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    A			(					_lowercase	,						_lowercase=10		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							[]
						for _ in range(_lowerCamelCase		):
												lrs.append(scheduler.get_lr()[0]		)
												scheduler.step()
						return lrs
def    A			(					_lowercase	,						_lowercase=10		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
						for step in range(_lowerCamelCase		):
												lrs.append(scheduler.get_lr()[0]		)
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							os.path.join(_lowerCamelCase	,						'''schedule.bin'''		)
																								torch.save(scheduler.state_dict()	,						_lowerCamelCase		)

																								SCREAMING_SNAKE_CASE							:					str           =							torch.load(_lowerCamelCase		)
																								scheduler.load_state_dict(_lowerCamelCase		)
						return lrs





@require_torch
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												self.assertEqual(len(_A		)					,	len(_A		)		)
												for a, b in zip(_A					,	_A		):
																		self.assertAlmostEqual(_A					,	_A					,	delta=_A		)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor([0.1, -0.2, -0.1]					,	requires_grad=_A		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor([0.4, 0.2, -0.5]		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												SCREAMING_SNAKE_CASE							:					List[Any]           =							AdamW(params=[w]					,	lr=2E-1					,	weight_decay=0.0		)
												for _ in range(100		):
																		SCREAMING_SNAKE_CASE							:					int           =							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  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.tensor([0.1, -0.2, -0.1]					,	requires_grad=_A		)
												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor([0.4, 0.2, -0.5]		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												SCREAMING_SNAKE_CASE							:					int           =							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		):
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							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       lowercase__		(     unittest.TestCase):
						UpperCamelCase_  =	nn.Linear(50			,							50) if is_torch_available() else None
						UpperCamelCase_  =	AdamW(m.parameters()			,							lr=10.0) if is_torch_available() else None
						UpperCamelCase_  =	10


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[Any]=None		):





												'''simple docstring'''





												self.assertEqual(len(_A		)					,	len(_A		)		)
												for a, b in zip(_A					,	_A		):
																		self.assertAlmostEqual(_A					,	_A					,	delta=_A					,	msg=_A		)



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							{'num_warmup_steps': 2, 'num_training_steps': 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{
												    get_constant_schedule: ({}, [10.0] * self.num_steps),
												    get_constant_schedule_with_warmup: (
												        {'num_warmup_steps': 4},
												        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
												    ),
												    get_linear_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
												    ),
												    get_cosine_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
												    ),
												    get_cosine_with_hard_restarts_schedule_with_warmup: (
												        {**common_kwargs, 'num_cycles': 2},
												        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
												    ),
												    get_polynomial_decay_schedule_with_warmup: (
												        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
												        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
												    ),
												    get_inverse_sqrt_schedule: (
												        {'num_warmup_steps': 2},
												        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
												    ),
												}

												for scheduler_func, data in scheds.items():
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							data

																		SCREAMING_SNAKE_CASE							:					Dict           =							scheduler_func(self.optimizer					,	**_A		)
																		self.assertEqual(len([scheduler.get_lr()[0]]		)					,	1		)
																		SCREAMING_SNAKE_CASE							:					int           =							unwrap_schedule(_A					,	self.num_steps		)
																		self.assertListAlmostEqual(
																		    _A					,	_A					,	tol=1E-2					,	msg=f"""failed for {scheduler_func} in normal scheduler"""					,	)

																		SCREAMING_SNAKE_CASE							:					Dict           =							scheduler_func(self.optimizer					,	**_A		)
																		if scheduler_func.__name__ != "get_constant_schedule":
																								LambdaScheduleWrapper.wrap_scheduler(_A		)  # wrap to test picklability of the schedule
																		SCREAMING_SNAKE_CASE							:					int           =							unwrap_and_save_reload_schedule(_A					,	self.num_steps		)
																		self.assertListEqual(_A					,	_A					,	msg=f"""failed for {scheduler_func} in save and reload"""		)






class       lowercase__		:


						def __init__( self			:       Any					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							fn


						def __call__( self			:       List[Any]					,	*UpperCamelCase__			:       Optional[Any]					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return self.fn(*_A					,	**_A		)



						@classmethod
						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							list(map(self					,	scheduler.lr_lambdas		)		)






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCamelCase	: str    		=  False





class       lowercase__		(     unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class       lowercase__		(     unittest.TestCase):


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





												'''simple docstring'''





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


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							generator.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''cyberpunk 2077'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipe.dual_guided(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''					,	).images

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

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					int           =							np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							pipe.text_to_image(
												    prompt=UpperCamelCase__					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Any           =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe.image_variation(UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

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





