
'''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,
)


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

lowercase					:							int   =      "\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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=8			) ->			str:
	_snake_case     		=	height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	_snake_case     		=	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_					,     ):





								"""simple docstring"""






								super().__init__()

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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								if latents is None:
									_snake_case     		=	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}'						)
									_snake_case     		=	latents.to(lowerCAmelCase_						)

								_snake_case     		=	latents * scheduler.init_noise_sigma
								return latents


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=0						):





								"""simple docstring"""






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

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

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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=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.'						)

								_snake_case     		=	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)

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

								# We'll offload the last model manually.
								_snake_case     		=	hook


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





								"""simple docstring"""






								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_ = 5_12					,     lowerCAmelCase_ = 5_12					,     lowerCAmelCase_ = 1_00					,     lowerCAmelCase_ = 4.0					,     lowerCAmelCase_ = 1					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = "pil"					,     lowerCAmelCase_ = True					,     ):





								"""simple docstring"""






								_snake_case     		=	self._execution_device

								_snake_case     		=	guidance_scale > 1.0

								if isinstance(lowerCAmelCase_					,     lowerCAmelCase_						):
									_snake_case     		=	torch.cat(lowerCAmelCase_					,     dim=0						)
								if isinstance(lowerCAmelCase_					,     lowerCAmelCase_						):
									_snake_case     		=	torch.cat(lowerCAmelCase_					,     dim=0						)
								if isinstance(lowerCAmelCase_					,     lowerCAmelCase_						):
									_snake_case     		=	torch.cat(lowerCAmelCase_					,     dim=0						)

								_snake_case     		=	image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
									_snake_case     		=	image_embeds.repeat_interleave(lowerCAmelCase_					,     dim=0						)
									_snake_case     		=	negative_image_embeds.repeat_interleave(lowerCAmelCase_					,     dim=0						)
									_snake_case     		=	hint.repeat_interleave(lowerCAmelCase_					,     dim=0						)

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

								_snake_case     		=	self.movq.config.latent_channels

								_snake_case		,				_snake_case     		=	downscale_height_and_width(lowerCAmelCase_					,     lowerCAmelCase_					,     self.movq_scale_factor						)

								# create initial latent
								_snake_case     		=	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
									_snake_case     		=	torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

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

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

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

								if output_type == "pil":
									_snake_case     		=	self.numpy_to_pil(lowerCAmelCase_						)

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase_						)



'''simple docstring'''







lowercase					:							Tuple   =      {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase					:							Union[str, Any]   =      {value: key for key, value in encode_dict.items()}







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_snake_case     		=	''
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception('encode() accepts only letters of the alphabet and spaces'			)
	return encoded







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	if set(__A			) - {"A", "B", " "} != set():
		raise Exception('decode() accepts only \'A\', \'B\' and spaces'			)
	_snake_case     		=	''
	for word in coded.split():
		while len(__A			) != 0:
			decoded += decode_dict[word[:5]]
			_snake_case     		=	word[5:]
		decoded += " "
	return decoded.strip()


if __name__ == "__main__":
					from doctest import testmod

					testmod()

