __lowerCamelCase            =    torch.Generator(device=snake_case__       ).manual_seed(snake_case__       )
         __lowerCamelCase            =    {
             "batch_size": 1,
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }
         return inputs






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    "cpu"

         __lowerCamelCase            =    self.get_dummy_components()
         __lowerCamelCase            =    self.pipeline_class(**snake_case__       )
         pipe.to(snake_case__       )
         pipe.set_progress_bar_config(disable=snake_case__       )

         __lowerCamelCase            =    self.get_dummy_inputs(snake_case__       )
         __lowerCamelCase            =    pipe(**snake_case__       ).images
         __lowerCamelCase            =    image[0, -3:, -3:, -1]

         self.assertEqual(image.shape    ,							(1, 32, 32, 3)       )
         __lowerCamelCase            =    np.array(
             [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]       )
         __lowerCamelCase            =    np.abs(image_slice.flatten() - expected_slice       ).max()
         self.assertLessEqual(snake_case__    ,							1e-3       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         super().test_save_load_local(expected_max_difference=3e-3       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         super().test_save_load_optional_components(expected_max_difference=3e-3       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         super().test_inference_batch_single_identical(expected_max_diff=3e-3       )




@slow
@require_torch_gpu
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    "google/ddpm-cifar10-32"

         __lowerCamelCase            =    UNetaDModel.from_pretrained(snake_case__       )
         __lowerCamelCase            =    DDIMScheduler()

         __lowerCamelCase            =    DDIMPipeline(unet=snake_case__    ,							scheduler=snake_case__       )
         ddim.to(snake_case__       )
         ddim.set_progress_bar_config(disable=snake_case__       )

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    ddim(generator=snake_case__    ,							eta=0.0    ,							output_type='''numpy'''       ).images

         __lowerCamelCase            =    image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase            =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]       )

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






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    "google/ddpm-ema-bedroom-256"

         __lowerCamelCase            =    UNetaDModel.from_pretrained(snake_case__       )
         __lowerCamelCase            =    DDIMScheduler.from_pretrained(snake_case__       )

         __lowerCamelCase            =    DDIMPipeline(unet=snake_case__    ,							scheduler=snake_case__       )
         ddpm.to(snake_case__       )
         ddpm.set_progress_bar_config(disable=snake_case__       )

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    ddpm(generator=snake_case__    ,							output_type='''numpy'''       ).images

         __lowerCamelCase            =    image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         __lowerCamelCase            =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]       )

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


__lowerCamelCase				=			"""Tobias Carryer"""

from time import time






class     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]=int(time()					)					)						->      List[Any]:  # noqa: B008


          '''simple docstring'''


          snake_case			:  Dict     							= multiplier
          snake_case			:  Dict     							= increment
          snake_case			:  Union[str, Any]     							= modulo
          snake_case			:  Tuple     							= seed



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= (self.multiplier * self.seed + self.increment) % self.modulo
          return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   __lowerCamelCase				=			LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
   while True:
      print(lcg.next_number())





