import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




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





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
    _UpperCamelCase:  List[Any]    						=			sd_pipe.to(_lowercase				)
    sd_pipe.set_progress_bar_config(disable=_lowercase				)

    sd_pipe.set_scheduler('''sample_euler'''				)

    _UpperCamelCase:  Any    						=			'''A painting of a squirrel eating a burger'''
    _UpperCamelCase:  Dict    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Any    						=			sd_pipe([prompt]		,	generator=_lowercase		,	guidance_scale=9.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Dict    						=			output.images

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

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  str    						=			np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]				)

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



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sd_pipe.to(_lowercase				)
    sd_pipe.set_progress_bar_config(disable=_lowercase				)

    sd_pipe.set_scheduler('''sample_euler'''				)

    _UpperCamelCase:  str    						=			'''A painting of a squirrel eating a burger'''
    _UpperCamelCase:  Any    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Any    						=			sd_pipe([prompt]		,	generator=_lowercase		,	guidance_scale=9.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Optional[Any]    						=			output.images

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

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  List[str]    						=			np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-1



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  Optional[Any]    						=			sd_pipe.to(_lowercase				)
    sd_pipe.set_progress_bar_config(disable=_lowercase				)

    sd_pipe.set_scheduler('''sample_dpmpp_2m'''				)

    _UpperCamelCase:  List[Any]    						=			'''A painting of a squirrel eating a burger'''
    _UpperCamelCase:  Optional[int]    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			sd_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	num_inference_steps=15		,	output_type='''np'''		,	use_karras_sigmas=_lowercase		,	)

    _UpperCamelCase:  Union[str, Any]    						=			output.images

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

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  Dict    						=			np.array(
        [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				complex	,       lowercase:				str = "x"	,       lowercase:				float = 10**-10	,       lowercase:				int = 1	,       )     ->					complex:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			symbols(lowercase		)
 _UpperCamelCase:  str    						=			lambdify(lowercase	,       lowercase		)
 _UpperCamelCase:  str    						=			lambdify(lowercase	,       diff(lowercase	,       lowercase		)		)

 _UpperCamelCase:  Optional[int]    						=			starting_point

 while True:
  if diff_function(lowercase		) != 0:
   _UpperCamelCase:  int    						=			prev_guess - multiplicity * func(lowercase		) / diff_function(
       lowercase		)
  else:
   raise ZeroDivisionError('''Could not find root'''		) from None

  # Precision is checked by comparing the difference of consecutive guesses
  if abs(next_guess - prev_guess		) < precision:
   return next_guess

  _UpperCamelCase:  Any    						=			next_guess


# 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
  # Find fourth Root of 5
  print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      f"""{newton_raphson('exp(x) - 1', 1_0, precision=0.0_0_5)}""",
  )

  # Find root of cos(x)
  print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")