
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							@property
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													return self.get_dummy_input()



							@property
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													if self.block_type == "down":
																			return (4, 32, 16, 16)
													elif self.block_type == "mid":
																			return (4, 32, 32, 32)
													elif self.block_type == "up":
																			return (4, 32, 64, 64)

													raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			4
													__UpperCamelCase       =			32
													__UpperCamelCase       =			(32, 32)

													__UpperCamelCase       =			torch.manual_seed(0			)
													__UpperCamelCase       =			torch.device(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			(batch_size, num_channels) + sizes
													__UpperCamelCase       =			randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			{'''hidden_states''': hidden_states}

													if include_temb:
																			__UpperCamelCase       =			128
																			__UpperCamelCase       =			randn_tensor((batch_size, temb_channels)     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=SCREAMING_SNAKE_CASE_			)

													if include_res_hidden_states_tuple:
																			__UpperCamelCase       =			torch.manual_seed(1			)
																			__UpperCamelCase       =			(randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=SCREAMING_SNAKE_CASE_			),)

													if include_encoder_hidden_states:
																			__UpperCamelCase       =			floats_tensor((batch_size, 32, 32)			).to(SCREAMING_SNAKE_CASE_			)

													if include_skip_sample:
																			__UpperCamelCase       =			randn_tensor(((batch_size, 3) + sizes)     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=SCREAMING_SNAKE_CASE_			)

													return dummy_input



							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			{
													    '''in_channels''': 32,
													    '''out_channels''': 32,
													    '''temb_channels''': 128,
													}
													if self.block_type == "up":
																			__UpperCamelCase       =			32

													if self.block_type == "mid":
																			init_dict.pop('''out_channels'''			)

													__UpperCamelCase       =			self.dummy_input
													return init_dict, inputs_dict



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.prepare_init_args_and_inputs_for_common()
													__UpperCamelCase       =			self.block_class(**SCREAMING_SNAKE_CASE_			)
													unet_block.to(SCREAMING_SNAKE_CASE_			)
													unet_block.eval()

													with torch.no_grad():
																			__UpperCamelCase       =			unet_block(**SCREAMING_SNAKE_CASE_			)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			output[0]

													self.assertEqual(output.shape     ,	self.output_shape			)

													__UpperCamelCase       =			output[0, -1, -3:, -3:]
													__UpperCamelCase       =			torch.tensor(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													assert torch_all_close(output_slice.flatten()     ,	SCREAMING_SNAKE_CASE_     ,	atol=5E-3			)



							@unittest.skipIf(torch_device == '''mps'''     ,	'''Training is not supported in mps'''			)
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.prepare_init_args_and_inputs_for_common()
													__UpperCamelCase       =			self.block_class(**SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.train()
													__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			output[0]

													__UpperCamelCase       =			torch.device(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			randn_tensor(output.shape     ,	device=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)
													loss.backward()






from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 	A_ (							snake_case							:   int )      ->    int:



						'''simple docstring'''

						def is_in_circle(snake_case							:   float ,      snake_case							:   float ) -> bool:
												__UpperCamelCase       =			sqrt((x**2) + (y**2) )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						__UpperCamelCase       =			mean(
						    int(is_in_circle(uniform(-1.0 ,      1.0 ) ,      uniform(-1.0 ,      1.0 ) ) )
						    for _ in range(snake_case ) )
						# The ratio of the area for circle to square is pi/4.
						__UpperCamelCase       =			proportion * 4
						print(f"The estimated value of pi is {pi_estimate}" )
						print(f"The numpy value of pi is {pi}" )
						print(f"The total error is {abs(pi - pi_estimate )}" )




def 	A_ (							snake_case							:   int ,      snake_case							:   Callable[[float], float] ,      snake_case							:   float = 0.0 ,      snake_case							:   float = 1.0 ,      )      ->    float:



						'''simple docstring'''

						return mean(
						    function_to_integrate(uniform(snake_case ,      snake_case ) ) for _ in range(snake_case ) ) * (max_value - min_value)




def 	A_ (							snake_case							:   int ,      snake_case							:   float = 0.0 ,      snake_case							:   float = 1.0 )      ->    None:



						'''simple docstring'''

						def identity_function(snake_case							:   float ) -> float:
												return x

						__UpperCamelCase       =			area_under_curve_estimator(
						    snake_case ,      snake_case ,      snake_case ,      snake_case )
						__UpperCamelCase       =			(max_value * max_value - min_value * min_value) / 2

						print('''******************''' )
						print(f"Estimating area under y=x where x varies from {min_value} to {max_value}" )
						print(f"Estimated value is {estimated_value}" )
						print(f"Expected value is {expected_value}" )
						print(f"Total error is {abs(estimated_value - expected_value )}" )
						print('''******************''' )




def 	A_ (							snake_case							:   int )      ->    None:



						'''simple docstring'''

						def function_to_integrate(snake_case							:   float ) -> float:
												return sqrt(4.0 - x * x )

						__UpperCamelCase       =			area_under_curve_estimator(
						    snake_case ,      snake_case ,      0.0 ,      2.0 )

						print('''******************''' )
						print('''Estimating pi using area_under_curve_estimator''' )
						print(f"Estimated value is {estimated_value}" )
						print(f"Expected value is {pi}" )
						print(f"Total error is {abs(estimated_value - pi )}" )
						print('''******************''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




