





"""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 					_SCREAMING_SNAKE_CASE(   A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  DDIMPipeline
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       SCREAMING_SNAKE_CASE_   :      Union[str, Any]							=  PipelineTesterMixin.required_optional_params - {
           '''num_images_per_prompt''',
           '''latents''',
           '''callback''',
           '''callback_steps''',
       }
       SCREAMING_SNAKE_CASE_   :      Union[str, Any]							=  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE_   :      Any							=  False




       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  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''')		,)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  DDIMScheduler()
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  {'''unet''': unet, '''scheduler''': scheduler}
             return components




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)			->      Dict:





             """simple docstring"""






             if str(SCREAMING_SNAKE_CASE__							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  {
                 '''batch_size''': 1,
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''numpy''',
             }
             return inputs




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Dict						=  '''cpu'''

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.get_dummy_components()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
             pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             __SCREAMING_SNAKE_CASE   :Dict						=  image[0, -3:, -3:, -1]

             self.assertEqual(image.shape		,(1, 32, 32, 3)							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.array(
                 [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  np.abs(image_slice.flatten() - expected_slice							).max()
             self.assertLessEqual(SCREAMING_SNAKE_CASE__		,1E-3							)




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






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




       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






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




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





             """simple docstring"""






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




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






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






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




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Dict						=  '''google/ddpm-cifar10-32'''

             __SCREAMING_SNAKE_CASE   :Any						=  UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  DDIMScheduler()

             __SCREAMING_SNAKE_CASE   :List[str]						=  DDIMPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             ddim.to(SCREAMING_SNAKE_CASE__							)
             ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :str						=  ddim(generator=SCREAMING_SNAKE_CASE__		,eta=0.0		,output_type='''numpy'''							).images

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]							)

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




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''google/ddpm-ema-bedroom-256'''

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  DDIMPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             ddpm.to(SCREAMING_SNAKE_CASE__							)
             ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  ddpm(generator=SCREAMING_SNAKE_CASE__		,output_type='''numpy'''							).images

             __SCREAMING_SNAKE_CASE   :Tuple						=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 2_56, 2_56, 3)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]							)

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






"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      Optional[torch.FloatTensor]							=  None






class 					_SCREAMING_SNAKE_CASE(   A    ,	A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  2




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = 1_00		,SCREAMING_SNAKE_CASE__ = 1.0_0_7		,SCREAMING_SNAKE_CASE__ = 80		,SCREAMING_SNAKE_CASE__ = 0.0_5		,SCREAMING_SNAKE_CASE__ = 50		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sigma_max

             # setable values
             __SCREAMING_SNAKE_CASE   :int						=  None
             __SCREAMING_SNAKE_CASE   :np.IntTensor						=  None
             __SCREAMING_SNAKE_CASE   :torch.FloatTensor						=  None  # sigma(t_i)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      torch.FloatTensor:





             """simple docstring"""






             return sample




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  num_inference_steps
             __SCREAMING_SNAKE_CASE   :int						=  np.arange(0		,self.num_inference_steps							)[::-1].copy()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  [
                 (
                     self.config.sigma_max**2
                     * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                 )
                 for i in self.timesteps
             ]
             __SCREAMING_SNAKE_CASE   :List[str]						=  torch.tensor(SCREAMING_SNAKE_CASE__		,dtype=torch.floataa		,device=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      Tuple[torch.FloatTensor, float]:





             """simple docstring"""






             if self.config.s_min <= sigma <= self.config.s_max:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  min(self.config.s_churn / self.num_inference_steps		,2**0.5 - 1							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  0

             # sample eps ~ N(0, S_noise^2 * I)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.config.s_noise * randn_tensor(sample.shape		,generator=SCREAMING_SNAKE_CASE__							).to(sample.device							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  sigma + gamma * sigma
             __SCREAMING_SNAKE_CASE   :str						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

             return sample_hat, sigma_hat




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  sample_hat + sigma_hat * model_output
             __SCREAMING_SNAKE_CASE   :Tuple						=  (sample_hat - pred_original_sample) / sigma_hat
             __SCREAMING_SNAKE_CASE   :List[Any]						=  sample_hat + (sigma_prev - sigma_hat) * derivative

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sample_prev + sigma_prev * model_output
             __SCREAMING_SNAKE_CASE   :List[Any]						=  (sample_prev - pred_original_sample) / sigma_prev
             __SCREAMING_SNAKE_CASE   :Dict						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             raise NotImplementedError()