



"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True	)			->      int:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), "Invalid type of value(s) specified to function!"

		if min_val > max_val:
				raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"	)
		return min_val if option else max_val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		return int((number_a + number_a) / 2	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      None:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), 'argument values must be type of "int"'

		if lower > higher:
				raise ValueError("argument value for lower and higher must be(lower > higher)"	)

		if not lower < to_guess < higher:
				raise ValueError(
				    "guess value must be within the range of lower and higher value"	)

		def answer(SCREAMING_SNAKE_CASE__	) -> str:
				if number > to_guess:
						return "high"
				elif number < to_guess:
						return "low"
				else:
						return "same"

		print("started..."	)

		a_     :      List[str] 						=     lower
		a_     :      Dict 						=     higher

		a_     :      str 						=     []

		while True:
				a_     :      List[str] 						=     get_avg(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
				last_numbers.append(SCREAMING_SNAKE_CASE__	)

				if answer(SCREAMING_SNAKE_CASE__	) == "low":
						a_     :      Optional[Any] 						=     number
				elif answer(SCREAMING_SNAKE_CASE__	) == "high":
						a_     :      Union[str, Any] 						=     number
				else:
						break

		print(F"""guess the number : {last_numbers[-1]}"""	)
		print(F"""details : {last_numbers!s}"""	)



def        lowerCAmelCase_						(							)			->      None:
		a_     :      str 						=     int(input("Enter lower value : "	).strip()	)
		a_     :      Dict 						=     int(input("Enter high value : "	).strip()	)
		a_     :      Optional[Any] 						=     int(input("Enter value to guess : "	).strip()	)
		guess_the_number(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					main()




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     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")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2