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





     def 		UpperCAmelCase(self				:   Dict       )						->						Tuple:
         torch.manual_seed(0       )
         snake_case								= UNetaDModel(
             block_out_channels=(3_2, 6_4)    ,   layers_per_block=2    ,   sample_size=3_2    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")    ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")    ,   )
         snake_case								= DDIMScheduler()
         snake_case								= {"unet": unet, "scheduler": scheduler}
         return components





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Any    ,   _A				:   List[str]=0       )						->						List[str]:
         if str(_A       ).startswith("mps"       ):
             snake_case								= torch.manual_seed(_A       )
         else:
             snake_case								= torch.Generator(device=_A       ).manual_seed(_A       )
         snake_case								= {
             "batch_size": 1,
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }
         return inputs





     def 		UpperCAmelCase(self				:   Tuple       )						->						int:
         snake_case								= "cpu"

         snake_case								= self.get_dummy_components()
         snake_case								= self.pipeline_class(**_A       )
         pipe.to(_A       )
         pipe.set_progress_bar_config(disable=_A       )

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

         self.assertEqual(image.shape    ,   (1, 3_2, 3_2, 3)       )
         snake_case								= np.array(
             [1.0_00E00, 5.7_17E-01, 4.7_17E-01, 1.0_00E00, 0.0_00E00, 1.0_00E00, 3.0_00E-04, 0.0_00E00, 9.0_00E-04]       )
         snake_case								= np.abs(image_slice.flatten() - expected_slice       ).max()
         self.assertLessEqual(_A    ,   1E-3       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )





     def 		UpperCAmelCase(self				:   str       )						->						Dict:
         super().test_save_load_local(expected_max_difference=3E-3       )





     def 		UpperCAmelCase(self				:   Tuple       )						->						str:
         super().test_save_load_optional_components(expected_max_difference=3E-3       )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						str:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )







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





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Dict:
         snake_case								= "google/ddpm-cifar10-32"

         snake_case								= UNetaDModel.from_pretrained(_A       )
         snake_case								= DDIMScheduler()

         snake_case								= DDIMPipeline(unet=_A    ,   scheduler=_A       )
         ddim.to(_A       )
         ddim.set_progress_bar_config(disable=_A       )

         snake_case								= torch.manual_seed(0       )
         snake_case								= ddim(generator=_A    ,   eta=0.0    ,   output_type="numpy"       ).images

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

         assert image.shape == (1, 3_2, 3_2, 3)
         snake_case								= 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 		UpperCAmelCase(self				:   List[Any]       )						->						List[Any]:
         snake_case								= "google/ddpm-ema-bedroom-256"

         snake_case								= UNetaDModel.from_pretrained(_A       )
         snake_case								= DDIMScheduler.from_pretrained(_A       )

         snake_case								= DDIMPipeline(unet=_A    ,   scheduler=_A       )
         ddpm.to(_A       )
         ddpm.set_progress_bar_config(disable=_A       )

         snake_case								= torch.manual_seed(0       )
         snake_case								= ddpm(generator=_A    ,   output_type="numpy"       ).images

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

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         snake_case								= 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



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

