
"""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,
)


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

lowerCamelCase__        =		'\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    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=8		):
	__lowerCAmelCase      :						Any						      =				height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	__lowerCAmelCase      :						Tuple						      =				width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor




class 			A__	(     _lowerCamelCase):

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

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):




			if latents is None:
				__lowerCAmelCase      :						Optional[Any]						      =				randn_tensor(_snake_case	,		generator=_snake_case	,		device=_snake_case	,		dtype=_snake_case			)
			else:
				if latents.shape != shape:
					raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"			)
				__lowerCAmelCase      :						Dict						      =				latents.to(_snake_case			)

			__lowerCAmelCase      :						Union[str, Any]						      =				latents * scheduler.init_noise_sigma
			return latents

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=0			):




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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=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.'			)

			__lowerCAmelCase      :						List[str]						      =				torch.device(f"cuda:{gpu_id}"			)

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

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

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

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




			if not hasattr(self.unet	,		'_hf_hook'			):
				return self.device
			for module in self.unet.modules():
				if (
				    hasattr(_snake_case	,		'_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(_snake_case			)
		def __call__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 5_12	,		_SCREAMING_SNAKE_CASE = 5_12	,		_SCREAMING_SNAKE_CASE = 1_00	,		_SCREAMING_SNAKE_CASE = 4.0	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = "pil"	,		_SCREAMING_SNAKE_CASE = True	,		):
			__lowerCAmelCase      :						Optional[Any]						      =				self._execution_device

			__lowerCAmelCase      :						Any						      =				guidance_scale > 1.0

			if isinstance(_snake_case	,		_snake_case			):
				__lowerCAmelCase      :						Any						      =				torch.cat(_snake_case	,		dim=0			)
			if isinstance(_snake_case	,		_snake_case			):
				__lowerCAmelCase      :						List[str]						      =				torch.cat(_snake_case	,		dim=0			)
			if isinstance(_snake_case	,		_snake_case			):
				__lowerCAmelCase      :						Optional[int]						      =				torch.cat(_snake_case	,		dim=0			)

			__lowerCAmelCase      :						List[str]						      =				image_embeds.shape[0] * num_images_per_prompt

			if do_classifier_free_guidance:
				__lowerCAmelCase      :						Optional[Any]						      =				image_embeds.repeat_interleave(_snake_case	,		dim=0			)
				__lowerCAmelCase      :						List[Any]						      =				negative_image_embeds.repeat_interleave(_snake_case	,		dim=0			)
				__lowerCAmelCase      :						Optional[int]						      =				hint.repeat_interleave(_snake_case	,		dim=0			)

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

			__lowerCAmelCase      :						Optional[Any]						      =				self.movq.config.latent_channels

			__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				downscale_height_and_width(_snake_case	,		_snake_case	,		self.movq_scale_factor			)

			# create initial latent
			__lowerCAmelCase      :						List[str]						      =				self.prepare_latents(
			    (batch_size, num_channels_latents, height, width)	,		image_embeds.dtype	,		_snake_case	,		_snake_case	,		_snake_case	,		self.scheduler	,		)

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

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

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

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

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

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=_snake_case			)

"""simple docstring"""







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"		)
	__lowerCAmelCase      :						str						      =				formatter_cls
	for alias in set(aliases + [format_type]		):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"		)
		__lowerCAmelCase      :						Any						      =				format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	__lowerCAmelCase      :						int						      =				aliases if aliases is not None else []
	for alias in set(aliases + [format_type]		):
		__lowerCAmelCase      :						str						      =				unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							lowerCamelCase__        =		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							lowerCamelCase__        =		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							lowerCamelCase__        =		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def    __lowerCAmelCase    (_UpperCamelCase		):
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				get_format_type_from_alias(_UpperCamelCase		)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_UpperCamelCase		)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"		)