

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):
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					StableDiffusionInstructPixaPixPipeline
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
						_SCREAMING_SNAKE_CASE	:       List[str]   =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_SCREAMING_SNAKE_CASE	:       List[str]   =					IMAGE_TO_IMAGE_IMAGE_PARAMS
						_SCREAMING_SNAKE_CASE	:       Tuple   =					IMAGE_TO_IMAGE_IMAGE_PARAMS






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												torch.manual_seed(0   )
												lowerCAmelCase__    						=						UNetaDConditionModel(
												    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						sample_size=32     ,						in_channels=8     ,						out_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,						cross_attention_dim=32     ,						)
												lowerCAmelCase__    						=						PNDMScheduler(skip_prk_steps=_UpperCamelCase   )
												torch.manual_seed(0   )
												lowerCAmelCase__    						=						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   )
												lowerCAmelCase__    						=						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=10_00     ,						)
												lowerCAmelCase__    						=						CLIPTextModel(_UpperCamelCase   )
												lowerCAmelCase__    						=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

												lowerCAmelCase__    						=						{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """safety_checker""": None,
												    """feature_extractor""": None,
												}
												return components






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=0   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						floats_tensor((1, 3, 32, 32)     ,						rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
												lowerCAmelCase__    						=						image.cpu().permute(0     ,						2     ,						3     ,						1   )[0]
												lowerCAmelCase__    						=						Image.fromarray(np.uinta(_UpperCamelCase   )   ).convert('RGB'   )
												if str(_UpperCamelCase   ).startswith('mps'   ):
																		lowerCAmelCase__    						=						torch.manual_seed(_UpperCamelCase   )
												else:
																		lowerCAmelCase__    						=						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
												lowerCAmelCase__    						=						{
												    """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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						sd_pipe.to(_UpperCamelCase   )
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						sd_pipe(**_UpperCamelCase   ).images
												lowerCAmelCase__    						=						image[0, -3:, -3:, -1]
												assert image.shape == (1, 32, 32, 3)
												lowerCAmelCase__    						=						np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						sd_pipe.to(_UpperCamelCase   )
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						"""french fries"""
												lowerCAmelCase__    						=						sd_pipe(**_UpperCamelCase     ,						negative_prompt=_UpperCamelCase   )
												lowerCAmelCase__    						=						output.images
												lowerCAmelCase__    						=						image[0, -3:, -3:, -1]

												assert image.shape == (1, 32, 32, 3)
												lowerCAmelCase__    						=						np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						sd_pipe.to(_UpperCamelCase   )
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						[inputs["""prompt"""]] * 2

												lowerCAmelCase__    						=						np.array(inputs['image']   ).astype(np.floataa   ) / 2_55.0
												lowerCAmelCase__    						=						torch.from_numpy(_UpperCamelCase   ).unsqueeze(0   ).to(_UpperCamelCase   )
												lowerCAmelCase__    						=						image / 2 + 0.5
												lowerCAmelCase__    						=						image.permute(0     ,						3     ,						1     ,						2   )
												lowerCAmelCase__    						=						image.repeat(2     ,						1     ,						1     ,						1   )

												lowerCAmelCase__    						=						sd_pipe(**_UpperCamelCase   ).images
												lowerCAmelCase__    						=						image[-1, -3:, -3:, -1]

												assert image.shape == (2, 32, 32, 3)
												lowerCAmelCase__    						=						np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						EulerAncestralDiscreteScheduler(
												    beta_start=0.0_00_85     ,						beta_end=0.0_12     ,						beta_schedule='scaled_linear'   )
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						sd_pipe.to(_UpperCamelCase   )
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						sd_pipe(**_UpperCamelCase   ).images
												lowerCAmelCase__    						=						image[0, -3:, -3:, -1]

												lowerCAmelCase__    						=						[round(_UpperCamelCase     ,						4   ) for x in image_slice.flatten().tolist()]
												print(','.join([str(_UpperCamelCase   ) for x in slice]   )   )

												assert image.shape == (1, 32, 32, 3)
												lowerCAmelCase__    						=						np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						VaeImageProcessor(do_resize=_UpperCamelCase     ,						do_normalize=_UpperCamelCase   )
												lowerCAmelCase__    						=						pipe.to(_UpperCamelCase   )
												pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						pipe(**self.get_dummy_inputs_by_type(_UpperCamelCase     ,						input_image_type='pt'   )   )[0]

												lowerCAmelCase__    						=						components["""vae"""]
												lowerCAmelCase__    						=						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():
																								lowerCAmelCase__    						=						vae.encode(inputs[image_param]   ).latent_dist.mode()

												lowerCAmelCase__    						=						pipe(**_UpperCamelCase   )[0]

												lowerCAmelCase__    						=						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):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=0   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						torch.manual_seed(_UpperCamelCase   )
												lowerCAmelCase__    						=						load_image(
												    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'   )
												lowerCAmelCase__    						=						{
												    """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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'     ,						safety_checker=_UpperCamelCase   )
												pipe.to(_UpperCamelCase   )
												pipe.set_progress_bar_config(disable=_UpperCamelCase   )
												pipe.enable_attention_slicing()

												lowerCAmelCase__    						=						self.get_inputs()
												lowerCAmelCase__    						=						pipe(**_UpperCamelCase   ).images
												lowerCAmelCase__    						=						image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_12, 5_12, 3)
												lowerCAmelCase__    						=						np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'     ,						safety_checker=_UpperCamelCase   )
												lowerCAmelCase__    						=						LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
												pipe.to(_UpperCamelCase   )
												pipe.set_progress_bar_config(disable=_UpperCamelCase   )
												pipe.enable_attention_slicing()

												lowerCAmelCase__    						=						self.get_inputs()
												lowerCAmelCase__    						=						pipe(**_UpperCamelCase   ).images
												lowerCAmelCase__    						=						image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_12, 5_12, 3)
												lowerCAmelCase__    						=						np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'     ,						safety_checker=_UpperCamelCase   )
												lowerCAmelCase__    						=						DDIMScheduler.from_config(pipe.scheduler.config   )
												pipe.to(_UpperCamelCase   )
												pipe.set_progress_bar_config(disable=_UpperCamelCase   )
												pipe.enable_attention_slicing()

												lowerCAmelCase__    						=						self.get_inputs()
												lowerCAmelCase__    						=						pipe(**_UpperCamelCase   ).images
												lowerCAmelCase__    						=						image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_12, 5_12, 3)
												lowerCAmelCase__    						=						np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]   )

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						0

												def callback_fn(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ) -> None:
																		lowerCAmelCase__    						=						True
																		nonlocal number_of_steps
																		number_of_steps += 1
																		if step == 1:
																								lowerCAmelCase__    						=						latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 64)
																								lowerCAmelCase__    						=						latents[0, -3:, -3:, -1]
																								lowerCAmelCase__    						=						np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]   )

																								assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
																		elif step == 2:
																								lowerCAmelCase__    						=						latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 64)
																								lowerCAmelCase__    						=						latents[0, -3:, -3:, -1]
																								lowerCAmelCase__    						=						np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]   )

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

												lowerCAmelCase__    						=						False

												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'     ,						safety_checker=_UpperCamelCase     ,						torch_dtype=torch.floataa   )
												lowerCAmelCase__    						=						pipe.to(_UpperCamelCase   )
												pipe.set_progress_bar_config(disable=_UpperCamelCase   )
												pipe.enable_attention_slicing()

												lowerCAmelCase__    						=						self.get_inputs()
												pipe(**_UpperCamelCase     ,						callback=_UpperCamelCase     ,						callback_steps=1   )
												assert callback_fn.has_been_called
												assert number_of_steps == 3






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'     ,						safety_checker=_UpperCamelCase     ,						torch_dtype=torch.floataa   )
												lowerCAmelCase__    						=						pipe.to(_UpperCamelCase   )
												pipe.set_progress_bar_config(disable=_UpperCamelCase   )
												pipe.enable_attention_slicing(1   )
												pipe.enable_sequential_cpu_offload()

												lowerCAmelCase__    						=						self.get_inputs()
												lowerCAmelCase__    						=						pipe(**_UpperCamelCase   )

												lowerCAmelCase__    						=						torch.cuda.max_memory_allocated()
												# make sure that less than 2.2 GB is allocated
												assert mem_bytes < 2.2 * 10**9







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_inputs()
												# resize to resolution that is divisible by 8 but not 16 or 32
												lowerCAmelCase__    						=						inputs["""image"""].resize((5_04, 5_04)   )

												lowerCAmelCase__    						=						"""timbrooks/instruct-pix2pix"""
												lowerCAmelCase__    						=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    _UpperCamelCase     ,						safety_checker=_UpperCamelCase     ,						)
												pipe.to(_UpperCamelCase   )
												pipe.set_progress_bar_config(disable=_UpperCamelCase   )
												pipe.enable_attention_slicing()

												lowerCAmelCase__    						=						pipe(**_UpperCamelCase   )
												lowerCAmelCase__    						=						output.images[0]

												lowerCAmelCase__    						=						image[2_55:2_58, 3_83:3_86, -1]

												assert image.shape == (5_04, 5_04, 3)
												lowerCAmelCase__    						=						np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]   )

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case    :    Tuple         =     {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     ["""GLPNFeatureExtractor"""]
					__snake_case    :    Optional[Any]         =     ["""GLPNImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     [
					    """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GLPNForDepthEstimation""",
					    """GLPNLayer""",
					    """GLPNModel""",
					    """GLPNPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





