
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class        _UpperCamelCase				(					_A     ,       _A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[Any]					      = VQModel
			__UpperCamelCase      :						Tuple					      = """sample"""

			@property
			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Optional[int]=(32, 32)      ):
							UpperCamelCase_:							List[Any]	=						4
							UpperCamelCase_:							Optional[Any]	=						3

							UpperCamelCase_:							Dict	=						floats_tensor((batch_size, num_channels) + sizes      ).to(snake_case_      )

							return {"sample": image}

			@property
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							return (3, 32, 32)

			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							return (3, 32, 32)

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Optional[int]	=						{
							    """block_out_channels""": [32, 64],
							    """in_channels""": 3,
							    """out_channels""": 3,
							    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
							    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
							    """latent_channels""": 3,
							}
							UpperCamelCase_:							List[Any]	=						self.dummy_input
							return init_dict, inputs_dict

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							pass

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							pass

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_,   UpperCamelCase_:							Union[str, Any]	=						VQModel.from_pretrained("""fusing/vqgan-dummy"""						,	output_loading_info=snake_case_      )
							self.assertIsNotNone(snake_case_      )
							self.assertEqual(len(loading_info["""missing_keys"""]      )						,	0      )

							model.to(snake_case_      )
							UpperCamelCase_:							int	=						model(**self.dummy_input      )

							assert image is not None, "Make sure output is not None"




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						VQModel.from_pretrained("""fusing/vqgan-dummy"""      )
							model.to(snake_case_      ).eval()

							torch.manual_seed(0      )
							if torch.cuda.is_available():
											torch.cuda.manual_seed_all(0      )

							UpperCamelCase_:							Optional[int]	=						torch.randn(1						,	model.config.in_channels						,	model.config.sample_size						,	model.config.sample_size      )
							UpperCamelCase_:							Union[str, Any]	=						image.to(snake_case_      )
							with torch.no_grad():
											UpperCamelCase_:							Dict	=						model(snake_case_      ).sample

							UpperCamelCase_:							Dict	=						output[0, -1, -3:, -3:].flatten().cpu()
							# fmt: off
							UpperCamelCase_:							str	=						torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
							# fmt: on
							self.assertTrue(torch.allclose(snake_case_						,	snake_case_						,	atol=1e-3      )      )








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = "x"     ,    lowerCamelCase = 10**-10     ,    lowerCamelCase = 1     ,    )  ->	complex:
				UpperCamelCase_:							Optional[Any]	=						symbols(lowerCamelCase						)
				UpperCamelCase_:							int	=						lambdify(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						lambdify(lowerCamelCase     ,    diff(lowerCamelCase     ,    lowerCamelCase						)						)

				UpperCamelCase_:							Tuple	=						starting_point

				while True:
								if diff_function(lowerCamelCase						) != 0:
												UpperCamelCase_:							List[Any]	=						prev_guess - multiplicity * func(lowerCamelCase						) / diff_function(
												    lowerCamelCase						)
								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', 10, precision=0.005)}""",
							)

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






