


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

__A :  List[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     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       List[Any]							,   A__ :       Any=8 ):
		'''simple docstring'''





		lowerCAmelCase_      :				Tuple      =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCAmelCase_      :				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        __snake_case	(       lowercase__):







				"""simple docstring"""



				def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  UNetaDConditionModel   ,						lowerCamelCase   :  DDPMScheduler   ,						lowerCamelCase   :  VQModel   ,						)       ->  List[Any]:
						super().__init__()

						self.register_modules(
						    unet=_a   ,						scheduler=_a   ,						movq=_a   ,						)
						lowerCAmelCase_      :				Any      =       2 ** (len(self.movq.config.block_out_channels		) - 1)

				def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[str]		)       ->  Optional[Any]:






						if latents is None:
								lowerCAmelCase_      :				int      =       randn_tensor(_a   ,						generator=_a   ,						device=_a   ,						dtype=_a		)
						else:
								if latents.shape != shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
								lowerCAmelCase_      :				List[str]      =       latents.to(_a		)

						lowerCAmelCase_      :				List[str]      =       latents * scheduler.init_noise_sigma
						return latents

				def __lowercase       (       self   :  str   ,						lowerCamelCase   :  Tuple=0		)       ->  Union[str, Any]:






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

						lowerCAmelCase_      :				List[str]      =       torch.device(F'cuda:{gpu_id}'		)

						lowerCAmelCase_      :				str      =       [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
										cpu_offload(_a   ,						_a		)

				def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Optional[Any]=0		)       ->  Tuple:






						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."""		)

						lowerCAmelCase_      :				str      =       torch.device(F'cuda:{gpu_id}'		)

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

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

						# We'll offload the last model manually.
						lowerCAmelCase_      :				List[str]      =       hook

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def __lowercase       (       self   :  Optional[Any]		)       ->  Any:






						if not hasattr(self.unet   ,						"""_hf_hook"""		):
								return self.device
						for module in self.unet.modules():
								if (
								    hasattr(_a   ,						"""_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(_a		)
				def __call__(       self   :  List[str]   ,						lowerCamelCase   :  Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						lowerCamelCase   :  Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						lowerCamelCase   :  torch.FloatTensor   ,						lowerCamelCase   :  int = 5_12   ,						lowerCamelCase   :  int = 5_12   ,						lowerCamelCase   :  int = 1_00   ,						lowerCamelCase   :  float = 4.0   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						lowerCamelCase   :  Optional[torch.FloatTensor] = None   ,						lowerCamelCase   :  Optional[str] = "pil"   ,						lowerCamelCase   :  bool = True   ,						)       ->  Tuple:
						lowerCAmelCase_      :				List[Any]      =       self._execution_device

						lowerCAmelCase_      :				Union[str, Any]      =       guidance_scale > 1.0

						if isinstance(_a   ,						_a		):
								lowerCAmelCase_      :				int      =       torch.cat(_a   ,						dim=0		)
						if isinstance(_a   ,						_a		):
								lowerCAmelCase_      :				Any      =       torch.cat(_a   ,						dim=0		)
						if isinstance(_a   ,						_a		):
								lowerCAmelCase_      :				Optional[int]      =       torch.cat(_a   ,						dim=0		)

						lowerCAmelCase_      :				Optional[Any]      =       image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
								lowerCAmelCase_      :				List[str]      =       image_embeds.repeat_interleave(_a   ,						dim=0		)
								lowerCAmelCase_      :				Union[str, Any]      =       negative_image_embeds.repeat_interleave(_a   ,						dim=0		)
								lowerCAmelCase_      :				int      =       hint.repeat_interleave(_a   ,						dim=0		)

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

						lowerCAmelCase_      :				Any      =       self.movq.config.latent_channels

						lowerCAmelCase_      :				Union[str, Any]      =       downscale_height_and_width(_a   ,						_a   ,						self.movq_scale_factor		)

						# create initial latent
						lowerCAmelCase_      :				Union[str, Any]      =       self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)   ,						image_embeds.dtype   ,						_a   ,						_a   ,						_a   ,						self.scheduler   ,						)

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

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

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

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

						if output_type == "pil":
								lowerCAmelCase_      :				List[str]      =       self.numpy_to_pil(_a		)

						if not return_dict:
								return (image,)

						return ImagePipelineOutput(images=_a		)







'''simple docstring'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



