



"""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,
    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      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Optional[int] 							=     StableDiffusionInstructPixaPixPipeline
		lowerCAmelCase_       :	Union[str, Any] 							=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
		lowerCAmelCase_       :	Dict 							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCAmelCase_       :	Any 							=     IMAGE_TO_IMAGE_IMAGE_PARAMS
		lowerCAmelCase_       :	Optional[int] 							=     IMAGE_TO_IMAGE_IMAGE_PARAMS



		def 					A_		( self  :    str				)			->						List[Any]:






									'''simple docstring'''

									torch.manual_seed(0				)
									A         =					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      ,							)
									A         =					PNDMScheduler(skip_prk_steps=snake_case				)
									torch.manual_seed(0				)
									A         =					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				)
									A         =					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=1_000      ,							)
									A         =					CLIPTextModel(snake_case				)
									A         =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

									A         =					{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components



		def 					A_		( self  :    str      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[int]=0				)			->						Optional[int]:






									'''simple docstring'''

									A         =					floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(snake_case				)				).to(snake_case				)
									A         =					image.cpu().permute(0      ,							2      ,							3      ,							1				)[0]
									A         =					Image.fromarray(np.uinta(snake_case				)				).convert('RGB'				)
									if str(snake_case				).startswith('mps'				):
																A         =					torch.manual_seed(snake_case				)
									else:
																A         =					torch.Generator(device=snake_case				).manual_seed(snake_case				)
									A         =					{
									    '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 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

									A         =					self.get_dummy_inputs(snake_case				)
									A         =					sd_pipe(**snake_case				).images
									A         =					image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									A         =					np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]				)

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



		def 					A_		( self  :    Any				)			->						Any:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

									A         =					self.get_dummy_inputs(snake_case				)
									A         =					'french fries'
									A         =					sd_pipe(**snake_case      ,							negative_prompt=snake_case				)
									A         =					output.images
									A         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A         =					np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]				)

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



		def 					A_		( self  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

									A         =					self.get_dummy_inputs(snake_case				)
									A         =					[inputs['prompt']] * 2

									A         =					np.array(inputs['image']				).astype(np.floataa				) / 255.0
									A         =					torch.from_numpy(snake_case				).unsqueeze(0				).to(snake_case				)
									A         =					image / 2 + 0.5
									A         =					image.permute(0      ,							3      ,							1      ,							2				)
									A         =					image.repeat(2      ,							1      ,							1      ,							1				)

									A         =					sd_pipe(**snake_case				).images
									A         =					image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									A         =					np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]				)

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



		def 					A_		( self  :    Optional[int]				)			->						str:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					EulerAncestralDiscreteScheduler(
									    beta_start=0.00085      ,							beta_end=0.012      ,							beta_schedule='scaled_linear'				)
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

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

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

									assert image.shape == (1, 32, 32, 3)
									A         =					np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]				)

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



		def 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

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



		def 					A_		( self  :    Any				)			->						Any:






									'''simple docstring'''

									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					VaeImageProcessor(do_resize=snake_case      ,							do_normalize=snake_case				)
									A         =					pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)

									A         =					pipe(**self.get_dummy_inputs_by_type(snake_case      ,							input_image_type='pt'				)				)[0]

									A         =					components['vae']
									A         =					self.get_dummy_inputs_by_type(snake_case      ,							input_image_type='pt'				)

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

									A         =					pipe(**snake_case				)[0]

									A         =					np.abs(out - out_latents_inputs				).max()
									self.assertLess(snake_case      ,							1E-4      ,							'passing latents as image input generate different result from passing image'				)






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



		def 					A_		( self  :    Optional[int]				)			->						str:






									'''simple docstring'''

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



		def 					A_		( self  :    Any      ,							snake_case  :    List[Any]=0				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					torch.manual_seed(snake_case				)
									A         =					load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'				)
									A         =					{
									    '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 					A_		( self  :    Union[str, Any]				)			->						Tuple:






									'''simple docstring'''

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case				)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 512, 3)
									A         =					np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]				)

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



		def 					A_		( self  :    List[str]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case				)
									A         =					LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 512, 3)
									A         =					np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]				)

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



		def 					A_		( self  :    List[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case				)
									A         =					DDIMScheduler.from_config(pipe.scheduler.config				)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 512, 3)
									A         =					np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]				)

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



		def 					A_		( self  :    List[Any]				)			->						Any:






									'''simple docstring'''

									A         =					0

									def callback_fn(snake_case  :    int      ,							snake_case  :    int      ,							snake_case  :    torch.FloatTensor				) -> None:
																A         =					True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																							A         =					latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							A         =					latents[0, -3:, -3:, -1]
																							A         =					np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]				)

																							assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2
																elif step == 2:
																							A         =					latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							A         =					latents[0, -3:, -3:, -1]
																							A         =					np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]				)

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

									A         =					False

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

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



		def 					A_		( self  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

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

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

									A         =					self.get_inputs()
									A         =					pipe(**snake_case				)

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



		def 					A_		( self  :    int				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									A         =					inputs['image'].resize((504, 504)				)

									A         =					'timbrooks/instruct-pix2pix'
									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    snake_case      ,							safety_checker=snake_case      ,							)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

									A         =					pipe(**snake_case				)
									A         =					output.images[0]

									A         =					image[255:258, 383:386, -1]

									assert image.shape == (504, 504, 3)
									A         =					np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]				)

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








"""simple docstring"""







from typing import TYPE_CHECKING

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


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



