

# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class  UpperCamelCase_      (	_snake_case		,  _snake_case		,  _snake_case		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   StableDiffusionControlNetInpaintPipeline
  SCREAMING_SNAKE_CASE__					:			int					    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  SCREAMING_SNAKE_CASE__					:			int					    =   frozenset({"""control_image"""}      )  # skip `image` and `mask` for now, only test for control_image
  SCREAMING_SNAKE_CASE__					:			str					    =   TEXT_TO_IMAGE_IMAGE_PARAMS



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			int:
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    UNetaDConditionModel(
          block_out_channels=(32, 64)  ,	layers_per_block=2  ,	sample_size=32  ,	in_channels=9  ,	out_channels=4  ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,	cross_attention_dim=32  ,	)
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    ControlNetModel(
          block_out_channels=(32, 64)  ,	layers_per_block=2  ,	in_channels=4  ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,	cross_attention_dim=32  ,	conditioning_embedding_out_channels=(16, 32)  ,	)
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    DDIMScheduler(
          beta_start=0.00_085  ,	beta_end=0.012  ,	beta_schedule="scaled_linear"  ,	clip_sample=A_  ,	set_alpha_to_one=A_  ,	)
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    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						)
      SCREAMING_SNAKE_CASE__         =    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  ,	)
      SCREAMING_SNAKE_CASE__         =    CLIPTextModel(A_						)
      SCREAMING_SNAKE_CASE__         =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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



  def   _UpperCamelCase     (						self     :					List[str]  ,	A_     :					List[str]  ,	A_     :					Union[str, Any]=0						)       ->			List[Any]:
      if str(A_						).startswith("mps"						):
          SCREAMING_SNAKE_CASE__         =    torch.manual_seed(A_						)
      else:
          SCREAMING_SNAKE_CASE__         =    torch.Generator(device=A_						).manual_seed(A_						)

      SCREAMING_SNAKE_CASE__         =    2
      SCREAMING_SNAKE_CASE__         =    randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,	generator=A_  ,	device=torch.device(A_						)  ,	)
      SCREAMING_SNAKE_CASE__         =    floats_tensor((1, 3, 32, 32)  ,	rng=random.Random(A_						)						).to(A_						)
      SCREAMING_SNAKE_CASE__         =    init_image.cpu().permute(0  ,	2  ,	3  ,	1						)[0]

      SCREAMING_SNAKE_CASE__         =    Image.fromarray(np.uinta(A_						)						).convert("RGB"						).resize((64, 64)						)
      SCREAMING_SNAKE_CASE__         =    Image.fromarray(np.uinta(init_image + 4						)						).convert("RGB"						).resize((64, 64)						)

      SCREAMING_SNAKE_CASE__         =    {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
          "image": image,
          "mask_image": mask_image,
          "control_image": control_image,
      }

      return inputs



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			str:
      return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3						)



  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()  ,	reason="XFormers attention is only available with CUDA and `xformers` installed"  ,	)
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Dict:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)







  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Union[str, Any]:
      self._test_inference_batch_single_identical(expected_max_diff=2E-3						)







class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   StableDiffusionControlNetInpaintPipeline
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  SCREAMING_SNAKE_CASE__					:			str					    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   frozenset([]      )



  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			List[str]:
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    UNetaDConditionModel(
          block_out_channels=(32, 64)  ,	layers_per_block=2  ,	sample_size=32  ,	in_channels=4  ,	out_channels=4  ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,	cross_attention_dim=32  ,	)
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    ControlNetModel(
          block_out_channels=(32, 64)  ,	layers_per_block=2  ,	in_channels=4  ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,	cross_attention_dim=32  ,	conditioning_embedding_out_channels=(16, 32)  ,	)
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    DDIMScheduler(
          beta_start=0.00_085  ,	beta_end=0.012  ,	beta_schedule="scaled_linear"  ,	clip_sample=A_  ,	set_alpha_to_one=A_  ,	)
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    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						)
      SCREAMING_SNAKE_CASE__         =    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  ,	)
      SCREAMING_SNAKE_CASE__         =    CLIPTextModel(A_						)
      SCREAMING_SNAKE_CASE__         =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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







class  UpperCamelCase_      (	_snake_case		,  _snake_case		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   StableDiffusionControlNetInpaintPipeline
  SCREAMING_SNAKE_CASE__					:			str					    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Tuple:
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    UNetaDConditionModel(
          block_out_channels=(32, 64)  ,	layers_per_block=2  ,	sample_size=32  ,	in_channels=9  ,	out_channels=4  ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,	cross_attention_dim=32  ,	)
      torch.manual_seed(0						)

      def init_weights(A_     :					Optional[Any]						):
          if isinstance(A_  ,	torch.nn.Convad						):
              torch.nn.init.normal(m.weight						)
              m.bias.data.fill_(1.0						)

      SCREAMING_SNAKE_CASE__         =    ControlNetModel(
          block_out_channels=(32, 64)  ,	layers_per_block=2  ,	in_channels=4  ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,	cross_attention_dim=32  ,	conditioning_embedding_out_channels=(16, 32)  ,	)
      controlneta.controlnet_down_blocks.apply(A_						)

      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    ControlNetModel(
          block_out_channels=(32, 64)  ,	layers_per_block=2  ,	in_channels=4  ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,	cross_attention_dim=32  ,	conditioning_embedding_out_channels=(16, 32)  ,	)
      controlneta.controlnet_down_blocks.apply(A_						)

      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    DDIMScheduler(
          beta_start=0.00_085  ,	beta_end=0.012  ,	beta_schedule="scaled_linear"  ,	clip_sample=A_  ,	set_alpha_to_one=A_  ,	)
      torch.manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    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						)
      SCREAMING_SNAKE_CASE__         =    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  ,	)
      SCREAMING_SNAKE_CASE__         =    CLIPTextModel(A_						)
      SCREAMING_SNAKE_CASE__         =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

      SCREAMING_SNAKE_CASE__         =    MultiControlNetModel([controlneta, controlneta]						)

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



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	A_     :					Union[str, Any]  ,	A_     :					Any=0						)       ->			List[Any]:
      if str(A_						).startswith("mps"						):
          SCREAMING_SNAKE_CASE__         =    torch.manual_seed(A_						)
      else:
          SCREAMING_SNAKE_CASE__         =    torch.Generator(device=A_						).manual_seed(A_						)

      SCREAMING_SNAKE_CASE__         =    2

      SCREAMING_SNAKE_CASE__         =    [
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,	generator=A_  ,	device=torch.device(A_						)  ,	),
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,	generator=A_  ,	device=torch.device(A_						)  ,	),
      ]
      SCREAMING_SNAKE_CASE__         =    floats_tensor((1, 3, 32, 32)  ,	rng=random.Random(A_						)						).to(A_						)
      SCREAMING_SNAKE_CASE__         =    init_image.cpu().permute(0  ,	2  ,	3  ,	1						)[0]

      SCREAMING_SNAKE_CASE__         =    Image.fromarray(np.uinta(A_						)						).convert("RGB"						).resize((64, 64)						)
      SCREAMING_SNAKE_CASE__         =    Image.fromarray(np.uinta(init_image + 4						)						).convert("RGB"						).resize((64, 64)						)

      SCREAMING_SNAKE_CASE__         =    {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
          "image": image,
          "mask_image": mask_image,
          "control_image": control_image,
      }

      return inputs



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.get_dummy_components()
      SCREAMING_SNAKE_CASE__         =    self.pipeline_class(**A_						)
      pipe.to(A_						)

      SCREAMING_SNAKE_CASE__         =    10.0
      SCREAMING_SNAKE_CASE__         =    4

      SCREAMING_SNAKE_CASE__         =    self.get_dummy_inputs(A_						)
      SCREAMING_SNAKE_CASE__         =    steps
      SCREAMING_SNAKE_CASE__         =    scale
      SCREAMING_SNAKE_CASE__         =    pipe(**A_						)[0]

      SCREAMING_SNAKE_CASE__         =    self.get_dummy_inputs(A_						)
      SCREAMING_SNAKE_CASE__         =    steps
      SCREAMING_SNAKE_CASE__         =    scale
      SCREAMING_SNAKE_CASE__         =    pipe(**A_  ,	control_guidance_start=0.1  ,	control_guidance_end=0.2						)[0]

      SCREAMING_SNAKE_CASE__         =    self.get_dummy_inputs(A_						)
      SCREAMING_SNAKE_CASE__         =    steps
      SCREAMING_SNAKE_CASE__         =    scale
      SCREAMING_SNAKE_CASE__         =    pipe(**A_  ,	control_guidance_start=[0.1, 0.3]  ,	control_guidance_end=[0.2, 0.7]						)[0]

      SCREAMING_SNAKE_CASE__         =    self.get_dummy_inputs(A_						)
      SCREAMING_SNAKE_CASE__         =    steps
      SCREAMING_SNAKE_CASE__         =    scale
      SCREAMING_SNAKE_CASE__         =    pipe(**A_  ,	control_guidance_start=0.4  ,	control_guidance_end=[0.5, 0.8]						)[0]

      # make sure that all outputs are different
      assert np.sum(np.abs(output_a - output_a						)						) > 1E-3
      assert np.sum(np.abs(output_a - output_a						)						) > 1E-3
      assert np.sum(np.abs(output_a - output_a						)						) > 1E-3



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Optional[Any]:
      return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3						)



  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()  ,	reason="XFormers attention is only available with CUDA and `xformers` installed"  ,	)
  def   _UpperCamelCase     (						self     :					Dict						)       ->			Dict:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)



  def   _UpperCamelCase     (						self     :					Any						)       ->			Optional[int]:
      self._test_inference_batch_single_identical(expected_max_diff=2E-3						)







  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    self.get_dummy_components()
      SCREAMING_SNAKE_CASE__         =    self.pipeline_class(**A_						)
      pipe.to(A_						)
      pipe.set_progress_bar_config(disable=A_						)
      with tempfile.TemporaryDirectory() as tmpdir:
          try:
              # save_pretrained is not implemented for Multi-ControlNet
              pipe.save_pretrained(A_						)
          except NotImplementedError:
              pass







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


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			List[Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"						)

      SCREAMING_SNAKE_CASE__         =    StableDiffusionControlNetInpaintPipeline.from_pretrained(
          "runwayml/stable-diffusion-inpainting"  ,	safety_checker=A_  ,	controlnet=A_						)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=A_						)

      SCREAMING_SNAKE_CASE__         =    torch.Generator(device="cpu"						).manual_seed(0						)
      SCREAMING_SNAKE_CASE__         =    load_image(
          "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"						).resize((512, 512)						)

      SCREAMING_SNAKE_CASE__         =    load_image(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
          "/stable_diffusion_inpaint/input_bench_mask.png"						).resize((512, 512)						)

      SCREAMING_SNAKE_CASE__         =    "pitch black hole"

      SCREAMING_SNAKE_CASE__         =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"						).resize((512, 512)						)

      SCREAMING_SNAKE_CASE__         =    pipe(
          A_  ,	image=A_  ,	mask_image=A_  ,	control_image=A_  ,	generator=A_  ,	output_type="np"  ,	num_inference_steps=3  ,	)

      SCREAMING_SNAKE_CASE__         =    output.images[0]

      assert image.shape == (512, 512, 3)

      SCREAMING_SNAKE_CASE__         =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"						)

      assert np.abs(expected_image - image						).max() < 9E-2







  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"						)

      SCREAMING_SNAKE_CASE__         =    StableDiffusionControlNetInpaintPipeline.from_pretrained(
          "runwayml/stable-diffusion-v1-5"  ,	safety_checker=A_  ,	controlnet=A_						)
      SCREAMING_SNAKE_CASE__         =    DDIMScheduler.from_config(pipe.scheduler.config						)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=A_						)

      SCREAMING_SNAKE_CASE__         =    torch.Generator(device="cpu"						).manual_seed(33						)

      SCREAMING_SNAKE_CASE__         =    load_image(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"						)
      SCREAMING_SNAKE_CASE__         =    init_image.resize((512, 512)						)

      SCREAMING_SNAKE_CASE__         =    load_image(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"						)
      SCREAMING_SNAKE_CASE__         =    mask_image.resize((512, 512)						)

      SCREAMING_SNAKE_CASE__         =    "a handsome man with ray-ban sunglasses"

      def make_inpaint_condition(A_     :					List[str]  ,	A_     :					Optional[Any]						):
          SCREAMING_SNAKE_CASE__         =    np.array(image.convert("RGB"						)						).astype(np.floataa						) / 255.0
          SCREAMING_SNAKE_CASE__         =    np.array(image_mask.convert("L"						)						).astype(np.floataa						) / 255.0

          assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
          SCREAMING_SNAKE_CASE__         =    -1.0  # set as masked pixel
          SCREAMING_SNAKE_CASE__         =    np.expand_dims(A_  ,	0						).transpose(0  ,	3  ,	1  ,	2						)
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						)
          return image

      SCREAMING_SNAKE_CASE__         =    make_inpaint_condition(A_  ,	A_						)

      SCREAMING_SNAKE_CASE__         =    pipe(
          A_  ,	image=A_  ,	mask_image=A_  ,	control_image=A_  ,	guidance_scale=9.0  ,	eta=1.0  ,	generator=A_  ,	num_inference_steps=20  ,	output_type="np"  ,	)
      SCREAMING_SNAKE_CASE__         =    output.images[0]

      assert image.shape == (512, 512, 3)

      SCREAMING_SNAKE_CASE__         =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"						)

      assert np.abs(expected_image - image						).max() < 9E-2





import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


A__										=      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			torch.FloatTensor
  SCREAMING_SNAKE_CASE__					:			Optional[torch.FloatTensor]					    =   None





def 				__a							(       __A			,							__A=0.999			,							__A="cosine"			,							):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__A						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__A						):
            return math.exp(t * -12.0						)

    else:
        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"						)

    SCREAMING_SNAKE_CASE__         =    []
    for i in range(__A						):
        SCREAMING_SNAKE_CASE__         =    i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE__         =    (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__A						) / alpha_bar_fn(__A						)			,							__A						)						)
    return torch.tensor(__A			,							dtype=torch.floataa						)







class  UpperCamelCase_      (	_snake_case		,  _snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   [e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   1



  @register_to_config
  def __init__(						self     :					Tuple  ,	A_     :					int = 1000  ,	A_     :					float = 0.0_001  ,	A_     :					float = 0.02  ,	A_     :					str = "linear"  ,	A_     :					Optional[Union[np.ndarray, List[float]]] = None  ,	A_     :					str = "epsilon"  ,	A_     :					str = "linspace"  ,	A_     :					int = 0  ,	)       ->			Optional[Any]:
      if trained_betas is not None:
          SCREAMING_SNAKE_CASE__         =    torch.tensor(A_  ,	dtype=torch.floataa						)
      elif beta_schedule == "linear":
          SCREAMING_SNAKE_CASE__         =    torch.linspace(A_  ,	A_  ,	A_  ,	dtype=torch.floataa						)
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          SCREAMING_SNAKE_CASE__         =    (
              torch.linspace(beta_start**0.5  ,	beta_end**0.5  ,	A_  ,	dtype=torch.floataa						) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          SCREAMING_SNAKE_CASE__         =    betas_for_alpha_bar(A_						)
      else:
          raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"						)

      SCREAMING_SNAKE_CASE__         =    1.0 - self.betas
      SCREAMING_SNAKE_CASE__         =    torch.cumprod(self.alphas  ,	dim=0						)

      SCREAMING_SNAKE_CASE__         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
      SCREAMING_SNAKE_CASE__         =    np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						)

      # setable values
      SCREAMING_SNAKE_CASE__         =    None
      SCREAMING_SNAKE_CASE__         =    np.linspace(0  ,	num_train_timesteps - 1  ,	A_  ,	dtype=A_						)[::-1].copy()
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						)
      SCREAMING_SNAKE_CASE__         =    False



  @property
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Dict:
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]						)       ->			torch.FloatTensor:
      if isinstance(A_  ,	torch.Tensor						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)
      SCREAMING_SNAKE_CASE__         =    (self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    sample / ((sigma**2 + 1) ** 0.5)
      SCREAMING_SNAKE_CASE__         =    True
      return sample



  def   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					int  ,	A_     :					Union[str, torch.device] = None						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
          SCREAMING_SNAKE_CASE__         =    np.linspace(0  ,	self.config.num_train_timesteps - 1  ,	A_  ,	dtype=A_						)[
              ::-1
          ].copy()
      elif self.config.timestep_spacing == "leading":
          SCREAMING_SNAKE_CASE__         =    self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          SCREAMING_SNAKE_CASE__         =    (np.arange(0  ,	A_						) * step_ratio).round()[::-1].copy().astype(A_						)
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          SCREAMING_SNAKE_CASE__         =    self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          SCREAMING_SNAKE_CASE__         =    (np.arange(self.config.num_train_timesteps  ,	0  ,	-step_ratio						)).round().copy().astype(A_						)
          timesteps -= 1
      else:
          raise ValueError(
              f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."						)

      SCREAMING_SNAKE_CASE__         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
      SCREAMING_SNAKE_CASE__         =    np.interp(A_  ,	np.arange(0  ,	len(A_						)						)  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)
      if str(A_						).startswith("mps"						):
          # mps does not support float64
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(A_  ,	dtype=torch.floataa						)
      else:
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]  ,	A_     :					torch.FloatTensor  ,	A_     :					Optional[torch.Generator] = None  ,	A_     :					bool = True  ,	)       ->			Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

      if (
          isinstance(A_  ,	A_						)
          or isinstance(A_  ,	torch.IntTensor						)
          or isinstance(A_  ,	torch.LongTensor						)
      ):
          raise ValueError(
              (
                  "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                  " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                  " one of the `scheduler.timesteps` as a timestep."
              )  ,	)

      if not self.is_scale_input_called:
          logger.warning(
              "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
              "See `StableDiffusionPipeline` for a usage example."						)

      if isinstance(A_  ,	torch.Tensor						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)

      SCREAMING_SNAKE_CASE__         =    (self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
          SCREAMING_SNAKE_CASE__         =    sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          SCREAMING_SNAKE_CASE__         =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
          raise NotImplementedError("prediction_type not implemented yet: sample"						)
      else:
          raise ValueError(
              f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index + 1]
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 - sigma_up**2) ** 0.5

      # 2. Convert to an ODE derivative
      SCREAMING_SNAKE_CASE__         =    (sample - pred_original_sample) / sigma

      SCREAMING_SNAKE_CASE__         =    sigma_down - sigma

      SCREAMING_SNAKE_CASE__         =    sample + derivative * dt

      SCREAMING_SNAKE_CASE__         =    model_output.device
      SCREAMING_SNAKE_CASE__         =    randn_tensor(model_output.shape  ,	dtype=model_output.dtype  ,	device=A_  ,	generator=A_						)

      SCREAMING_SNAKE_CASE__         =    prev_sample + noise * sigma_up

      if not return_dict:
          return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=A_  ,	pred_original_sample=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	)       ->			torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      SCREAMING_SNAKE_CASE__         =    self.sigmas.to(device=original_samples.device  ,	dtype=original_samples.dtype						)
      if original_samples.device.type == "mps" and torch.is_floating_point(A_						):
          # mps does not support float64
          SCREAMING_SNAKE_CASE__         =    self.timesteps.to(original_samples.device  ,	dtype=torch.floataa						)
          SCREAMING_SNAKE_CASE__         =    timesteps.to(original_samples.device  ,	dtype=torch.floataa						)
      else:
          SCREAMING_SNAKE_CASE__         =    self.timesteps.to(original_samples.device						)
          SCREAMING_SNAKE_CASE__         =    timesteps.to(original_samples.device						)

      SCREAMING_SNAKE_CASE__         =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

      SCREAMING_SNAKE_CASE__         =    sigmas[step_indices].flatten()
      while len(sigma.shape						) < len(original_samples.shape						):
          SCREAMING_SNAKE_CASE__         =    sigma.unsqueeze(-1						)

      SCREAMING_SNAKE_CASE__         =    original_samples + noise * sigma
      return noisy_samples







  def __len__(						self     :					List[Any]						)       ->			Tuple:
      return self.config.num_train_timesteps


