



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 						a__  (					_snake_case,      _snake_case,      _snake_case,      unittest.TestCase						):
	__magic_name__		:     Union[str, Any]						     =  StableDiffusionInpaintPipeline
	__magic_name__		:     Optional[Any]						     =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	__magic_name__		:     Optional[Any]						     =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__magic_name__		:     Tuple						     =  frozenset(
	    []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	__magic_name__		:     Union[str, Any]						     =  frozenset([]						)


	def 					lowercase__	(self			:			Optional[Any]   )  ->      List[Any]:

					"""simple docstring"""

					torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE	:    str          =			UNetaDConditionModel(
					    block_out_channels=(32, 64),       layers_per_block=2,       sample_size=32,       in_channels=9,       out_channels=4,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),       cross_attention_dim=32,       attention_head_dim=(2, 4),       use_linear_projection=_lowerCamelCase,       )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			PNDMScheduler(skip_prk_steps=_lowerCamelCase   )
					torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			AutoencoderKL(
					    block_out_channels=[32, 64],       in_channels=3,       out_channels=3,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],       latent_channels=4,       sample_size=128,       )
					torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE	:    str          =			CLIPTextConfig(
					    bos_token_id=0,       eos_token_id=2,       hidden_size=32,       intermediate_size=37,       layer_norm_eps=1e-05,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       hidden_act='''gelu''',       projection_dim=512,       )
					SCREAMING_SNAKE_CASE	:    List[Any]          =			CLIPTextModel(_lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

					SCREAMING_SNAKE_CASE	:    str          =			{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''safety_checker''': None,
					    '''feature_extractor''': None,
					}
					return components


	def 					lowercase__	(self			:			int,       __UpperCAmelCase			:			Tuple,       __UpperCAmelCase			:			Optional[Any]=0   )  ->      Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    List[Any]          =			floats_tensor((1, 3, 32, 32),       rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			image.cpu().permute(0,       2,       3,       1   )[0]
					SCREAMING_SNAKE_CASE	:    Any          =			Image.fromarray(np.uinta(_lowerCamelCase   )   ).convert('''RGB'''   ).resize((64, 64)   )
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			Image.fromarray(np.uinta(image + 4   )   ).convert('''RGB'''   ).resize((64, 64)   )
					if str(_lowerCamelCase   ).startswith('''mps'''   ):
									SCREAMING_SNAKE_CASE	:    Any          =			torch.manual_seed(_lowerCamelCase   )
					else:
									SCREAMING_SNAKE_CASE	:    Optional[Any]          =			torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			{
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''image''': init_image,
					    '''mask_image''': mask_image,
					    '''generator''': generator,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 6.0,
					    '''output_type''': '''numpy''',
					}
					return inputs


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    int          =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			self.get_dummy_components()
					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			StableDiffusionInpaintPipeline(**_lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    List[str]          =			sd_pipe.to(_lowerCamelCase   )
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

					SCREAMING_SNAKE_CASE	:    Any          =			self.get_dummy_inputs(_lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Any          =			sd_pipe(**_lowerCamelCase   ).images
					SCREAMING_SNAKE_CASE	:    str          =			image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]   )

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






	def 					lowercase__	(self			:			Tuple   )  ->      int:

					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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


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

					"""simple docstring"""

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


	def 					lowercase__	(self			:			Optional[int]   )  ->      List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Dict          =			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-inpaint/init_image.png'''   )
					SCREAMING_SNAKE_CASE	:    List[Any]          =			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
					SCREAMING_SNAKE_CASE	:    str          =			load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
					    '''/yellow_cat_sitting_on_a_park_bench.npy'''   )

					SCREAMING_SNAKE_CASE	:    Dict          =			'''stabilityai/stable-diffusion-2-inpainting'''
					SCREAMING_SNAKE_CASE	:    List[Any]          =			StableDiffusionInpaintPipeline.from_pretrained(_lowerCamelCase,       safety_checker=_lowerCamelCase   )
					pipe.to(_lowerCamelCase   )
					pipe.set_progress_bar_config(disable=_lowerCamelCase   )
					pipe.enable_attention_slicing()

					SCREAMING_SNAKE_CASE	:    Optional[int]          =			'''Face of a yellow cat, high resolution, sitting on a park bench'''

					SCREAMING_SNAKE_CASE	:    Optional[int]          =			torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE	:    List[Any]          =			pipe(
					    prompt=_lowerCamelCase,       image=_lowerCamelCase,       mask_image=_lowerCamelCase,       generator=_lowerCamelCase,       output_type='''np''',       )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			output.images[0]

					assert image.shape == (512, 512, 3)
					assert np.abs(expected_image - image   ).max() < 9e-3


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    str          =			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-inpaint/init_image.png'''   )
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
					    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''   )

					SCREAMING_SNAKE_CASE	:    List[Any]          =			'''stabilityai/stable-diffusion-2-inpainting'''
					SCREAMING_SNAKE_CASE	:    List[str]          =			StableDiffusionInpaintPipeline.from_pretrained(
					    _lowerCamelCase,       torch_dtype=torch.floataa,       safety_checker=_lowerCamelCase,       )
					pipe.to(_lowerCamelCase   )
					pipe.set_progress_bar_config(disable=_lowerCamelCase   )
					pipe.enable_attention_slicing()

					SCREAMING_SNAKE_CASE	:    int          =			'''Face of a yellow cat, high resolution, sitting on a park bench'''

					SCREAMING_SNAKE_CASE	:    Tuple          =			torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE	:    int          =			pipe(
					    prompt=_lowerCamelCase,       image=_lowerCamelCase,       mask_image=_lowerCamelCase,       generator=_lowerCamelCase,       output_type='''np''',       )
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			output.images[0]

					assert image.shape == (512, 512, 3)
					assert np.abs(expected_image - image   ).max() < 5e-1






	def 					lowercase__	(self			:			str   )  ->      Optional[Any]:

					"""simple docstring"""

					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					SCREAMING_SNAKE_CASE	:    List[str]          =			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-inpaint/init_image.png'''   )
					SCREAMING_SNAKE_CASE	:    str          =			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )

					SCREAMING_SNAKE_CASE	:    int          =			'''stabilityai/stable-diffusion-2-inpainting'''
					SCREAMING_SNAKE_CASE	:    Optional[int]          =			PNDMScheduler.from_pretrained(_lowerCamelCase,       subfolder='''scheduler'''   )
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			StableDiffusionInpaintPipeline.from_pretrained(
					    _lowerCamelCase,       safety_checker=_lowerCamelCase,       scheduler=_lowerCamelCase,       torch_dtype=torch.floataa,       )
					pipe.to(_lowerCamelCase   )
					pipe.set_progress_bar_config(disable=_lowerCamelCase   )
					pipe.enable_attention_slicing(1   )
					pipe.enable_sequential_cpu_offload()

					SCREAMING_SNAKE_CASE	:    Tuple          =			'''Face of a yellow cat, high resolution, sitting on a park bench'''

					SCREAMING_SNAKE_CASE	:    int          =			torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			pipe(
					    prompt=_lowerCamelCase,       image=_lowerCamelCase,       mask_image=_lowerCamelCase,       generator=_lowerCamelCase,       num_inference_steps=2,       output_type='''np''',       )

					SCREAMING_SNAKE_CASE	:    Any          =			torch.cuda.max_memory_allocated()
					# make sure that less than 2.65 GB is allocated
					assert mem_bytes < 2.65 * 10**9








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		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





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

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			Adafactor(
           params=[w]   ,    lr=1e-2   ,    eps=(1e-30, 1e-3)   ,    clip_threshold=1.0   ,    decay_rate=-0.8   ,    betaa=_lowerCamelCase   ,    weight_decay=0.0   ,    relative_step=_lowerCamelCase   ,    scale_parameter=_lowerCamelCase   ,    warmup_init=_lowerCamelCase   ,    )
       for _ in range(1000       ):
             a		:str									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             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 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase   ,    msg=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		: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():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

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







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





