


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    UpperCAmelCase__	( A_       ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple              =  DDIMPipeline
    UpperCAmelCase__						:		Optional[Any]              =  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
    UpperCAmelCase__						:		List[Any]              =  PipelineTesterMixin.required_optional_params - {
        "num_images_per_prompt",
        "latents",
        "callback",
        "callback_steps",
    }
    UpperCAmelCase__						:		Union[str, Any]              =  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
    UpperCAmelCase__						:		Union[str, Any]              =  False






    def _a (      self   )  ->	Dict:
         torch.manual_seed(0   )
         __UpperCamelCase   							=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') ,       )
         __UpperCamelCase   							=DDIMScheduler()
         __UpperCamelCase   							={'unet': unet, 'scheduler': scheduler}
         return components






    def _a (      self ,       A_ ,       A_=0   )  ->	int:
         if str(A_   ).startswith('mps'   ):
              __UpperCamelCase   							=torch.manual_seed(A_   )
         else:
              __UpperCamelCase   							=torch.Generator(device=A_   ).manual_seed(A_   )
         __UpperCamelCase   							={
             'batch_size': 1,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






    def _a (      self   )  ->	str:
         __UpperCamelCase   							='cpu'

         __UpperCamelCase   							=self.get_dummy_components()
         __UpperCamelCase   							=self.pipeline_class(**A_   )
         pipe.to(A_   )
         pipe.set_progress_bar_config(disable=A_   )

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

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






    def _a (      self   )  ->	Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )






    def _a (      self   )  ->	Union[str, Any]:
         super().test_save_load_local(expected_max_difference=3E-3   )






    def _a (      self   )  ->	List[Any]:
         super().test_save_load_optional_components(expected_max_difference=3E-3   )






    def _a (      self   )  ->	str:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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



    """simple docstring"""






    def _a (      self   )  ->	Any:
         __UpperCamelCase   							='google/ddpm-cifar10-32'

         __UpperCamelCase   							=UNetaDModel.from_pretrained(A_   )
         __UpperCamelCase   							=DDIMScheduler()

         __UpperCamelCase   							=DDIMPipeline(unet=A_ ,       scheduler=A_   )
         ddim.to(A_   )
         ddim.set_progress_bar_config(disable=A_   )

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

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

         assert image.shape == (1, 32, 32, 3)
         __UpperCamelCase   							=np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]   )

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






    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							='google/ddpm-ema-bedroom-256'

         __UpperCamelCase   							=UNetaDModel.from_pretrained(A_   )
         __UpperCamelCase   							=DDIMScheduler.from_pretrained(A_   )

         __UpperCamelCase   							=DDIMPipeline(unet=A_ ,       scheduler=A_   )
         ddpm.to(A_   )
         ddpm.set_progress_bar_config(disable=A_   )

         __UpperCamelCase   							=torch.manual_seed(0   )
         __UpperCamelCase   							=ddpm(generator=A_ ,       output_type='numpy'   ).images

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

         assert image.shape == (1, 256, 256, 3)
         __UpperCamelCase   							=np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]   )

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









from __future__ import annotations

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     if b == 0:
          return (1, 0)
     ((__UpperCamelCase)							,				(__UpperCamelCase))   							=extended_euclid(SCREAMING_SNAKE_CASE__      ,					a % b    )
     __UpperCamelCase   							=a // b
     return (y, x - k * y)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     ((__UpperCamelCase)							,				(__UpperCamelCase))   							=extended_euclid(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=na * na
     __UpperCamelCase   							=ra * x * na + ra * y * na
     return (n % m + m) % m

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     ((__UpperCamelCase)							,				(__UpperCamelCase))   							=extended_euclid(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     if b < 0:
          __UpperCamelCase   							=(b % n + n) % n
     return b

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase							,				__UpperCamelCase   							=invert_modulo(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ), invert_modulo(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=na * na
     __UpperCamelCase   							=ra * x * na + ra * y * na
     return (n % m + m) % m


if __name__ == "__main__":
   from doctest import testmod

   testmod(name='chinese_remainder_theorem', verbose=True)
   testmod(name='chinese_remainder_theorem2', verbose=True)
   testmod(name='invert_modulo', verbose=True)
   testmod(name='extended_euclid', verbose=True)





