






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__		:							List[str] 				=				"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=8       ) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCAmelCase__      :  str				      =			width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						):
          super().__init__()

          self.register_modules(
              unet=_lowerCAmelCase	,						scheduler=_lowerCAmelCase	,						movq=_lowerCAmelCase	,						)
          UpperCAmelCase__      :  Optional[Any]				      =			2 ** (len(self.movq.config.block_out_channels		) - 1)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          if latents is None:
               UpperCAmelCase__      :  Any				      =			randn_tensor(_lowerCAmelCase	,						generator=_lowerCAmelCase	,						device=_lowerCAmelCase	,						dtype=_lowerCAmelCase		)
          else:
               if latents.shape != shape:
                    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
               UpperCAmelCase__      :  str				      =			latents.to(_lowerCAmelCase		)

          UpperCAmelCase__      :  Dict				      =			latents * scheduler.init_noise_sigma
          return latents




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0		):
          if is_accelerate_available():
               from accelerate import cpu_offload
          else:
               raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

          UpperCAmelCase__      :  Any				      =			torch.device(f"cuda:{gpu_id}"		)

          UpperCAmelCase__      :  int				      =			[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                    cpu_offload(_lowerCAmelCase	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0		):
          if is_accelerate_available() and is_accelerate_version(""">="""	,						"""0.17.0.dev0"""		):
               from accelerate import cpu_offload_with_hook
          else:
               raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""		)

          UpperCAmelCase__      :  str				      =			torch.device(f"cuda:{gpu_id}"		)

          if self.device.type != "cpu":
               self.to("""cpu"""	,						silence_dtype_warnings=_lowerCAmelCase		)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          UpperCAmelCase__      :  Any				      =			None
          for cpu_offloaded_model in [self.unet, self.movq]:
               UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			cpu_offload_with_hook(_lowerCAmelCase	,						_lowerCAmelCase	,						prev_module_hook=_lowerCAmelCase		)

          # We'll offload the last model manually.
          UpperCAmelCase__      :  str				      =			hook




     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def   __UpperCAmelCase  (				self		):
          if not hasattr(self.unet	,						"""_hf_hook"""		):
               return self.device
          for module in self.unet.modules():
               if (
                   hasattr(_lowerCAmelCase	,						"""_hf_hook"""		)
                   and hasattr(module._hf_hook	,						"""execution_device"""		)
                   and module._hf_hook.execution_device is not None
               ):
                    return torch.device(module._hf_hook.execution_device		)
          return self.device




     @torch.no_grad()
     @replace_example_docstring(_lowerCAmelCase		)
     def __call__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = 512	,						_lowerCAmelCase = 512	,						_lowerCAmelCase = 100	,						_lowerCAmelCase = 4.0	,						_lowerCAmelCase = 1	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = "pil"	,						_lowerCAmelCase = True	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			self._execution_device

          UpperCAmelCase__      :  Dict				      =			guidance_scale > 1.0

          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  Union[str, Any]				      =			torch.cat(_lowerCAmelCase	,						dim=0		)
          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  List[str]				      =			torch.cat(_lowerCAmelCase	,						dim=0		)
          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  List[str]				      =			torch.cat(_lowerCAmelCase	,						dim=0		)

          UpperCAmelCase__      :  Optional[int]				      =			image_embeds.shape[0] * num_images_per_prompt

          if do_classifier_free_guidance:
               UpperCAmelCase__      :  str				      =			image_embeds.repeat_interleave(_lowerCAmelCase	,						dim=0		)
               UpperCAmelCase__      :  Tuple				      =			negative_image_embeds.repeat_interleave(_lowerCAmelCase	,						dim=0		)
               UpperCAmelCase__      :  Dict				      =			hint.repeat_interleave(_lowerCAmelCase	,						dim=0		)

          UpperCAmelCase__      :  Tuple				      =			torch.cat([negative_image_embeds, image_embeds]	,						dim=0		).to(dtype=self.unet.dtype	,						device=_lowerCAmelCase		)
          UpperCAmelCase__      :  Any				      =			torch.cat([hint, hint]	,						dim=0		).to(dtype=self.unet.dtype	,						device=_lowerCAmelCase		)
          self.scheduler.set_timesteps(_lowerCAmelCase	,						device=_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			self.scheduler.timesteps

          UpperCAmelCase__      :  List[Any]				      =			self.movq.config.latent_channels

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[str]				      =			downscale_height_and_width(_lowerCAmelCase	,						_lowerCAmelCase	,						self.movq_scale_factor		)

          # create initial latent
          UpperCAmelCase__      :  Tuple				      =			self.prepare_latents(
              (batch_size, num_channels_latents, height, width)	,						image_embeds.dtype	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						self.scheduler	,						)

          for i, t in enumerate(self.progress_bar(_lowerCAmelCase		)		):
               # expand the latents if we are doing classifier free guidance
               UpperCAmelCase__      :  int				      =			torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

               UpperCAmelCase__      :  int				      =			{"""image_embeds""": image_embeds, """hint""": hint}
               UpperCAmelCase__      :  str				      =			self.unet(
                   sample=_lowerCAmelCase	,						timestep=_lowerCAmelCase	,						encoder_hidden_states=_lowerCAmelCase	,						added_cond_kwargs=_lowerCAmelCase	,						return_dict=_lowerCAmelCase	,						)[0]

               if do_classifier_free_guidance:
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			noise_pred.split(latents.shape[1]	,						dim=1		)
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			noise_pred.chunk(2		)
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			variance_pred.chunk(2		)
                    UpperCAmelCase__      :  Optional[Any]				      =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    UpperCAmelCase__      :  int				      =			torch.cat([noise_pred, variance_pred_text]	,						dim=1		)

               if not (
                   hasattr(self.scheduler.config	,						"""variance_type"""		)
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[int]				      =			noise_pred.split(latents.shape[1]	,						dim=1		)

               # compute the previous noisy sample x_t -> x_t-1
               UpperCAmelCase__      :  Any				      =			self.scheduler.step(
                   _lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						generator=_lowerCAmelCase	,						)[0]
          # post-processing
          UpperCAmelCase__      :  Dict				      =			self.movq.decode(_lowerCAmelCase	,						force_not_quantize=_lowerCAmelCase		)["""sample"""]

          if output_type not in ["pt", "np", "pil"]:
               raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"		)

          if output_type in ["np", "pil"]:
               UpperCAmelCase__      :  int				      =			image * 0.5 + 0.5
               UpperCAmelCase__      :  List[str]				      =			image.clamp(0	,						1		)
               UpperCAmelCase__      :  Tuple				      =			image.cpu().permute(0	,						2	,						3	,						1		).float().numpy()

          if output_type == "pil":
               UpperCAmelCase__      :  Tuple				      =			self.numpy_to_pil(_lowerCAmelCase		)

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=_lowerCAmelCase		)













import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class 						UpperCamelCase_  (  snake_case__							):
					_a      :						Union[str, Any]						    =					(DPMSolverSDEScheduler,)
					_a      :						List[Any]						    =					1_0



					def   __a     (		self     :     Any       ,      **lowerCamelCase     :     str						):
							lowerCamelCase_	:     Any 						=      {
							    'num_train_timesteps': 11_00,
							    'beta_start': 0.0_001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'noise_sampler_seed': 0,
							}

							config.update(**lowerCamelCase						)
							return config



					def   __a     (		self     :     Tuple						):
							for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase						)



					def   __a     (		self     :     int						):
							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,      [0.0_002, 0.002, 0.02]						):
									self.check_over_configs(beta_start=lowerCamelCase       ,      beta_end=lowerCamelCase						)



					def   __a     (		self     :     Any						):
							for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     int 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     str 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Optional[Any] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_model()
							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     Union[str, Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     str 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Tuple 						=      output.prev_sample

							lowerCamelCase_	:     List[str] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Optional[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.47_821_044_921_875						) < 1E-2
									assert abs(result_mean.item() - 0.2_178_705_964_565_277						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_352_111_816_406						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_906_892_299_652						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     Any 						=      self.get_scheduler_config(prediction_type='v_prediction'						)
							lowerCamelCase_	:     Tuple 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Any 						=      self.dummy_model()
							lowerCamelCase_	:     Tuple 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     List[Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     Dict 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Union[str, Any] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Dict 						=      output.prev_sample

							lowerCamelCase_	:     Optional[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     List[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 124.77_149_200_439_453						) < 1E-2
									assert abs(result_mean.item() - 0.16_226_289_014_816_284						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 128.1_663_360_595_703						) < 1E-2
									assert abs(result_mean.item() - 0.16_688_326_001_167_297						) < 1E-3
							else:
									assert abs(result_sum.item() - 119.8_487_548_828_125						) < 1E-2
									assert abs(result_mean.item() - 0.1_560_530_662_536_621						) < 1E-3



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     List[str] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      self.dummy_model()
							lowerCamelCase_	:     Dict 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Tuple 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     int 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Optional[int] 						=      output.prev_sample

							lowerCamelCase_	:     List[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     str 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.46_957_397_460_938						) < 1E-2
									assert abs(result_mean.item() - 0.21_805_934_607_982_635						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_353_637_695_312						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_908_382_415_771						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3





					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Dict 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Union[str, Any] 						=      scheduler_class(**lowerCamelCase       ,      use_karras_sigmas=lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     int 						=      self.dummy_model()
							lowerCamelCase_	:     str 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma
							lowerCamelCase_	:     Dict 						=      sample.to(lowerCamelCase						)

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Optional[Any] 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Any 						=      output.prev_sample

							lowerCamelCase_	:     Optional[int] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Union[str, Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 176.66_974_135_742_188						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 177.63_653_564_453_125						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							else:
									assert abs(result_sum.item() - 170.3_135_223_388_672						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
