



'''simple docstring'''




import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    Adafactor,
							    AdamW,
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_inverse_sqrt_schedule,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1_0	)   ->    Optional[int]:
							UpperCAmelCase  :	Any							  =	[]
							for _ in range(_lowercase	):
														lrs.append(scheduler.get_lr()[0]	)
														scheduler.step()
							return lrs



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1_0	)   ->    Tuple:
							UpperCAmelCase  :	List[str]							  =	[]
							for step in range(_lowercase	):
														lrs.append(scheduler.get_lr()[0]	)
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												UpperCAmelCase  :	Union[str, Any]							  =	os.path.join(_lowercase			,       """schedule.bin"""	)
																												torch.save(scheduler.state_dict()			,       _lowercase	)

																												UpperCAmelCase  :	Dict							  =	torch.load(_lowercase	)
																												scheduler.load_state_dict(_lowercase	)
							return lrs

@require_torch
class     UpperCamelCase_				(							unittest.TestCase    ):





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Optional[int]:
								self.assertEqual(len(__A					)	,	len(__A					)					)
								for a, b in zip(__A	,	__A					):
															self.assertAlmostEqual(__A	,	__A	,	delta=__A					)





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	int							  =	torch.tensor([0.1, -0.2, -0.1]	,	requires_grad=__A					)
								UpperCAmelCase  :	Dict							  =	torch.tensor([0.4, 0.2, -0.5]					)
								UpperCAmelCase  :	Optional[int]							  =	nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								UpperCAmelCase  :	List[Any]							  =	AdamW(params=[w]	,	lr=2e-1	,	weight_decay=0.0					)
								for _ in range(100					):
															UpperCAmelCase  :	Optional[Any]							  =	criterion(__A	,	__A					)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()	,	[0.4, 0.2, -0.5]	,	tol=1e-2					)






	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Any							  =	torch.tensor([0.1, -0.2, -0.1]	,	requires_grad=__A					)
								UpperCAmelCase  :	Optional[int]							  =	torch.tensor([0.4, 0.2, -0.5]					)
								UpperCAmelCase  :	int							  =	nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								UpperCAmelCase  :	Union[str, Any]							  =	Adafactor(
								    params=[w]	,	lr=1e-2	,	eps=(1e-30, 1e-3)	,	clip_threshold=1.0	,	decay_rate=-0.8	,	betaa=__A	,	weight_decay=0.0	,	relative_step=__A	,	scale_parameter=__A	,	warmup_init=__A	,	)
								for _ in range(1000					):
															UpperCAmelCase  :	str							  =	criterion(__A	,	__A					)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()	,	[0.4, 0.2, -0.5]	,	tol=1e-2					)




@require_torch
class     UpperCamelCase_				(							unittest.TestCase    ):
	lowercase    =				nn.Linear(50   ,  50    ) if is_torch_available() else None
	lowercase    =				AdamW(m.parameters()   ,  lr=10.0    ) if is_torch_available() else None
	lowercase    =				10





	def 				_lowercase(						self	,	A	,	A	,	A	,	A=None					)    ->    List[Any]:
								self.assertEqual(len(__A					)	,	len(__A					)					)
								for a, b in zip(__A	,	__A					):
															self.assertAlmostEqual(__A	,	__A	,	delta=__A	,	msg=__A					)






	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	{"""num_warmup_steps""": 2, """num_training_steps""": 10}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								UpperCAmelCase  :	List[str]							  =	{
								    get_constant_schedule: ({}, [1_0.0] * self.num_steps),
								    get_constant_schedule_with_warmup: (
								        {"""num_warmup_steps""": 4},
								        [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
								    ),
								    get_linear_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
								    ),
								    get_cosine_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
								    ),
								    get_cosine_with_hard_restarts_schedule_with_warmup: (
								        {**common_kwargs, """num_cycles""": 2},
								        [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
								    ),
								    get_polynomial_decay_schedule_with_warmup: (
								        {**common_kwargs, """power""": 2.0, """lr_end""": 1e-7},
								        [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
								    ),
								    get_inverse_sqrt_schedule: (
								        {"""num_warmup_steps""": 2},
								        [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
								    ),
								}

								for scheduler_func, data in scheds.items():
															UpperCAmelCase  ,  UpperCAmelCase  :	Tuple							  =	data

															UpperCAmelCase  :	str							  =	scheduler_func(self.optimizer	,	**__A					)
															self.assertEqual(len([scheduler.get_lr()[0]]					)	,	1					)
															UpperCAmelCase  :	Dict							  =	unwrap_schedule(__A	,	self.num_steps					)
															self.assertListAlmostEqual(
															    __A	,	__A	,	tol=1e-2	,	msg=f'''failed for {scheduler_func} in normal scheduler'''	,	)

															UpperCAmelCase  :	Optional[Any]							  =	scheduler_func(self.optimizer	,	**__A					)
															if scheduler_func.__name__ != "get_constant_schedule":
																						LambdaScheduleWrapper.wrap_scheduler(__A					)  # wrap to test picklability of the schedule
															UpperCAmelCase  :	List[Any]							  =	unwrap_and_save_reload_schedule(__A	,	self.num_steps					)
															self.assertListEqual(__A	,	__A	,	msg=f'''failed for {scheduler_func} in save and reload'''					)




class     UpperCamelCase_				:





	def __init__(						self	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	fn





	def __call__(						self	,	*A	,	**A					)    ->    Optional[int]:
								return self.fn(*__A	,	**__A					)






	@classmethod
	def 				_lowercase(						self	,	A					)    ->    List[str]:
								UpperCAmelCase  :	Optional[Any]							  =	list(map(self	,	scheduler.lr_lambdas					)					)






'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a     :    Tuple      						=					False

class     UpperCamelCase_				(							unittest.TestCase    ):
	pass




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





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Any							  =	VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""					)
								pipe.to(A					)
								pipe.set_progress_bar_config(disable=A					)

								UpperCAmelCase  :	List[str]							  =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""					)
								UpperCAmelCase  :	List[Any]							  =	torch.manual_seed(0					)
								UpperCAmelCase  :	List[str]							  =	pipe(
								    image=A	,	generator=A	,	guidance_scale=7.5	,	num_inference_steps=50	,	output_type="""numpy"""	,	).images

								UpperCAmelCase  :	Tuple							  =	image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase  :	List[str]							  =	np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]					)

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

