


'''simple docstring'''
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,
)


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

UpperCAmelCase_			:  Union[str, Any]										=   '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'


def       _lowercase							(					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       int,					UpperCamelCase__							:       Tuple=8      ):
 __A	:		Any	  =      height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __A	:		Union[str, Any]	  =      width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   ):

  """simple docstring"""







  super().__init__()

  self.register_modules(
      unet=__lowercase   ,   scheduler=__lowercase   ,   movq=__lowercase   ,   )
  __A	:		int	  =      2 ** (len(self.movq.config.block_out_channels    ) - 1)


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if latents is None:
   __A	:		Tuple	  =      randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase   ,   dtype=__lowercase    )
  else:
   if latents.shape != shape:
    raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""    )
   __A	:		List[str]	  =      latents.to(__lowercase    )

  __A	:		Tuple	  =      latents * scheduler.init_noise_sigma
  return latents


 def 			snake_case__ (   self   ,   __lowercase=0    ):

  """simple docstring"""







  if is_accelerate_available():
   from accelerate import cpu_offload
  else:
   raise ImportError('Please install accelerate via `pip install accelerate`'    )

  __A	:		List[Any]	  =      torch.device(F"""cuda:{gpu_id}"""    )

  __A	:		Any	  =      [
      self.unet,
      self.movq,
  ]
  for cpu_offloaded_model in models:
   if cpu_offloaded_model is not None:
    cpu_offload(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self   ,   __lowercase=0    ):

  """simple docstring"""







  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.'    )

  __A	:		Optional[Any]	  =      torch.device(F"""cuda:{gpu_id}"""    )

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

  __A	:		str	  =      None
  for cpu_offloaded_model in [self.unet, self.movq]:
   __A  ,__A	:		Optional[Any]	  =      cpu_offload_with_hook(__lowercase   ,   __lowercase   ,   prev_module_hook=__lowercase    )

  # We'll offload the last model manually.
  __A	:		int	  =      hook


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 			snake_case__ (   self    ):

  """simple docstring"""







  if not hasattr(self.unet   ,   '_hf_hook'    ):
   return self.device
  for module in self.unet.modules():
   if (
       hasattr(__lowercase   ,   '_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(__lowercase    )
 def __call__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase = 512   ,   __lowercase = 512   ,   __lowercase = 100   ,   __lowercase = 4.0   ,   __lowercase = 1   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = "pil"   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A	:		Dict	  =      self._execution_device

  __A	:		Dict	  =      guidance_scale > 1.0

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		str	  =      torch.cat(__lowercase   ,   dim=0    )
  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		List[str]	  =      torch.cat(__lowercase   ,   dim=0    )
  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		str	  =      torch.cat(__lowercase   ,   dim=0    )

  __A	:		List[Any]	  =      image_embeds.shape[0] * num_images_per_prompt

  if do_classifier_free_guidance:
   __A	:		Optional[Any]	  =      image_embeds.repeat_interleave(__lowercase   ,   dim=0    )
   __A	:		Optional[Any]	  =      negative_image_embeds.repeat_interleave(__lowercase   ,   dim=0    )
   __A	:		List[Any]	  =      hint.repeat_interleave(__lowercase   ,   dim=0    )

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

  __A	:		Tuple	  =      self.movq.config.latent_channels

  __A  ,__A	:		Any	  =      downscale_height_and_width(__lowercase   ,   __lowercase   ,   self.movq_scale_factor    )

  # create initial latent
  __A	:		List[Any]	  =      self.prepare_latents(
      (batch_size, num_channels_latents, height, width)   ,   image_embeds.dtype   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   self.scheduler   ,   )

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

   __A	:		int	  =      {'image_embeds': image_embeds, 'hint': hint}
   __A	:		List[str]	  =      self.unet(
       sample=__lowercase   ,   timestep=__lowercase   ,   encoder_hidden_states=__lowercase   ,   added_cond_kwargs=__lowercase   ,   return_dict=__lowercase   ,   )[0]

   if do_classifier_free_guidance:
    __A  ,__A	:		Dict	  =      noise_pred.split(latents.shape[1]   ,   dim=1    )
    __A  ,__A	:		Optional[Any]	  =      noise_pred.chunk(2    )
    __A  ,__A	:		List[str]	  =      variance_pred.chunk(2    )
    __A	:		Tuple	  =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
    __A	:		Optional[Any]	  =      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"]
   ):
    __A  ,__A	:		str	  =      noise_pred.split(latents.shape[1]   ,   dim=1    )

   # compute the previous noisy sample x_t -> x_t-1
   __A	:		List[str]	  =      self.scheduler.step(
       __lowercase   ,   __lowercase   ,   __lowercase   ,   generator=__lowercase   ,   )[0]
  # post-processing
  __A	:		Optional[Any]	  =      self.movq.decode(__lowercase   ,   force_not_quantize=__lowercase    )['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"]:
   __A	:		Dict	  =      image * 0.5 + 0.5
   __A	:		List[str]	  =      image.clamp(0   ,   1    )
   __A	:		List[str]	  =      image.cpu().permute(0   ,   2   ,   3   ,   1    ).float().numpy()

  if output_type == "pil":
   __A	:		Union[str, Any]	  =      self.numpy_to_pil(__lowercase    )

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=__lowercase    )










'''simple docstring'''
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class     _lowerCamelCase       :




 '''simple docstring'''


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.get_dummy_input()


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  if self.block_type == "down":
   return (4, 32, 16, 16)
  elif self.block_type == "mid":
   return (4, 32, 32, 32)
  elif self.block_type == "up":
   return (4, 32, 64, 64)

  raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""    )


 def 			snake_case__ (   self   ,   __lowercase=True   ,   __lowercase=False   ,   __lowercase=False   ,   __lowercase=False   ,   ):

  """simple docstring"""







  __A	:		List[str]	  =      4
  __A	:		List[Any]	  =      32
  __A	:		Optional[Any]	  =      (32, 32)

  __A	:		List[str]	  =      torch.manual_seed(0    )
  __A	:		Union[str, Any]	  =      torch.device(__lowercase    )
  __A	:		Optional[int]	  =      (batch_size, num_channels) + sizes
  __A	:		str	  =      randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    )
  __A	:		int	  =      {'hidden_states': hidden_states}

  if include_temb:
   __A	:		Optional[Any]	  =      128
   __A	:		Any	  =      randn_tensor((batch_size, temb_channels)   ,   generator=__lowercase   ,   device=__lowercase    )

  if include_res_hidden_states_tuple:
   __A	:		Any	  =      torch.manual_seed(1    )
   __A	:		List[Any]	  =      (randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    ),)

  if include_encoder_hidden_states:
   __A	:		str	  =      floats_tensor((batch_size, 32, 32)    ).to(__lowercase    )

  if include_skip_sample:
   __A	:		List[Any]	  =      randn_tensor(((batch_size, 3) + sizes)   ,   generator=__lowercase   ,   device=__lowercase    )

  return dummy_input


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {
      'in_channels': 32,
      'out_channels': 32,
      'temb_channels': 128,
  }
  if self.block_type == "up":
   __A	:		int	  =      32

  if self.block_type == "mid":
   init_dict.pop('out_channels'    )

  __A	:		Optional[int]	  =      self.dummy_input
  return init_dict, inputs_dict


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A  ,__A	:		List[Any]	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		int	  =      self.block_class(**__lowercase    )
  unet_block.to(__lowercase    )
  unet_block.eval()

  with torch.no_grad():
   __A	:		List[Any]	  =      unet_block(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Tuple	  =      output[0]

  self.assertEqual(output.shape   ,   self.output_shape    )

  __A	:		Any	  =      output[0, -1, -3:, -3:]
  __A	:		Dict	  =      torch.tensor(__lowercase    ).to(__lowercase    )
  assert torch_all_close(output_slice.flatten()   ,   __lowercase   ,   atol=5E-3    )







 @unittest.skipIf(torch_device == 'mps'   ,   'Training is not supported in mps'    )
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		str	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		Optional[Any]	  =      self.block_class(**__lowercase    )
  model.to(__lowercase    )
  model.train()
  __A	:		Any	  =      model(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Any	  =      output[0]

  __A	:		int	  =      torch.device(__lowercase    )
  __A	:		Dict	  =      randn_tensor(output.shape   ,   device=__lowercase    )
  __A	:		Dict	  =      torch.nn.functional.mse_loss(__lowercase   ,   __lowercase    )
  loss.backward()






