






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  UpperCamelCase    (			lowerCamelCase_	,  unittest.TestCase   ):
 UpperCamelCase      : Optional[Any]    =      RePaintPipeline
 UpperCamelCase      : List[Any]    =      IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
 UpperCamelCase      : List[Any]    =      PipelineTesterMixin.required_optional_params - {
     """latents""",
     """num_images_per_prompt""",
     """callback""",
     """callback_steps""",
 }
 UpperCamelCase      : List[Any]    =      IMAGE_INPAINTING_BATCH_PARAMS
 UpperCamelCase      : List[str]    =      False


 def _lowercase			(  self	:		Tuple							)      -> Optional[Any]:
   torch.manual_seed(0							)
   torch.manual_seed(0							)
   _a						: Union[str, 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""")							,		)
   _a						: Union[str, Any]    =			RePaintScheduler()
   _a						: Optional[Any]    =			{"unet": unet, "scheduler": scheduler}
   return components


 def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		int=0							)      -> Optional[Any]:







   if str(UpperCAmelCase__							).startswith("""mps"""							):
     _a						: str    =			torch.manual_seed(UpperCAmelCase__							)
   else:
     _a						: List[Any]    =			torch.Generator(device=UpperCAmelCase__							).manual_seed(UpperCAmelCase__							)
   _a						: Any    =			np.random.RandomState(UpperCAmelCase__							).standard_normal((1, 3, 32, 32)							)
   _a						: Any    =			torch.from_numpy(UpperCAmelCase__							).to(device=UpperCAmelCase__							,		dtype=torch.floataa							)
   _a						: List[str]    =			(image > 0).to(device=UpperCAmelCase__							,		dtype=torch.floataa							)
   _a						: 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 _lowercase			(  self	:		Tuple							)      -> Union[str, Any]:
   _a						: List[Any]    =			"cpu"  # ensure determinism for the device-dependent torch.Generator
   _a						: Any    =			self.get_dummy_components()
   _a						: Optional[int]    =			RePaintPipeline(**UpperCAmelCase__							)
   _a						: Optional[Any]    =			sd_pipe.to(UpperCAmelCase__							)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a						: Any    =			self.get_dummy_inputs(UpperCAmelCase__							)
   _a						: Dict    =			sd_pipe(**UpperCAmelCase__							).images
   _a						: Optional[int]    =			image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _a						: List[str]    =			np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]							)

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


 @skip_mps
 def _lowercase			(  self	:		Union[str, Any]							)      -> Optional[int]:
   return super().test_save_load_local()


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


 @skip_mps
 def _lowercase			(  self	:		str							)      -> Any:
   return super().test_dict_tuple_outputs_equivalent()


 @skip_mps
 def _lowercase			(  self	:		Dict							)      -> Dict:
   return super().test_save_load_optional_components()







 @skip_mps
 def _lowercase			(  self	:		str							)      -> Optional[int]:
   return super().test_attention_slicing_forward_pass()







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


 def _lowercase			(  self	:		Any							)      -> List[Any]:
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







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

   _a						: str    =			"google/ddpm-ema-celebahq-256"
   _a						: Optional[Any]    =			UNetaDModel.from_pretrained(UpperCAmelCase__							)
   _a						: Optional[int]    =			RePaintScheduler.from_pretrained(UpperCAmelCase__							)

   _a						: str    =			RePaintPipeline(unet=UpperCAmelCase__							,		scheduler=UpperCAmelCase__							).to(UpperCAmelCase__							)
   repaint.set_progress_bar_config(disable=UpperCAmelCase__							)
   repaint.enable_attention_slicing()

   _a						: Union[str, Any]    =			torch.manual_seed(0							)
   _a						: int    =			repaint(
       UpperCAmelCase__							,		UpperCAmelCase__							,		num_inference_steps=250							,		eta=0.0							,		jump_length=10							,		jump_n_sample=10							,		generator=UpperCAmelCase__							,		output_type="""np"""							,		)
   _a						: List[Any]    =			output.images[0]

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









"""simple docstring"""




from __future__ import annotations

import sys

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n < 0:
    raise ValueError("""Negative arguments are not supported"""       )
  return _fib(UpperCamelCase__       )[0]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''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
  _a   ,						_a						: Dict    =			_fib(n // 2       )
  _a						: Dict    =			a * (b * 2 - a)
  _a						: Optional[Any]    =			a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


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

