


'''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 _lowerCamelCase			(		__a						,	__a						,	__a						,	unittest.TestCase				):



		'''simple docstring'''







		A_      :     Optional[int]        =							StableDiffusionInpaintPipeline
		A_      :     int        =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		A_      :     int        =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		A_      :     Dict        =							frozenset(
		    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		A_      :     Tuple        =							frozenset([]				)







		def   __lowerCAmelCase						(   self						:		Any     )    ->					Any:
			torch.manual_seed(0     )
			__magic_name__			:       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=_UpperCamelCase    ,	)
			__magic_name__			:       Any	      =	PNDMScheduler(skip_prk_steps=_UpperCamelCase     )
			torch.manual_seed(0     )
			__magic_name__			:       Dict	      =	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     )
			__magic_name__			:       Any	      =	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    ,	)
			__magic_name__			:       int	      =	CLIPTextModel(_UpperCamelCase     )
			__magic_name__			:       Tuple	      =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

			__magic_name__			:       Optional[int]	      =	{
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'safety_checker': None,
			    'feature_extractor': None,
			}
			return components







		def   __lowerCAmelCase						(   self						:		Union[str, Any]    ,	_A						:		List[Any]    ,	_A						:		int=0     )    ->					Tuple:
			# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
			__magic_name__			:       int	      =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(_UpperCamelCase     )     ).to(_UpperCamelCase     )
			__magic_name__			:       Any	      =	image.cpu().permute(0    ,	2    ,	3    ,	1     )[0]
			__magic_name__			:       Optional[int]	      =	Image.fromarray(np.uinta(_UpperCamelCase     )     ).convert('RGB'     ).resize((64, 64)     )
			__magic_name__			:       str	      =	Image.fromarray(np.uinta(image + 4     )     ).convert('RGB'     ).resize((64, 64)     )
			if str(_UpperCamelCase     ).startswith('mps'     ):
				__magic_name__			:       str	      =	torch.manual_seed(_UpperCamelCase     )
			else:
				__magic_name__			:       Optional[int]	      =	torch.Generator(device=_UpperCamelCase     ).manual_seed(_UpperCamelCase     )
			__magic_name__			:       List[str]	      =	{
			    '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   __lowerCAmelCase						(   self						:		Tuple     )    ->					Union[str, Any]:
			__magic_name__			:       Any	      =	'cpu'  # ensure determinism for the device-dependent torch.Generator
			__magic_name__			:       List[str]	      =	self.get_dummy_components()
			__magic_name__			:       Dict	      =	StableDiffusionInpaintPipeline(**_UpperCamelCase     )
			__magic_name__			:       int	      =	sd_pipe.to(_UpperCamelCase     )
			sd_pipe.set_progress_bar_config(disable=_UpperCamelCase     )

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

			assert image.shape == (1, 64, 64, 3)
			__magic_name__			:       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   __lowerCAmelCase						(   self						:		List[str]     )    ->					Optional[Any]:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







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



		'''simple docstring'''







		def   __lowerCAmelCase						(   self						:		Union[str, Any]     )    ->					Tuple:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def   __lowerCAmelCase						(   self						:		List[Any]     )    ->					int:
			__magic_name__			:       List[str]	      =	load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/sd2-inpaint/init_image.png'     )
			__magic_name__			:       Tuple	      =	load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'     )
			__magic_name__			:       int	      =	load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
			    '/yellow_cat_sitting_on_a_park_bench.npy'     )

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

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

			__magic_name__			:       Dict	      =	torch.manual_seed(0     )
			__magic_name__			:       List[str]	      =	pipe(
			    prompt=_UpperCamelCase    ,	image=_UpperCamelCase    ,	mask_image=_UpperCamelCase    ,	generator=_UpperCamelCase    ,	output_type='np'    ,	)
			__magic_name__			:       List[str]	      =	output.images[0]

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







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

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

			__magic_name__			:       List[str]	      =	'Face of a yellow cat, high resolution, sitting on a park bench'

			__magic_name__			:       str	      =	torch.manual_seed(0     )
			__magic_name__			:       Any	      =	pipe(
			    prompt=_UpperCamelCase    ,	image=_UpperCamelCase    ,	mask_image=_UpperCamelCase    ,	generator=_UpperCamelCase    ,	output_type='np'    ,	)
			__magic_name__			:       Any	      =	output.images[0]

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







		def   __lowerCAmelCase						(   self						:		List[Any]     )    ->					Dict:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

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

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

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

			__magic_name__			:       List[str]	      =	torch.manual_seed(0     )
			__magic_name__			:       List[str]	      =	pipe(
			    prompt=_UpperCamelCase    ,	image=_UpperCamelCase    ,	mask_image=_UpperCamelCase    ,	generator=_UpperCamelCase    ,	num_inference_steps=2    ,	output_type='np'    ,	)

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

import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMSNForImageClassification, ViTMSNModel
			from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=30							,							_UpperCamelCase=2							,							_UpperCamelCase=3							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=32							,							_UpperCamelCase=5							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=10							,							_UpperCamelCase=0.02							,							_UpperCamelCase=None							,							)							-> List[Any]:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      image_size
							lowerCAmelCase_         =      patch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scope

							# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase_         =      (image_size // patch_size) ** 2
							lowerCAmelCase_         =      num_patches + 1




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

							lowerCAmelCase_         =      None
							if self.use_labels:
										lowerCAmelCase_         =      ids_tensor([self.batch_size]							,							self.type_sequence_label_size	)

							lowerCAmelCase_         =      self.get_config()

							return config, pixel_values, labels




				def 	__a		(   self	)							-> Optional[Any]:
							return ViTMSNConfig(
							    image_size=self.image_size							,							patch_size=self.patch_size							,							num_channels=self.num_channels							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							initializer_range=self.initializer_range							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      ViTMSNModel(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.type_sequence_label_size
							lowerCAmelCase_         =      ViTMSNForImageClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							labels=_UpperCamelCase	)
							print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"	)
							print("Labels: {labels}"	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)	)

							# test greyscale images
							lowerCAmelCase_         =      1
							lowerCAmelCase_         =      ViTMSNForImageClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()

							lowerCAmelCase_         =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)	)




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      config_and_inputs
							lowerCAmelCase_         =      {"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				_lowercase     						=(
				    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      ViTMSNModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							has_text_modality=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> Optional[int]:
							self.config_tester.run_common_tests()




				@unittest.skip(reason="ViTMSN does not use inputs_embeds"	)
				def 	__a		(   self	)							-> str:
							pass




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCAmelCase_         =      model_class(_UpperCamelCase	)
										self.assertIsInstance(model.get_input_embeddings()							,							(nn.Module)	)
										lowerCAmelCase_         =      model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(_UpperCamelCase							,							nn.Linear	)	)




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCAmelCase_         =      model_class(_UpperCamelCase	)
										lowerCAmelCase_         =      inspect.signature(model.forward	)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase_         =      [*signature.parameters.keys()]

										lowerCAmelCase_         =      ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_         =      ViTMSNModel.from_pretrained(_UpperCamelCase	)
										self.assertIsNotNone(_UpperCamelCase	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
			return image





@require_torch
@require_vision
class       _lowerCAmelCase   (      unittest.TestCase			):




				@cached_property
				def 	__a		(   self	)							-> List[str]:
							return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"	) if is_vision_available() else None




				@slow
				def 	__a		(   self	)							-> int:
							torch.manual_seed(2	)
							lowerCAmelCase_         =      ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"	).to(_UpperCamelCase	)

							lowerCAmelCase_         =      self.default_image_processor
							lowerCAmelCase_         =      prepare_img()
							lowerCAmelCase_         =      image_processor(images=_UpperCamelCase							,							return_tensors="pt"	).to(_UpperCamelCase	)

							# forward pass
							with torch.no_grad():
										lowerCAmelCase_         =      model(**_UpperCamelCase	)

							# verify the logits
							lowerCAmelCase_         =      torch.Size((1, 1_000)	)
							self.assertEqual(outputs.logits.shape							,							_UpperCamelCase	)

							lowerCAmelCase_         =      torch.tensor([-0.0803, -0.4454, -0.2375]	).to(_UpperCamelCase	)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]							,							_UpperCamelCase							,							atol=1e-4	)	)



