"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=10 ):






							"""simple docstring"""





							A__		   =							[]
							for _ in range(UpperCamelCase_ ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
							return lrs






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=10 ):






							"""simple docstring"""





							A__		   =							[]
							for step in range(UpperCamelCase_ ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												A__		   =							os.path.join(UpperCamelCase_				,			'schedule.bin' )
																												torch.save(scheduler.state_dict()				,			UpperCamelCase_ )

																												A__		   =							torch.load(UpperCamelCase_ )
																												scheduler.load_state_dict(UpperCamelCase_ )
							return lrs
@require_torch
class 			UpperCamelCase__(	unittest.TestCase       ):





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													self.assertEqual(len(UpperCAmelCase__   )						,len(UpperCAmelCase__   )   )
													for a, b in zip(UpperCAmelCase__						,UpperCAmelCase__   ):
																				self.assertAlmostEqual(UpperCAmelCase__						,UpperCAmelCase__						,delta=UpperCAmelCase__   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							torch.tensor([0.1, -0.2, -0.1]						,requires_grad=UpperCAmelCase__   )
													A__		   =							torch.tensor([0.4, 0.2, -0.5]   )
													A__		   =							nn.MSELoss()
													# No warmup, constant schedule, no gradient clipping
													A__		   =							AdamW(params=[w]						,lr=2e-1						,weight_decay=0.0   )
													for _ in range(1_00   ):
																				A__		   =							criterion(UpperCAmelCase__						,UpperCAmelCase__   )
																				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  snake_case__		(				self   )    -> str:
													A__		   =							torch.tensor([0.1, -0.2, -0.1]						,requires_grad=UpperCAmelCase__   )
													A__		   =							torch.tensor([0.4, 0.2, -0.5]   )
													A__		   =							nn.MSELoss()
													# No warmup, constant schedule, no gradient clipping
													A__		   =							Adafactor(
													    params=[w]						,lr=1e-2						,eps=(1e-30, 1e-3)						,clip_threshold=1.0						,decay_rate=-0.8						,betaa=UpperCAmelCase__						,weight_decay=0.0						,relative_step=UpperCAmelCase__						,scale_parameter=UpperCAmelCase__						,warmup_init=UpperCAmelCase__						,)
													for _ in range(10_00   ):
																				A__		   =							criterion(UpperCAmelCase__						,UpperCAmelCase__   )
																				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       ):
						lowerCAmelCase__     :  Any  							=						nn.Linear(50						,  50       ) if is_torch_available() else None
						lowerCAmelCase__     :  Optional[Any]  							=						AdamW(m.parameters()						,  lr=10.0       ) if is_torch_available() else None
						lowerCAmelCase__     :  Tuple  							=						10





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None   )    -> Optional[int]:
													self.assertEqual(len(UpperCAmelCase__   )						,len(UpperCAmelCase__   )   )
													for a, b in zip(UpperCAmelCase__						,UpperCAmelCase__   ):
																				self.assertAlmostEqual(UpperCAmelCase__						,UpperCAmelCase__						,delta=UpperCAmelCase__						,msg=UpperCAmelCase__   )





						def  snake_case__		(				self   )    -> Optional[int]:
													A__		   =							{'num_warmup_steps': 2, 'num_training_steps': 10}
													# schedulers doct format
													# function: (sched_args_dict, expected_learning_rates)
													A__		   =							{
													    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():
																				A__   ,     A__		   =							data

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

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






class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase   )    -> List[str]:
													A__		   =							fn





						def __call__(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> str:
													return self.fn(*UpperCAmelCase__						,**UpperCAmelCase__   )





						@classmethod
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							list(map(self						,scheduler.lr_lambdas   )   )








"""simple docstring"""





import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort




class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	"ssube/stable-diffusion-x4-upscaler-onnx"


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			List[str]=0	):
    __lowercase								=						floats_tensor((1, 3, 1_2_8, 1_2_8),					rng=random.Random(UpperCAmelCase__	)	)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "image": image,
        "generator": generator,
        "num_inference_steps": 3,
        "guidance_scale": 7.5,
        "output_type": "numpy",
    }
    return inputs


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1].flatten()

    # started as 128, should now be 512
    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						PNDMScheduler.from_config(pipe.scheduler.config,					skip_prk_steps=UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			int	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]	)

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


 def 					_lowercase (			self							:			str	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]	)

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







@nightly
@require_onnxruntime
@require_torch_gpu
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @property
 def 					_lowercase (			self							:			Tuple	):
    return (
        "CUDAExecutionProvider",
        {
            "gpu_mem_limit": "15000000000",  # 15GB
            "arena_extend_strategy": "kSameAsRequested",
        },
    )


 @property
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						ort.SessionOptions()
    __lowercase								=						False
    return options


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    # using the PNDM scheduler by default
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=1_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



 def 					_lowercase (			self							:			str	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    __lowercase								=						LMSDiscreteScheduler.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					subfolder="scheduler"	)
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					scheduler=UpperCAmelCase__,					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=2_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




