




"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					_snake_case						,				unittest.TestCase					):
						_a    						=						DiTPipeline
						_a    						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
						_a    						=						PipelineTesterMixin.required_optional_params - {
						    'latents',
						    'num_images_per_prompt',
						    'callback',
						    'callback_steps',
						}
						_a    						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
						_a    						=						False


						def   __lowercase (							self				:			List[Any]					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				TransformeraDModel(
										    sample_size=16						,						num_layers=2						,						patch_size=4						,						attention_head_dim=8						,						num_attention_heads=2						,						in_channels=4						,						out_channels=8						,						attention_bias=lowerCAmelCase__						,						activation_fn="""gelu-approximate"""						,						num_embeds_ada_norm=1000						,						norm_type="""ada_norm_zero"""						,						norm_elementwise_affine=lowerCAmelCase__						,						)
										lowerCAmelCase  =				AutoencoderKL()
										lowerCAmelCase  =				DDIMScheduler()
										lowerCAmelCase  =				{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
										return components


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			List[Any]=0					):
										if str(lowerCAmelCase__					).startswith("""mps"""					):
														lowerCAmelCase  =				torch.manual_seed(lowerCAmelCase__					)
										else:
														lowerCAmelCase  =				torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
										lowerCAmelCase  =				{
										    """class_labels""": [1],
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """output_type""": """numpy""",
										}
										return inputs


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				"""cpu"""

										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				self.pipeline_class(**lowerCAmelCase__					)
										pipe.to(lowerCAmelCase__					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase__					)
										lowerCAmelCase  =				pipe(**lowerCAmelCase__					).images
										lowerCAmelCase  =				image[0, -3:, -3:, -1]

										self.assertEqual(image.shape						,						(1, 16, 16, 3)					)
										lowerCAmelCase  =				np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]					)
										lowerCAmelCase  =				np.abs(image_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(lowerCAmelCase__						,						1e-3					)


						def   __lowercase (							self				:			Optional[int]					):
										self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase__						,						expected_max_diff=1e-3					)




						@unittest.skipIf(
						    torch_device != """cuda""" or not is_xformers_available()						,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,						)
						def   __lowercase (							self				:			Any					):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






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


						def   __lowercase (							self				:			str					):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				torch.manual_seed(0					)

										lowerCAmelCase  =				DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
										pipe.to("""cuda"""					)

										lowerCAmelCase  =				["""vase""", """umbrella""", """white shark""", """white wolf"""]
										lowerCAmelCase  =				pipe.get_label_ids(lowerCAmelCase__					)

										lowerCAmelCase  =				pipe(lowerCAmelCase__						,						generator=lowerCAmelCase__						,						num_inference_steps=40						,						output_type="""np"""					).images

										for word, image in zip(lowerCAmelCase__						,						lowerCAmelCase__					):
														lowerCAmelCase  =				load_numpy(
														    f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''					)
														assert np.abs((expected_image - image).max()					) < 1e-2




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""					)
										lowerCAmelCase  =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
										pipe.to("""cuda"""					)

										lowerCAmelCase  =				["""vase""", """umbrella"""]
										lowerCAmelCase  =				pipe.get_label_ids(lowerCAmelCase__					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(lowerCAmelCase__						,						generator=lowerCAmelCase__						,						num_inference_steps=25						,						output_type="""np"""					).images

										for word, image in zip(lowerCAmelCase__						,						lowerCAmelCase__					):
														lowerCAmelCase  =				load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    f'''/dit/{word}_512.npy'''					)

														assert np.abs((expected_image - image).max()					) < 1e-1











"""simple docstring"""







from __future__ import annotations

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

from sympy import diff





def 							lowercase					(snake_case__   : str      ,       snake_case__   : float | Decimal      ,       snake_case__   : float = 10**-10 )     ->  float:


				'''simple docstring'''






				lowerCAmelCase  =				a
				while True:
								lowerCAmelCase  =				Decimal(snake_case__ ) - (
								    Decimal(eval(snake_case__ ) ) / Decimal(eval(str(diff(snake_case__ ) ) ) )  # noqa: S307
								)
								# This number dictates the accuracy of the answer
								if abs(eval(snake_case__ ) ) < precision:  # noqa: S307
												return float(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)}""")





