

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

def 						UpperCamelCase					(lowercase_: int					)     ->      Tuple:

	# A local function to see if a dot lands in the circle.
	def is_in_circle(lowercase_: float    ,      lowercase_: float					) -> bool:
		A__							:    Optional[Any]        =		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
	A__							:    Any        =		mean(
	    int(is_in_circle(uniform(-1.0    ,      1.0					)    ,      uniform(-1.0    ,      1.0					)					)					)
	    for _ in range(lowercase_					)					)
	# The ratio of the area for circle to square is pi/4.
	A__							:    Tuple        =		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 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Callable[[float], float]    ,      lowercase_: float = 0.0    ,      lowercase_: float = 1.0    ,      )     ->      float:
	return mean(
	    function_to_integrate(uniform(lowercase_    ,      lowercase_					)					) for _ in range(lowercase_					)					) * (max_value - min_value)

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: float = 0.0    ,      lowercase_: float = 1.0					)     ->      None:

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

	A__							:    str        =		area_under_curve_estimator(
	    lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_					)
	A__							:    Dict        =		(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 						UpperCamelCase					(lowercase_: int					)     ->      None:

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

	A__							:    Tuple        =		area_under_curve_estimator(
	    lowercase_    ,      lowercase_    ,      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()






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		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""")    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



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


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



