



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 		lowercase						(			snake_case__			,   snake_case__			,   snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Tuple		       =							StableDiffusionInstructPixaPixPipeline
	a__				: Any		       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
	a__				: List[str]		       =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	a__				: Optional[Any]		       =							IMAGE_TO_IMAGE_IMAGE_PARAMS
	a__				: Dict		       =							IMAGE_TO_IMAGE_IMAGE_PARAMS




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Union[str, Any]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       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						,  )
							UpperCAmelCase_=       PNDMScheduler(skip_prk_steps=__UpperCAmelCase					)
							torch.manual_seed(0					)
							UpperCAmelCase_=       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_=       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=1_000						,  )
							UpperCAmelCase_=       CLIPTextModel(__UpperCAmelCase					)
							UpperCAmelCase_=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						List[str]=0					)      ->			Tuple:
							UpperCAmelCase_=       floats_tensor((1, 3, 32, 32)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					)[0]
							UpperCAmelCase_=       Image.fromarray(np.uinta(__UpperCAmelCase					)					).convert("""RGB"""					)
							if str(__UpperCAmelCase					).startswith("""mps"""					):
													UpperCAmelCase_=       torch.manual_seed(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
							UpperCAmelCase_=       {
							    """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   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			int:
							UpperCAmelCase_=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_=       self.get_dummy_components()
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase					)
							UpperCAmelCase_=       sd_pipe.to(__UpperCAmelCase					)
							sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       self.get_dummy_inputs(__UpperCAmelCase					)
							UpperCAmelCase_=       sd_pipe(**__UpperCAmelCase					).images
							UpperCAmelCase_=       image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							UpperCAmelCase_=       np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_=       self.get_dummy_components()
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase					)
							UpperCAmelCase_=       sd_pipe.to(__UpperCAmelCase					)
							sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

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

							assert image.shape == (1, 32, 32, 3)
							UpperCAmelCase_=       np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Dict:
							UpperCAmelCase_=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_=       self.get_dummy_components()
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase					)
							UpperCAmelCase_=       sd_pipe.to(__UpperCAmelCase					)
							sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       self.get_dummy_inputs(__UpperCAmelCase					)
							UpperCAmelCase_=       [inputs["""prompt"""]] * 2

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

							UpperCAmelCase_=       sd_pipe(**__UpperCAmelCase					).images
							UpperCAmelCase_=       image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							UpperCAmelCase_=       np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Tuple:
							UpperCAmelCase_=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_=       self.get_dummy_components()
							UpperCAmelCase_=       EulerAncestralDiscreteScheduler(
							    beta_start=0.00_085						,  beta_end=0.012						,  beta_schedule="""scaled_linear"""					)
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase					)
							UpperCAmelCase_=       sd_pipe.to(__UpperCAmelCase					)
							sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

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

							UpperCAmelCase_=       [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)
							UpperCAmelCase_=       np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       self.get_dummy_components()
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase					)
							UpperCAmelCase_=       VaeImageProcessor(do_resize=__UpperCAmelCase						,  do_normalize=__UpperCAmelCase					)
							UpperCAmelCase_=       pipe.to(__UpperCAmelCase					)
							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       pipe(**self.get_dummy_inputs_by_type(__UpperCAmelCase						,  input_image_type="""pt"""					)					)[0]

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

							UpperCAmelCase_=       pipe(**__UpperCAmelCase					)[0]

							UpperCAmelCase_=       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 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						str=0					)      ->			Tuple:
							UpperCAmelCase_=       torch.manual_seed(__UpperCAmelCase					)
							UpperCAmelCase_=       load_image(
							    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""					)
							UpperCAmelCase_=       {
							    """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   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""						,  safety_checker=__UpperCAmelCase					)
							pipe.to(__UpperCAmelCase					)
							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)
							pipe.enable_attention_slicing()

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

							assert image.shape == (1, 512, 512, 3)
							UpperCAmelCase_=       np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			str:
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""						,  safety_checker=__UpperCAmelCase					)
							UpperCAmelCase_=       LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
							pipe.to(__UpperCAmelCase					)
							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)
							pipe.enable_attention_slicing()

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

							assert image.shape == (1, 512, 512, 3)
							UpperCAmelCase_=       np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Optional[Any]:
							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""						,  safety_checker=__UpperCAmelCase					)
							UpperCAmelCase_=       DDIMScheduler.from_config(pipe.scheduler.config					)
							pipe.to(__UpperCAmelCase					)
							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)
							pipe.enable_attention_slicing()

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

							assert image.shape == (1, 512, 512, 3)
							UpperCAmelCase_=       np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			str:
							UpperCAmelCase_=       0

							def callback_fn(__UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						torch.FloatTensor					) -> None:
													UpperCAmelCase_=       True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																			UpperCAmelCase_=       latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			UpperCAmelCase_=       latents[0, -3:, -3:, -1]
																			UpperCAmelCase_=       np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]					)

																			assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
													elif step == 2:
																			UpperCAmelCase_=       latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			UpperCAmelCase_=       latents[0, -3:, -3:, -1]
																			UpperCAmelCase_=       np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]					)

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

							UpperCAmelCase_=       False

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Tuple:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							UpperCAmelCase_=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""						,  safety_checker=__UpperCAmelCase						,  torch_dtype=torch.floataa					)
							UpperCAmelCase_=       pipe.to(__UpperCAmelCase					)
							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)
							pipe.enable_attention_slicing(1					)
							pipe.enable_sequential_cpu_offload()

							UpperCAmelCase_=       self.get_inputs()
							UpperCAmelCase_=       pipe(**__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCAmelCase_=       inputs["""image"""].resize((504, 504)					)

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

							UpperCAmelCase_=       pipe(**__UpperCAmelCase					)
							UpperCAmelCase_=       output.images[0]

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

							assert image.shape == (504, 504, 3)
							UpperCAmelCase_=       np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]					)

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









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




