


'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class      lowercase			(							A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       DDIMPipeline
		_a									=       UNCONDITIONAL_IMAGE_GENERATION_PARAMS
		_a									=       PipelineTesterMixin.required_optional_params - {
		    'num_images_per_prompt',
		    'latents',
		    'callback',
		    'callback_steps',
		}
		_a									=       UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :List[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''')					,     )
							UpperCamelCase__     :Union[str, Any]							      =				DDIMScheduler()
							UpperCamelCase__     :List[str]							      =				{'''unet''': unet, '''scheduler''': scheduler}
							return components




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__     :Optional[Any]							      =				torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__     :List[Any]							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				{
							    '''batch_size''': 1,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				'''cpu'''

							UpperCamelCase__     :Optional[int]							      =				self.get_dummy_components()
							UpperCamelCase__     :Dict							      =				self.pipeline_class(**UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :str							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :Dict							      =				image[0, -3:, -3:, -1]

							self.assertEqual(image.shape					,     (1, 32, 32, 3)							)
							UpperCamelCase__     :int							      =				np.array(
							    [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]							)
							UpperCamelCase__     :int							      =				np.abs(image_slice.flatten() - expected_slice							).max()
							self.assertLessEqual(UpperCamelCase_					,     1e-3							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().test_save_load_local(expected_max_difference=3e-3							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().test_save_load_optional_components(expected_max_difference=3e-3							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3							)



@slow
@require_torch_gpu
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''google/ddpm-cifar10-32'''

							UpperCamelCase__     :Union[str, Any]							      =				UNetaDModel.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				DDIMScheduler()

							UpperCamelCase__     :Optional[int]							      =				DDIMPipeline(unet=UpperCamelCase_					,     scheduler=UpperCamelCase_							)
							ddim.to(UpperCamelCase_							)
							ddim.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				torch.manual_seed(0							)
							UpperCamelCase__     :Tuple							      =				ddim(generator=UpperCamelCase_					,     eta=0.0					,     output_type='''numpy'''							).images

							UpperCamelCase__     :Tuple							      =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__     :Optional[Any]							      =				np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]							)

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				'''google/ddpm-ema-bedroom-256'''

							UpperCamelCase__     :List[str]							      =				UNetaDModel.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				DDIMScheduler.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				DDIMPipeline(unet=UpperCamelCase_					,     scheduler=UpperCamelCase_							)
							ddpm.to(UpperCamelCase_							)
							ddpm.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				torch.manual_seed(0							)
							UpperCamelCase__     :Optional[Any]							      =				ddpm(generator=UpperCamelCase_					,     output_type='''numpy'''							).images

							UpperCamelCase__     :Optional[int]							      =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							UpperCamelCase__     :List[Any]							      =				np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]							)

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



'''simple docstring'''
from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff


def     a   (    __a				,      __a				,      __a = 10**-10     )						->			float:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				a
					while True:
										UpperCamelCase__     :Dict							      =				Decimal(__a     ) - (
										    Decimal(eval(__a     )     ) / Decimal(eval(str(diff(__a     )     )     )     )  # noqa: S307
										)
										# This number dictates the accuracy of the answer
										if abs(eval(__a     )     ) < precision:  # noqa: S307
															return float(__a     )


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
		# Find root of polynomial
		print(F"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
		# Find Square Root of 5
		print(F"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
		# Exponential Roots
		print(F"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")