'''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 				a__(					lowerCAmelCase__ ,						lowerCAmelCase__ ,						lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[Any]      =       StableDiffusionInstructPixaPixPipeline
			UpperCAmelCase_			:     Optional[int]      =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
			UpperCAmelCase_			:     Optional[int]      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCAmelCase_			:     Optional[Any]      =       IMAGE_TO_IMAGE_IMAGE_PARAMS
			UpperCAmelCase_			:     List[str]      =       IMAGE_TO_IMAGE_IMAGE_PARAMS



			def 							a_       (   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=__lowerCAmelCase)
					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-0_5       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1000       ,    )
					lowerCAmelCase   					=  CLIPTextModel(__lowerCAmelCase)
					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=0):




					"""simple docstring"""


					lowerCAmelCase   					=  floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase   					=  image.cpu().permute(0       ,    2       ,    3       ,    1)[0]
					lowerCAmelCase   					=  Image.fromarray(np.uinta(__lowerCAmelCase)).convert("""RGB""")
					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase   					=  torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
					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 							a_       (   self):




					"""simple docstring"""


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

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase)
					lowerCAmelCase   					=  sd_pipe(**__lowerCAmelCase).images
					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  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):




					"""simple docstring"""


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

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

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  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):




					"""simple docstring"""


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

					lowerCAmelCase   					=  self.get_dummy_inputs(__lowerCAmelCase)
					lowerCAmelCase   					=  [inputs["""prompt"""]] * 2

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

					lowerCAmelCase   					=  sd_pipe(**__lowerCAmelCase).images
					lowerCAmelCase   					=  image[-1, -3:, -3:, -1]

					assert image.shape == (2, 32, 32, 3)
					lowerCAmelCase   					=  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):




					"""simple docstring"""


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

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

					lowerCAmelCase   					=  [round(__lowerCAmelCase       ,    4) for x in image_slice.flatten().tolist()]
					print(""",""".join([str(__lowerCAmelCase) for x in slice]))

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  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):




					"""simple docstring"""


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







			def 							a_       (   self):




					"""simple docstring"""


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

					lowerCAmelCase   					=  pipe(**self.get_dummy_inputs_by_type(__lowerCAmelCase       ,    input_image_type="""pt"""))[0]

					lowerCAmelCase   					=  components["""vae"""]
					lowerCAmelCase   					=  self.get_dummy_inputs_by_type(__lowerCAmelCase       ,    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(**__lowerCAmelCase)[0]

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







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


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase=0):




					"""simple docstring"""


					lowerCAmelCase   					=  torch.manual_seed(__lowerCAmelCase)
					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 							a_       (   self):




					"""simple docstring"""


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

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

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




					"""simple docstring"""


					lowerCAmelCase   					=  StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    """timbrooks/instruct-pix2pix"""       ,    safety_checker=__lowerCAmelCase)
					lowerCAmelCase   					=  LMSDiscreteScheduler.from_config(pipe.scheduler.config)
					pipe.to(__lowerCAmelCase)
					pipe.set_progress_bar_config(disable=__lowerCAmelCase)
					pipe.enable_attention_slicing()

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

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




					"""simple docstring"""


					lowerCAmelCase   					=  StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    """timbrooks/instruct-pix2pix"""       ,    safety_checker=__lowerCAmelCase)
					lowerCAmelCase   					=  DDIMScheduler.from_config(pipe.scheduler.config)
					pipe.to(__lowerCAmelCase)
					pipe.set_progress_bar_config(disable=__lowerCAmelCase)
					pipe.enable_attention_slicing()

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

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




					"""simple docstring"""


					lowerCAmelCase   					=  0

					def callback_fn(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase) -> 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([-0.2463, -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:
									lowerCAmelCase   					=  latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									lowerCAmelCase   					=  latents[0, -3:, -3:, -1]
									lowerCAmelCase   					=  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

					lowerCAmelCase   					=  False

					lowerCAmelCase   					=  StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    """timbrooks/instruct-pix2pix"""       ,    safety_checker=__lowerCAmelCase       ,    torch_dtype=torch.floataa)
					lowerCAmelCase   					=  pipe.to(__lowerCAmelCase)
					pipe.set_progress_bar_config(disable=__lowerCAmelCase)
					pipe.enable_attention_slicing()

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



			def 							a_       (   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=__lowerCAmelCase       ,    torch_dtype=torch.floataa)
					lowerCAmelCase   					=  pipe.to(__lowerCAmelCase)
					pipe.set_progress_bar_config(disable=__lowerCAmelCase)
					pipe.enable_attention_slicing(1)
					pipe.enable_sequential_cpu_offload()

					lowerCAmelCase   					=  self.get_inputs()
					lowerCAmelCase   					=  pipe(**__lowerCAmelCase)

					lowerCAmelCase   					=  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):




					"""simple docstring"""


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

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

					lowerCAmelCase   					=  pipe(**__lowerCAmelCase)
					lowerCAmelCase   					=  output.images[0]

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

					assert image.shape == (504, 504, 3)
					lowerCAmelCase   					=  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'''




import math

import flax.linen as nn
import jax.numpy as jnp






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




