






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 SwinaSRImageProcessor
	from transformers.image_transforms import get_image_size

class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   List[Any]=7					,    __lowerCamelCase							:   Union[str, Any]=3					,    __lowerCamelCase							:   Union[str, Any]=18					,    __lowerCamelCase							:   Tuple=30					,    __lowerCamelCase							:   str=400					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Any=1 / 255					,    __lowerCamelCase							:   Tuple=True					,    __lowerCamelCase							:   List[str]=8					,    ):
		UpperCamelCase	:Tuple		=      parent
		UpperCamelCase	:Any		=      batch_size
		UpperCamelCase	:Any		=      num_channels
		UpperCamelCase	:int		=      image_size
		UpperCamelCase	:List[Any]		=      min_resolution
		UpperCamelCase	:Dict		=      max_resolution
		UpperCamelCase	:Optional[Any]		=      do_rescale
		UpperCamelCase	:Optional[int]		=      rescale_factor
		UpperCamelCase	:Tuple		=      do_pad
		UpperCamelCase	:str		=      pad_size





	def 		_A					(						self							:   Optional[int]      ):
		return {
		    "do_rescale": self.do_rescale,
		    "rescale_factor": self.rescale_factor,
		    "do_pad": self.do_pad,
		    "pad_size": self.pad_size,
		}





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   List[Any]=False					,    __lowerCamelCase							:   Optional[int]=False					,    __lowerCamelCase							:   List[str]=False      ):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		if equal_resolution:
			UpperCamelCase	:List[Any]		=      []
			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:
			UpperCamelCase	:str		=      []
			for i in range(self.batch_size      ):
				UpperCamelCase	: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
			UpperCamelCase	:str		=      [Image.fromarray(np.moveaxis(_UpperCAmelCase					,    0					,    -1      )      ) for x in image_inputs]

		if torchify:
			UpperCamelCase	:List[str]		=      [torch.from_numpy(_UpperCAmelCase      ) for x in image_inputs]

		return image_inputs

@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE			(     UpperCamelCase__						,  unittest.TestCase ):
	snake_case__				:	List[str]	      =    SwinaSRImageProcessor if is_vision_available() else None





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:str		=      SwinaSRImageProcessingTester(self      )





	@property
	def 		_A					(						self							:   str      ):
		return self.image_processor_tester.prepare_image_processor_dict()





	def 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:str		=      self.image_processing_class(**self.image_processor_dict      )
		self.assertTrue(hasattr(_UpperCAmelCase					,    """do_rescale"""      )      )
		self.assertTrue(hasattr(_UpperCAmelCase					,    """rescale_factor"""      )      )
		self.assertTrue(hasattr(_UpperCAmelCase					,    """do_pad"""      )      )
		self.assertTrue(hasattr(_UpperCAmelCase					,    """pad_size"""      )      )





	def 		_A					(						self							:   str      ):
		pass





	def 		_A					(						self							:   str					,    __lowerCamelCase							:   Any      ):
		UpperCamelCase	:List[Any]		=      get_image_size(_UpperCAmelCase      )
		UpperCamelCase	:List[str]		=      self.image_processor_tester.pad_size

		UpperCamelCase	:Union[str, Any]		=      (old_height // size + 1) * size - old_height
		UpperCamelCase	:Any		=      (old_width // size + 1) * size - old_width
		return old_height + pad_height, old_width + pad_width





	def 		_A					(						self							:   str      ):
		# Initialize image_processor
		UpperCamelCase	:List[Any]		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PIL images
		UpperCamelCase	:List[Any]		=      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase      )
		for image in image_inputs:
			self.assertIsInstance(_UpperCAmelCase					,    Image.Image      )

		# Test not batched input
		UpperCamelCase	:str		=      image_processor(image_inputs[0]					,    return_tensors="""pt"""      ).pixel_values
		UpperCamelCase	:List[Any]		=      self.calculate_expected_size(np.array(image_inputs[0]      )      )
		self.assertEqual(
		    encoded_images.shape					,    (
		        1,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,    )





	def 		_A					(						self							:   Tuple      ):
		# Initialize image_processor
		UpperCamelCase	:str		=      self.image_processing_class(**self.image_processor_dict      )
		# create random numpy tensors
		UpperCamelCase	:Tuple		=      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase					,    numpify=_UpperCAmelCase      )
		for image in image_inputs:
			self.assertIsInstance(_UpperCAmelCase					,    np.ndarray      )

		# Test not batched input
		UpperCamelCase	:Optional[int]		=      image_processor(image_inputs[0]					,    return_tensors="""pt"""      ).pixel_values
		UpperCamelCase	:Any		=      self.calculate_expected_size(image_inputs[0]      )
		self.assertEqual(
		    encoded_images.shape					,    (
		        1,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,    )





	def 		_A					(						self							:   Any      ):
		# Initialize image_processor
		UpperCamelCase	:Optional[Any]		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PyTorch tensors
		UpperCamelCase	:List[str]		=      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase					,    torchify=_UpperCAmelCase      )
		for image in image_inputs:
			self.assertIsInstance(_UpperCAmelCase					,    torch.Tensor      )

		# Test not batched input
		UpperCamelCase	:List[Any]		=      image_processor(image_inputs[0]					,    return_tensors="""pt"""      ).pixel_values
		UpperCamelCase	:int		=      self.calculate_expected_size(image_inputs[0]      )
		self.assertEqual(
		    encoded_images.shape					,    (
		        1,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,    )








'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class 					__UpperCAmelCase (		UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				unittest.TestCase	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					StableDiffusionModelEditingPipeline
      SCREAMING_SNAKE_CASE				:    str				       =					TEXT_TO_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					TEXT_TO_IMAGE_BATCH_PARAMS
      SCREAMING_SNAKE_CASE				:    Dict				       =					TEXT_TO_IMAGE_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					TEXT_TO_IMAGE_IMAGE_PARAMS




      def 							lowerCamelCase     (					self						):
             torch.manual_seed(0						)
             UpperCAmelCase__	:   List[Any]												=	UNetaDConditionModel(
                 block_out_channels=(32, 64)  ,		layers_per_block=2  ,		sample_size=32  ,		in_channels=4  ,		out_channels=4  ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,		cross_attention_dim=32  ,		)
             UpperCAmelCase__	:   List[str]												=	DDIMScheduler()
             torch.manual_seed(0						)
             UpperCAmelCase__	:   str												=	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  ,		)
             torch.manual_seed(0						)
             UpperCAmelCase__	:   Optional[int]												=	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  ,		)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextModel(_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

             UpperCAmelCase__	:   List[str]												=	{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Tuple												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 # Setting height and width to None to prevent OOMs on CPU.
                 '''height''': None,
                 '''width''': None,
                 '''num_inference_steps''': 2,
                 '''guidance_scale''': 6.0,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Optional[int]												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]
             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   int												=	np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[str]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	'''french fries'''
             UpperCAmelCase__	:   Any												=	sd_pipe(**_UpperCAmelCase  ,		negative_prompt=_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	output.images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   str												=	np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   str												=	EulerAncestralDiscreteScheduler(
                 beta_start=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''						)
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

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

             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   Tuple												=	np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   str												=	self.get_dummy_components()
             UpperCAmelCase__	:   List[Any]												=	PNDMScheduler()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Tuple												=	self.get_dummy_inputs(_UpperCAmelCase						)
             # the pipeline does not expect pndm so test if it raises error.
             with self.assertRaises(_UpperCAmelCase						):
                    UpperCAmelCase__	:   List[Any]												=	sd_pipe(**_UpperCAmelCase						).images




      def 							lowerCamelCase     (					self						):
             super().test_inference_batch_single_identical(expected_max_diff=5E-3						)




      def 							lowerCamelCase     (					self						):
             super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)




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

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Dict												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   List[str]												=	StableDiffusionModelEditingPipeline.from_pretrained(_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing()

             UpperCAmelCase__	:   Union[str, Any]												=	self.get_inputs()
             UpperCAmelCase__	:   Union[str, Any]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Optional[int]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

             UpperCAmelCase__	:   List[str]												=	np.array(
                 [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

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

             # make sure image changes after editing
             pipe.edit_model('''A pack of roses'''  ,		'''A pack of blue roses'''						)

             UpperCAmelCase__	:   Optional[int]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

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




      def 							lowerCamelCase     (					self						):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase__	:   Dict												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   int												=	DDIMScheduler.from_pretrained(_UpperCAmelCase  ,		subfolder='''scheduler'''						)
             UpperCAmelCase__	:   int												=	StableDiffusionModelEditingPipeline.from_pretrained(
                 _UpperCAmelCase  ,		scheduler=_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             UpperCAmelCase__	:   Tuple												=	pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing(1						)
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase__	:   Any												=	self.get_inputs()
             UpperCAmelCase__	:   Any												=	pipe(**_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	torch.cuda.max_memory_allocated()
             # make sure that less than 4.4 GB is allocated
             assert mem_bytes < 4.4 * 10**9