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 		a__						( lowerCAmelCase__   ,		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     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							torch.manual_seed(0   )
							torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		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"),)
							SCREAMING_SNAKE_CASE_  :		int    				=   RePaintScheduler()
							SCREAMING_SNAKE_CASE_  :		Dict    				=   {'''unet''': unet, '''scheduler''': scheduler}
							return components

					def     __UpperCamelCase       (   self				:	int,_A				:	str,_A				:	str=0   ):







							"""simple docstring"""






							if str(_A   ).startswith("mps"   ):
									SCREAMING_SNAKE_CASE_  :		Dict    				=   torch.manual_seed(_A   )
							else:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.Generator(device=_A   ).manual_seed(_A   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   np.random.RandomState(_A   ).standard_normal((1, 3, 32, 32)   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.from_numpy(_A   ).to(device=_A,dtype=torch.floataa   )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   (image > 0).to(device=_A,dtype=torch.floataa   )
							SCREAMING_SNAKE_CASE_  :		str    				=   {
							    '''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     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.get_dummy_components()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   RePaintPipeline(**_A   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   sd_pipe.to(_A   )
							sd_pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.get_dummy_inputs(_A   )
							SCREAMING_SNAKE_CASE_  :		int    				=   sd_pipe(**_A   ).images
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							SCREAMING_SNAKE_CASE_  :		int    				=   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]   )

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

					@skip_mps
					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							return super().test_save_load_local()

					@unittest.skip("non-deterministic pipeline"   )
					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							return super().test_inference_batch_single_identical()

					@skip_mps
					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							return super().test_dict_tuple_outputs_equivalent()

					@skip_mps
					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							return super().test_save_load_optional_components()






					@skip_mps
					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							return super().test_attention_slicing_forward_pass()







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

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
							    "repaint/celeba_hq_256.png"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"   )
							SCREAMING_SNAKE_CASE_  :		int    				=   load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
							    "repaint/celeba_hq_256_result.npy"   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   '''google/ddpm-ema-celebahq-256'''
							SCREAMING_SNAKE_CASE_  :		int    				=   UNetaDModel.from_pretrained(_A   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   RePaintScheduler.from_pretrained(_A   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   RePaintPipeline(unet=_A,scheduler=_A   ).to(_A   )
							repaint.set_progress_bar_config(disable=_A   )
							repaint.enable_attention_slicing()

							SCREAMING_SNAKE_CASE_  :		Any    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		str    				=   repaint(
							    _A,_A,num_inference_steps=250,eta=0.0,jump_length=10,jump_n_sample=10,generator=_A,output_type="np",)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   output.images[0]

							assert image.shape == (256, 256, 3)
							assert np.abs(expected_image - image   ).mean() < 1E-2

from __future__ import annotations

import sys




def 				_snake_case     (       lowerCAmelCase							:       int     ):
		"""simple docstring"""







		if n < 0:
				raise ValueError("Negative arguments are not supported"     )
		return _fib(lowerCAmelCase     )[0]




def 				_snake_case     (       lowerCAmelCase							:       int     ):
		"""simple docstring"""







		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Dict    				=   _fib(n // 2     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		__lowerCamelCase :     str          =  int(sys.argv[1])
		print(f'''fibonacci({n}) is {fibonacci(n)}''')
