


'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor





class 							__UpperCamelCase				(		unittest.TestCase					):




	def __init__(   self						,				__a						,				__a=7						,				__a=3						,				__a=18						,				__a=30						,				__a=400						,				__a=True						,				__a=None						,				__a=True						,				__a=None						,				__a=True						,				__a=[0.48145466, 0.4578275, 0.40821073]						,				__a=[0.26862954, 0.26130258, 0.27577711]						,				__a=True						,				):

						'''simple docstring'''





						__a			:       List[str]					    =  size if size is not None else {'height': 224, 'width': 224}
						__a			:       int					    =  crop_size if crop_size is not None else {'height': 18, 'width': 18}
						__a			:       List[Any]					    =  parent
						__a			:       Any					    =  batch_size
						__a			:       Optional[Any]					    =  num_channels
						__a			:       List[Any]					    =  image_size
						__a			:       int					    =  min_resolution
						__a			:       Dict					    =  max_resolution
						__a			:       Optional[Any]					    =  do_resize
						__a			:       Tuple					    =  size
						__a			:       str					    =  do_center_crop
						__a			:       Union[str, Any]					    =  crop_size
						__a			:       Optional[int]					    =  do_normalize
						__a			:       Tuple					    =  image_mean
						__a			:       Any					    =  image_std
						__a			:       int					    =  do_convert_rgb




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}





	def       __UpperCAmelCase							(   self						,				__a=False						,				__a=False						,				__a=False							):

						'''simple docstring'''





						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
											__a			:       int					    =  []
											for i in range(self.batch_size							):
																image_inputs.append(
																    np.random.randint(
																        255						,				size=(self.num_channels, self.max_resolution, self.max_resolution)						,				dtype=np.uinta							)							)
						else:
											__a			:       Union[str, Any]					    =  []
											for i in range(self.batch_size							):
																__a    ,				__a			:       List[Any]					    =  np.random.choice(np.arange(self.min_resolution						,				self.max_resolution							)						,				2							)
																image_inputs.append(np.random.randint(255						,				size=(self.num_channels, width, height)						,				dtype=np.uinta							)							)

						if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											__a			:       int					    =  [Image.fromarray(np.moveaxis(__a						,				0						,				-1							)							) for x in image_inputs]

						if torchify:
											__a			:       Optional[int]					    =  [torch.from_numpy(__a							) for x in image_inputs]

						return image_inputs





@require_torch
@require_vision
class 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    ChineseCLIPImageProcessor if is_vision_available() else None




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  ChineseCLIPImageProcessingTester(self						,				do_center_crop=__a							)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__a						,				'do_resize'							)							)
						self.assertTrue(hasattr(__a						,				'size'							)							)
						self.assertTrue(hasattr(__a						,				'do_center_crop'							)							)
						self.assertTrue(hasattr(__a						,				'center_crop'							)							)
						self.assertTrue(hasattr(__a						,				'do_normalize'							)							)
						self.assertTrue(hasattr(__a						,				'image_mean'							)							)
						self.assertTrue(hasattr(__a						,				'image_std'							)							)
						self.assertTrue(hasattr(__a						,				'do_convert_rgb'							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size						,				{'height': 224, 'width': 224}							)
						self.assertEqual(image_processor.crop_size						,				{'height': 18, 'width': 18}							)

						__a			:       Union[str, Any]					    =  self.image_processing_class.from_dict(self.image_processor_dict						,				size=42						,				crop_size=84							)
						self.assertEqual(image_processor.size						,				{'shortest_edge': 42}							)
						self.assertEqual(image_processor.crop_size						,				{'height': 84, 'width': 84}							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						__a			:       List[str]					    =  self.image_processor_tester.prepare_inputs(equal_resolution=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,				Image.Image							)

						# Test not batched input
						__a			:       Optional[int]					    =  image_processing(image_inputs[0]						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)

						# Test batched
						__a			:       List[str]					    =  image_processing(__a						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						__a			:       str					    =  self.image_processor_tester.prepare_inputs(equal_resolution=__a						,				numpify=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,				np.ndarray							)

						# Test not batched input
						__a			:       str					    =  image_processing(image_inputs[0]						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)

						# Test batched
						__a			:       List[str]					    =  image_processing(__a						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						__a			:       str					    =  self.image_processor_tester.prepare_inputs(equal_resolution=__a						,				torchify=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,				torch.Tensor							)

						# Test not batched input
						__a			:       Dict					    =  image_processing(image_inputs[0]						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)

						# Test batched
						__a			:       Any					    =  image_processing(__a						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)





@require_torch
@require_vision
class 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    ChineseCLIPImageProcessor if is_vision_available() else None




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  ChineseCLIPImageProcessingTester(self						,				num_channels=4						,				do_center_crop=__a							)
						__a			:       Optional[Any]					    =  3




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__a						,				'do_resize'							)							)
						self.assertTrue(hasattr(__a						,				'size'							)							)
						self.assertTrue(hasattr(__a						,				'do_center_crop'							)							)
						self.assertTrue(hasattr(__a						,				'center_crop'							)							)
						self.assertTrue(hasattr(__a						,				'do_normalize'							)							)
						self.assertTrue(hasattr(__a						,				'image_mean'							)							)
						self.assertTrue(hasattr(__a						,				'image_std'							)							)
						self.assertTrue(hasattr(__a						,				'do_convert_rgb'							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						__a			:       List[str]					    =  self.image_processor_tester.prepare_inputs(equal_resolution=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,				Image.Image							)

						# Test not batched input
						__a			:       Optional[Any]					    =  image_processing(image_inputs[0]						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)

						# Test batched
						__a			:       List[str]					    =  image_processing(__a						,				return_tensors='pt'							).pixel_values
						self.assertEqual(
						    encoded_images.shape						,				(
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )						,				)







'''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 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    StableDiffusionInpaintPipeline
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	A_				=    frozenset(
	    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	A_				=    frozenset([]					)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       int					    =  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=__a						,				)
						__a			:       str					    =  PNDMScheduler(skip_prk_steps=__a							)
						torch.manual_seed(0							)
						__a			:       Union[str, Any]					    =  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							)
						__a			:       List[str]					    =  CLIPTextConfig(
						    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1E-0_5						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='gelu'						,				projection_dim=512						,				)
						__a			:       Dict					    =  CLIPTextModel(__a							)
						__a			:       Union[str, Any]					    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a			:       Union[str, Any]					    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components




	def       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       List[Any]					    =  image.cpu().permute(0						,				2						,				3						,				1							)[0]
						__a			:       Tuple					    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							).resize((64, 64)							)
						__a			:       Tuple					    =  Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((64, 64)							)
						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       str					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    '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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a			:       str					    =  self.get_dummy_components()
						__a			:       Union[str, Any]					    =  StableDiffusionInpaintPipeline(**__a							)
						__a			:       List[Any]					    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a			:       List[Any]					    =  self.get_dummy_inputs(__a							)
						__a			:       Dict					    =  sd_pipe(**__a							).images
						__a			:       Optional[Any]					    =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__a			:       List[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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

						__a			:       Optional[int]					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Optional[int]					    =  StableDiffusionInpaintPipeline.from_pretrained(__a						,				safety_checker=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Dict					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       int					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       Dict					    =  output.images[0]

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       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'							)

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

						__a			:       Union[str, Any]					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

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

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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

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

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

						__a			:       str					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       str					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				num_inference_steps=2						,				output_type='np'						,				)

						__a			:       List[str]					    =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9



