


'''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,
)


a_					:    Union[str, Any]          =    logging.get_logger(__name__)  # pylint: disable=invalid-name

a_					:    int          =    '''
    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 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Any				,						lowerCAmelCase__  :Optional[int]=8		)   ->  List[Any]:




     '''simple docstring'''



     _a			      =							height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     _a			      =							width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor






class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							Optional[Any]:
      super().__init__()

      self.register_modules(
          unet=UpperCamelCase_      ,				scheduler=UpperCamelCase_      ,				movq=UpperCamelCase_      ,				)
      _a			      =							2 ** (len(self.movq.config.block_out_channels						) - 1)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[int]:





      if latents is None:
           _a			      =							randn_tensor(UpperCamelCase_      ,				generator=UpperCamelCase_      ,				device=UpperCamelCase_      ,				dtype=UpperCamelCase_						)
      else:
           if latents.shape != shape:
                raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
           _a			      =							latents.to(UpperCamelCase_						)

      _a			      =							latents * scheduler.init_noise_sigma
      return latents
 def 							__UpperCAmelCase		( self      ,				__magic_name__=0						)					->							Dict:





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

      _a			      =							torch.device(f'cuda:{gpu_id}'						)

      _a			      =							[
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
                cpu_offload(UpperCamelCase_      ,				UpperCamelCase_						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__=0						)					->							Union[str, Any]:





      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			      =							torch.device(f'cuda:{gpu_id}'						)

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

      _a			      =							None
      for cpu_offloaded_model in [self.unet, self.movq]:
           _a			      =							cpu_offload_with_hook(UpperCamelCase_      ,				UpperCamelCase_      ,				prev_module_hook=UpperCamelCase_						)

      # We'll offload the last model manually.
      _a			      =							hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:





      if not hasattr(self.unet      ,				'_hf_hook'						):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(UpperCamelCase_      ,				'_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(UpperCamelCase_						)
 def __call__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 5_12      ,				__magic_name__ = 5_12      ,				__magic_name__ = 1_00      ,				__magic_name__ = 4.0      ,				__magic_name__ = 1      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = "pil"      ,				__magic_name__ = True      ,				)					->							List[Any]:
      _a			      =							self._execution_device

      _a			      =							guidance_scale > 1.0

      if isinstance(UpperCamelCase_      ,				UpperCamelCase_						):
           _a			      =							torch.cat(UpperCamelCase_      ,				dim=0						)
      if isinstance(UpperCamelCase_      ,				UpperCamelCase_						):
           _a			      =							torch.cat(UpperCamelCase_      ,				dim=0						)
      if isinstance(UpperCamelCase_      ,				UpperCamelCase_						):
           _a			      =							torch.cat(UpperCamelCase_      ,				dim=0						)

      _a			      =							image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
           _a			      =							image_embeds.repeat_interleave(UpperCamelCase_      ,				dim=0						)
           _a			      =							negative_image_embeds.repeat_interleave(UpperCamelCase_      ,				dim=0						)
           _a			      =							hint.repeat_interleave(UpperCamelCase_      ,				dim=0						)

      _a			      =							torch.cat([negative_image_embeds, image_embeds]      ,				dim=0						).to(dtype=self.unet.dtype      ,				device=UpperCamelCase_						)
      _a			      =							torch.cat([hint, hint]      ,				dim=0						).to(dtype=self.unet.dtype      ,				device=UpperCamelCase_						)
      self.scheduler.set_timesteps(UpperCamelCase_      ,				device=UpperCamelCase_						)
      _a			      =							self.scheduler.timesteps

      _a			      =							self.movq.config.latent_channels

      _a			      =							downscale_height_and_width(UpperCamelCase_      ,				UpperCamelCase_      ,				self.movq_scale_factor						)

      # create initial latent
      _a			      =							self.prepare_latents(
          (batch_size, num_channels_latents, height, width)      ,				image_embeds.dtype      ,				UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_      ,				self.scheduler      ,				)

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

           _a			      =							{'image_embeds': image_embeds, 'hint': hint}
           _a			      =							self.unet(
               sample=UpperCamelCase_      ,				timestep=UpperCamelCase_      ,				encoder_hidden_states=UpperCamelCase_      ,				added_cond_kwargs=UpperCamelCase_      ,				return_dict=UpperCamelCase_      ,				)[0]

           if do_classifier_free_guidance:
                _a			      =							noise_pred.split(latents.shape[1]      ,				dim=1						)
                _a			      =							noise_pred.chunk(2						)
                _a			      =							variance_pred.chunk(2						)
                _a			      =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                _a			      =							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			      =							noise_pred.split(latents.shape[1]      ,				dim=1						)

           # compute the previous noisy sample x_t -> x_t-1
           _a			      =							self.scheduler.step(
               UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_      ,				generator=UpperCamelCase_      ,				)[0]
      # post-processing
      _a			      =							self.movq.decode(UpperCamelCase_      ,				force_not_quantize=UpperCamelCase_						)['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			      =							image * 0.5 + 0.5
           _a			      =							image.clamp(0      ,				1						)
           _a			      =							image.cpu().permute(0      ,				2      ,				3      ,				1						).float().numpy()

      if output_type == "pil":
           _a			      =							self.numpy_to_pil(UpperCamelCase_						)

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=UpperCamelCase_						)





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

