

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class     lowercase_       (    __lowercase	):



      def __init__(     self :							Any     ,   A__ :							Any     ,   A__ :							Union[str, Any]						)       ->	List[Any]:
        super().__init__()
        self.register_modules(unet=A__     ,   scheduler=A__						)







      @torch.no_grad()
      def __call__(     self :							Union[str, Any]     ,   A__ :							int = 1     ,   A__ :							Optional[torch.Generator] = None     ,   A__ :							int = 50     ,   A__ :							Optional[str] = "pil"     ,   A__ :							bool = True     ,   **A__ :							Union[str, Any]     ,   )       ->	Union[ImagePipelineOutput, Tuple]:
        _snake_case		=				torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,   generator=A__     ,   )
        _snake_case		=				image.to(self.device						)

        # set step values
        self.scheduler.set_timesteps(A__						)

        for t in self.progress_bar(self.scheduler.timesteps						):
          # 1. predict noise model_output
          _snake_case		=				self.unet(A__     ,   A__						).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          _snake_case		=				self.scheduler.step(A__     ,   A__     ,   A__						).prev_sample

        _snake_case		=				(image / 2 + 0.5).clamp(0     ,   1						)
        _snake_case		=				image.cpu().permute(0     ,   2     ,   3     ,   1						).numpy()
        if output_type == "pil":
          _snake_case		=				self.numpy_to_pil(A__						)

        if not return_dict:
          return (image,), "This is a local test"

        return ImagePipelineOutput(images=A__						), "This is a local test"




import functools
from math import sqrt
from time import time



def  snake_case_(_UpperCamelCase					,			*_UpperCamelCase					,			**_UpperCamelCase	)				->      str:



  """simple docstring"""






  _snake_case		=				time()
  _snake_case		=				func(*_UpperCamelCase					,			**_UpperCamelCase	)
  _snake_case		=				time()
  if int(end - start	) > 0:
    print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""	)
  else:
    print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""	)
  return output



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n == 0:
    return [0]
  _snake_case		=				[0, 1]
  for _ in range(n - 1	):
    fib.append(fib[-1] + fib[-2]	)
  return fib



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  @functools.cache
  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  # Cache must be outside recursuive function
  # other it will reset every time it calls itself.
  _snake_case		=				{0: 0, 1: 1, 2: 1}  # Prefilled cache

  def rec_fn_memoized(_UpperCamelCase	) -> int:
    if num in cache:
      return cache[num]

    _snake_case		=				rec_fn_memoized(num - 1	) + rec_fn_memoized(num - 2	)
    _snake_case		=				value
    return value

  return [rec_fn_memoized(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n >= 1_475:
    raise Exception('''n is too large'''	)
  _snake_case		=				sqrt(5	)
  _snake_case		=				(1 + sqrt_a) / 2
  return [round(phi**i / sqrt_a	) for i in range(n + 1	)]


if __name__ == "__main__":
 __A     						=      30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)

