






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,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							SCREAMING_SNAKE_CASE__		(       _UpperCamelCase				,     _UpperCamelCase				,     _UpperCamelCase				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	StableDiffusionInstructPixaPixPipeline
					a_			=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					a_			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a_			=	IMAGE_TO_IMAGE_IMAGE_PARAMS
					a_			=	IMAGE_TO_IMAGE_IMAGE_PARAMS



					def  _lowercase  (      self  :						str ):
						torch.manual_seed(0 )
						snake_case__				:   Optional[Any]     =						UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)							,	layers_per_block=2							,	sample_size=3_2							,	in_channels=8							,	out_channels=4							,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,	cross_attention_dim=3_2							,	)
						snake_case__				:   List[str]     =						PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
						torch.manual_seed(0 )
						snake_case__				:   int     =						AutoencoderKL(
						    block_out_channels=[3_2, 6_4]							,	in_channels=3							,	out_channels=3							,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,	latent_channels=4							,	)
						torch.manual_seed(0 )
						snake_case__				:   str     =						CLIPTextConfig(
						    bos_token_id=0							,	eos_token_id=2							,	hidden_size=3_2							,	intermediate_size=3_7							,	layer_norm_eps=1e-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1_0_0_0							,	)
						snake_case__				:   Optional[int]     =						CLIPTextModel(_UpperCAmelCase )
						snake_case__				:   str     =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

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



					def  _lowercase  (      self  :						Tuple							,	__A  :						str							,	__A  :						int=0 ):
						snake_case__				:   Any     =						floats_tensor((1, 3, 3_2, 3_2)							,	rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
						snake_case__				:   Optional[Any]     =						image.cpu().permute(0							,	2							,	3							,	1 )[0]
						snake_case__				:   Tuple     =						Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("RGB" )
						if str(_UpperCAmelCase ).startswith("mps" ):
							snake_case__				:   Union[str, Any]     =						torch.manual_seed(_UpperCAmelCase )
						else:
							snake_case__				:   Union[str, Any]     =						torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
						snake_case__				:   Union[str, Any]     =						{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': image,
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 6.0,
						    '''image_guidance_scale''': 1,
						    '''output_type''': '''numpy''',
						}
						return inputs



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   List[Any]     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						snake_case__				:   Dict     =						self.get_dummy_components()
						snake_case__				:   Any     =						StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
						snake_case__				:   int     =						sd_pipe.to(_UpperCAmelCase )
						sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

						snake_case__				:   Union[str, Any]     =						self.get_dummy_inputs(_UpperCAmelCase )
						snake_case__				:   str     =						sd_pipe(**_UpperCAmelCase ).images
						snake_case__				:   List[Any]     =						image[0, -3:, -3:, -1]
						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__				:   Optional[int]     =						np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Dict     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						snake_case__				:   Optional[Any]     =						self.get_dummy_components()
						snake_case__				:   Dict     =						StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
						snake_case__				:   List[Any]     =						sd_pipe.to(_UpperCAmelCase )
						sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

						snake_case__				:   Dict     =						self.get_dummy_inputs(_UpperCAmelCase )
						snake_case__				:   Union[str, Any]     =						'''french fries'''
						snake_case__				:   str     =						sd_pipe(**_UpperCAmelCase							,	negative_prompt=_UpperCAmelCase )
						snake_case__				:   Dict     =						output.images
						snake_case__				:   Optional[int]     =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__				:   Tuple     =						np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



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

						snake_case__				:   Union[str, Any]     =						self.get_dummy_inputs(_UpperCAmelCase )
						snake_case__				:   List[str]     =						[inputs['''prompt''']] * 2

						snake_case__				:   Optional[Any]     =						np.array(inputs["image"] ).astype(np.floataa ) / 2_5_5.0
						snake_case__				:   Optional[Any]     =						torch.from_numpy(_UpperCAmelCase ).unsqueeze(0 ).to(_UpperCAmelCase )
						snake_case__				:   Tuple     =						image / 2 + 0.5
						snake_case__				:   str     =						image.permute(0							,	3							,	1							,	2 )
						snake_case__				:   List[str]     =						image.repeat(2							,	1							,	1							,	1 )

						snake_case__				:   int     =						sd_pipe(**_UpperCAmelCase ).images
						snake_case__				:   Optional[Any]     =						image[-1, -3:, -3:, -1]

						assert image.shape == (2, 3_2, 3_2, 3)
						snake_case__				:   List[str]     =						np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Dict     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						snake_case__				:   List[str]     =						self.get_dummy_components()
						snake_case__				:   Union[str, Any]     =						EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0_0_8_5							,	beta_end=0.0_1_2							,	beta_schedule="scaled_linear" )
						snake_case__				:   str     =						StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
						snake_case__				:   List[str]     =						sd_pipe.to(_UpperCAmelCase )
						sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

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

						snake_case__				:   Optional[int]     =						[round(_UpperCAmelCase							,	4 ) for x in image_slice.flatten().tolist()]
						print(",".join([str(_UpperCAmelCase ) for x in slice] ) )

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__				:   int     =						np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3



					def  _lowercase  (      self  :						Optional[Any] ):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Any     =						self.get_dummy_components()
						snake_case__				:   Optional[Any]     =						StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase )
						snake_case__				:   Any     =						VaeImageProcessor(do_resize=_UpperCAmelCase							,	do_normalize=_UpperCAmelCase )
						snake_case__				:   Dict     =						pipe.to(_UpperCAmelCase )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase )

						snake_case__				:   str     =						pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase							,	input_image_type="pt" ) )[0]

						snake_case__				:   List[Any]     =						components['''vae''']
						snake_case__				:   List[Any]     =						self.get_dummy_inputs_by_type(_UpperCAmelCase							,	input_image_type="pt" )

						for image_param in self.image_latents_params:
							if image_param in inputs.keys():
								snake_case__				:   Optional[Any]     =						vae.encode(inputs[image_param] ).latent_dist.mode()

						snake_case__				:   str     =						pipe(**_UpperCAmelCase )[0]

						snake_case__				:   Union[str, Any]     =						np.abs(out - out_latents_inputs ).max()
						self.assertLess(_UpperCAmelCase							,	1e-4							,	"passing latents as image input generate different result from passing image" )



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





					"""simple docstring"""






					def  _lowercase  (      self  :						Optional[int] ):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def  _lowercase  (      self  :						int							,	__A  :						str=0 ):
						snake_case__				:   List[str]     =						torch.manual_seed(_UpperCAmelCase )
						snake_case__				:   str     =						load_image(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" )
						snake_case__				:   List[str]     =						{
						    '''prompt''': '''turn him into a cyborg''',
						    '''image''': image,
						    '''generator''': generator,
						    '''num_inference_steps''': 3,
						    '''guidance_scale''': 7.5,
						    '''image_guidance_scale''': 1.0,
						    '''output_type''': '''numpy''',
						}
						return inputs



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   List[str]     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,	safety_checker=_UpperCAmelCase )
						pipe.to(_UpperCAmelCase )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase )
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__				:   Tuple     =						np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )

						assert np.abs(expected_slice - image_slice ).max() < 1e-3



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Tuple     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,	safety_checker=_UpperCAmelCase )
						snake_case__				:   Optional[int]     =						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
						pipe.to(_UpperCAmelCase )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase )
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__				:   Tuple     =						np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )

						assert np.abs(expected_slice - image_slice ).max() < 1e-3



					def  _lowercase  (      self  :						int ):
						snake_case__				:   List[Any]     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,	safety_checker=_UpperCAmelCase )
						snake_case__				:   Optional[int]     =						DDIMScheduler.from_config(pipe.scheduler.config )
						pipe.to(_UpperCAmelCase )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase )
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__				:   Any     =						np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )

						assert np.abs(expected_slice - image_slice ).max() < 1e-3



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Dict     =						0

						def callback_fn(__A  :						Any							,	__A  :						Dict							,	__A  :						Optional[Any] ) -> None:
							snake_case__				:   Optional[Any]     =						True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
								snake_case__				:   Optional[int]     =						latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__				:   int     =						latents[0, -3:, -3:, -1]
								snake_case__				:   int     =						np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )

								assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
							elif step == 2:
								snake_case__				:   Union[str, Any]     =						latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__				:   List[str]     =						latents[0, -3:, -3:, -1]
								snake_case__				:   Tuple     =						np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )

								assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2

						snake_case__				:   Union[str, Any]     =						False

						snake_case__				:   Optional[int]     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,	safety_checker=_UpperCAmelCase							,	torch_dtype=torch.floataa )
						snake_case__				:   Optional[int]     =						pipe.to(_UpperCAmelCase )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase )
						pipe.enable_attention_slicing()

						snake_case__				:   str     =						self.get_inputs()
						pipe(**_UpperCAmelCase							,	callback=_UpperCAmelCase							,	callback_steps=1 )
						assert callback_fn.has_been_called
						assert number_of_steps == 3



					def  _lowercase  (      self  :						Dict ):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						snake_case__				:   Any     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,	safety_checker=_UpperCAmelCase							,	torch_dtype=torch.floataa )
						snake_case__				:   Optional[int]     =						pipe.to(_UpperCAmelCase )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase )
						pipe.enable_attention_slicing(1 )
						pipe.enable_sequential_cpu_offload()

						snake_case__				:   List[Any]     =						self.get_inputs()
						snake_case__				:   Tuple     =						pipe(**_UpperCAmelCase )

						snake_case__				:   List[str]     =						torch.cuda.max_memory_allocated()
						# make sure that less than 2.2 GB is allocated
						assert mem_bytes < 2.2 * 1_0**9






					def  _lowercase  (      self  :						str ):
						snake_case__				:   List[Any]     =						self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						snake_case__				:   str     =						inputs['''image'''].resize((5_0_4, 5_0_4) )

						snake_case__				:   Tuple     =						'''timbrooks/instruct-pix2pix'''
						snake_case__				:   Any     =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    _UpperCAmelCase							,	safety_checker=_UpperCAmelCase							,	)
						pipe.to(_UpperCAmelCase )
						pipe.set_progress_bar_config(disable=_UpperCAmelCase )
						pipe.enable_attention_slicing()

						snake_case__				:   List[Any]     =						pipe(**_UpperCAmelCase )
						snake_case__				:   int     =						output.images[0]

						snake_case__				:   Optional[int]     =						image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert image.shape == (5_0_4, 5_0_4, 3)
						snake_case__				:   Union[str, Any]     =						np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



