




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class  __lowerCAmelCase							(				lowercase_   ,			unittest.TestCase):
       _a			       =    RePaintPipeline
       _a			       =    IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
       _a			       =    PipelineTesterMixin.required_optional_params - {
           '''latents''',
           '''num_images_per_prompt''',
           '''callback''',
           '''callback_steps''',
       }
       _a			       =    IMAGE_INPAINTING_BATCH_PARAMS
       _a			       =    False






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         torch.manual_seed(0					)
         torch.manual_seed(0					)
         lowercase     :Dict   						=    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")							,       )
         lowercase     :Dict   						=    RePaintScheduler()
         lowercase     :str   						=    {"unet": unet, "scheduler": scheduler}
         return components






       def        SCREAMING_SNAKE_CASE     ( self:       Dict							,       _lowerCAmelCase:       Optional[Any]							,       _lowerCAmelCase:       Dict=0					):
         if str(_lowerCAmelCase					).startswith("mps"					):
           lowercase     :str   						=    torch.manual_seed(_lowerCAmelCase					)
         else:
           lowercase     :Optional[Any]   						=    torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
         lowercase     :Tuple   						=    np.random.RandomState(_lowerCAmelCase					).standard_normal((1, 3, 32, 32)					)
         lowercase     :Optional[int]   						=    torch.from_numpy(_lowerCAmelCase					).to(device=_lowerCAmelCase							,       dtype=torch.floataa					)
         lowercase     :Optional[int]   						=    (image > 0).to(device=_lowerCAmelCase							,       dtype=torch.floataa					)
         lowercase     :Optional[int]   						=    {
             "image": image,
             "mask_image": mask,
             "generator": generator,
             "num_inference_steps": 5,
             "eta": 0.0,
             "jump_length": 2,
             "jump_n_sample": 2,
             "output_type": "numpy",
         }
         return inputs






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :Any   						=    "cpu"  # ensure determinism for the device-dependent torch.Generator
         lowercase     :int   						=    self.get_dummy_components()
         lowercase     :Dict   						=    RePaintPipeline(**_lowerCAmelCase					)
         lowercase     :Optional[int]   						=    sd_pipe.to(_lowerCAmelCase					)
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

         lowercase     :Dict   						=    self.get_dummy_inputs(_lowerCAmelCase					)
         lowercase     :Any   						=    sd_pipe(**_lowerCAmelCase					).images
         lowercase     :List[str]   						=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         lowercase     :Union[str, Any]   						=    np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]					)

         assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         return super().test_save_load_local()






       @unittest.skip("non-deterministic pipeline"					)
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]					):
         return super().test_inference_batch_single_identical()






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       Any					):
         return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]					):
         return super().test_save_load_optional_components()






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class  __lowerCAmelCase							(				unittest.TestCase):






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         lowercase     :Tuple   						=    load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
             "repaint/celeba_hq_256.png"					)
         lowercase     :Dict   						=    load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"					)
         lowercase     :int   						=    load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
             "repaint/celeba_hq_256_result.npy"					)

         lowercase     :Union[str, Any]   						=    "google/ddpm-ema-celebahq-256"
         lowercase     :Tuple   						=    UNetaDModel.from_pretrained(_lowerCAmelCase					)
         lowercase     :Optional[int]   						=    RePaintScheduler.from_pretrained(_lowerCAmelCase					)

         lowercase     :Any   						=    RePaintPipeline(unet=_lowerCAmelCase							,       scheduler=_lowerCAmelCase					).to(_lowerCAmelCase					)
         repaint.set_progress_bar_config(disable=_lowerCAmelCase					)
         repaint.enable_attention_slicing()

         lowercase     :str   						=    torch.manual_seed(0					)
         lowercase     :List[Any]   						=    repaint(
             _lowerCAmelCase							,       _lowerCAmelCase							,       num_inference_steps=2_50							,       eta=0.0							,       jump_length=10							,       jump_n_sample=10							,       generator=_lowerCAmelCase							,       output_type="np"							,       )
         lowercase     :Tuple   						=    output.images[0]

         assert image.shape == (2_56, 2_56, 3)
         assert np.abs(expected_image - image					).mean() < 1e-2










def  __UpperCamelCase   (     a)			->str:
					if len(a) == 0 or not all(len(a) == 2 for pair in coordinates):
										raise ValueError("The program cannot work out a fitting polynomial.")

					if len({tuple(a) for pair in coordinates}) != len(a):
										raise ValueError("The program cannot work out a fitting polynomial.")

					lowerCamelCase__      =							{x for x, _ in coordinates}
					if len(a) == 1:
										return f"x={coordinates[0][0]}"

					if len(a) != len(a):
										raise ValueError("The program cannot work out a fitting polynomial.")

					lowerCamelCase__      =							len(a)

					lowerCamelCase__      =							0
					lowerCamelCase__      =							[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										lowerCamelCase__      =							0
										lowerCamelCase__      =							coordinates[count_of_line][0]
										lowerCamelCase__      =							[]
										while count_in_line < x:
															count_line.append(a ** (x - (count_in_line + 1)))
															count_in_line += 1
										matrix.append(a)
										count_of_line += 1

					lowerCamelCase__      =							0
					# put the y values into a vector
					lowerCamelCase__      =							[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1])
										count_of_line += 1

					lowerCamelCase__      =							0

					while count < x:
										lowerCamelCase__      =							0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															lowerCamelCase__      =							matrix[zahlen][count] / matrix[count][count]
															for counting_columns, item in enumerate(matrix[count]):
																				# manipulating all the values in the matrix
																				matrix[zahlen][counting_columns] -= item * bruch
															# manipulating the values in the vector
															vector[zahlen] -= vector[count] * bruch
															zahlen += 1
										count += 1

					lowerCamelCase__      =							0
					# make solutions
					lowerCamelCase__      =							[]
					while count < x:
										solution.append(str(vector[count] / matrix[count][count]))
										count += 1

					lowerCamelCase__      =							0
					lowerCamelCase__      =							"f(x)="

					while count < x:
										lowerCamelCase__      =							solution[count].split("E")
										if len(a) > 1:
															lowerCamelCase__      =							f"{remove_e[0]}*10^{remove_e[1]}"
										solved += f"x^{x - (count + 1)}*{solution[count]}"
										if count + 1 != x:
															solved += "+"
										count += 1

					return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 1_1]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))

