

"""simple docstring"""


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    snake_case     (    __snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				int       				=  VQModel
				SCREAMING_SNAKE_CASE_       :				Any       				=  """sample"""

				@property
				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Optional[int]=(3_2, 3_2))->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      4
						__lowerCAmelCase:	Optional[int]   				=      3

						__lowerCAmelCase:	List[str]   				=      floats_tensor((batch_size, num_channels) + sizes).to(UpperCamelCase__)

						return {"sample": image}

				@property
				def 							lowercase_					(     self							:    int)->       int:



						'''simple docstring'''


						return (3, 3_2, 3_2)

				@property
				def 							lowercase_					(     self							:    Tuple)->       List[Any]:



						'''simple docstring'''


						return (3, 3_2, 3_2)

				def 							lowercase_					(     self							:    Dict)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      {
						    "block_out_channels": [3_2, 6_4],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 3,
						}
						__lowerCAmelCase:	List[str]   				=      self.dummy_input
						return init_dict, inputs_dict

				def 							lowercase_					(     self							:    str)->       List[Any]:



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Any)->       str:



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	int   				=      VQModel.from_pretrained("fusing/vqgan-dummy"					,  output_loading_info=UpperCamelCase__)
						self.assertIsNotNone(UpperCamelCase__)
						self.assertEqual(len(loading_info["missing_keys"])					,  0)

						model.to(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      model(**self.dummy_input)

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




				def 							lowercase_					(     self							:    List[Any])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      VQModel.from_pretrained("fusing/vqgan-dummy")
						model.to(UpperCamelCase__).eval()

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

						__lowerCAmelCase:	List[Any]   				=      torch.randn(1					,  model.config.in_channels					,  model.config.sample_size					,  model.config.sample_size)
						__lowerCAmelCase:	List[Any]   				=      image.to(UpperCamelCase__)
						with torch.no_grad():
								__lowerCAmelCase:	Dict   				=      model(UpperCamelCase__).sample

						__lowerCAmelCase:	int   				=      output[0, -1, -3:, -3:].flatten().cpu()
						# fmt: off
						__lowerCAmelCase:	List[Any]   				=      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(UpperCamelCase__					,  UpperCamelCase__					,  atol=1e-3))





"""simple docstring"""


from __future__ import annotations

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

from sympy import diff






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1_0**-1_0			)     ->     float:
		__lowerCAmelCase:	Union[str, Any]   				=      a
		while True:
				__lowerCAmelCase:	Optional[int]   				=      Decimal(__SCREAMING_SNAKE_CASE			) - (
				    Decimal(eval(__SCREAMING_SNAKE_CASE			)			) / Decimal(eval(str(diff(__SCREAMING_SNAKE_CASE			)			)			)			)  # noqa: S307
				)
				# This number dictates the accuracy of the answer
				if abs(eval(__SCREAMING_SNAKE_CASE			)			) < precision:  # noqa: S307
						return float(__SCREAMING_SNAKE_CASE			)


# 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)}''')


