






import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')




def      lowercase__      (     __snake_case    :							np.ndarray  ):


					'''simple docstring'''


					return input_array.reshape((input_array.size, 1)  )




def      lowercase__      (     __snake_case    :							np.ndarray     ,       __snake_case    :							np.ndarray     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						np.nan
					for i in range(__snake_case  ):
										UpperCAmelCase_    :      Any            =						features[:, labels == i]
										UpperCAmelCase_    :      Tuple            =						data.mean(1  )
										# Centralize the data of class i
										UpperCAmelCase_    :      List[str]            =						data - column_reshape(__snake_case  )
										if i > 0:
															# If covariance_sum is not None
															covariance_sum += np.dot(__snake_case     ,       centered_data.T  )
										else:
															# If covariance_sum is np.nan (i.e. first loop)
															UpperCAmelCase_    :      Optional[Any]            =						np.dot(__snake_case     ,       centered_data.T  )

					return covariance_sum / features.shape[1]




def      lowercase__      (     __snake_case    :							np.ndarray     ,       __snake_case    :							np.ndarray     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						features.mean(1  )
					UpperCAmelCase_    :      Tuple            =						np.nan
					for i in range(__snake_case  ):
										UpperCAmelCase_    :      Tuple            =						features[:, labels == i]
										UpperCAmelCase_    :      Union[str, Any]            =						data.shape[1]
										UpperCAmelCase_    :      List[str]            =						data.mean(1  )
										if i > 0:
															# If covariance_sum is not None
															covariance_sum += device_data * np.dot(
															    column_reshape(__snake_case  ) - column_reshape(__snake_case  )     ,       (column_reshape(__snake_case  ) - column_reshape(__snake_case  )).T     ,       )
										else:
															# If covariance_sum is np.nan (i.e. first loop)
															UpperCAmelCase_    :      Any            =						device_data * np.dot(
															    column_reshape(__snake_case  ) - column_reshape(__snake_case  )     ,       (column_reshape(__snake_case  ) - column_reshape(__snake_case  )).T     ,       )

					return covariance_sum / features.shape[1]




def      lowercase__      (     __snake_case    :							np.ndarray     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if features.any():
										UpperCAmelCase_    :      int            =						features.mean(1  )
										# Center the dataset
										UpperCAmelCase_    :      Optional[int]            =						features - np.reshape(__snake_case     ,       (data_mean.size, 1)  )
										UpperCAmelCase_    :      int            =						np.dot(__snake_case     ,       centered_data.T  ) / features.shape[1]
										UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						np.linalg.eigh(__snake_case  )
										# Take all the columns in the reverse order (-1), and then takes only the first
										UpperCAmelCase_    :      Optional[int]            =						eigenvectors[:, ::-1][:, 0:dimensions]
										# Project the database on the new space
										UpperCAmelCase_    :      Optional[Any]            =						np.dot(filtered_eigenvectors.T     ,       __snake_case  )
										logging.info('Principal Component Analysis computed'  )

										return projected_data
					else:
										logging.basicConfig(level=logging.ERROR     ,       format='%(message)s'     ,       force=__snake_case  )
										logging.error('Dataset empty'  )
										raise AssertionError




def      lowercase__      (     __snake_case    :							np.ndarray     ,       __snake_case    :							np.ndarray     ,       __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


					assert classes > dimensions

					# Check if features have been already loaded
					if features.any:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						eigh(
										    covariance_between_classes(__snake_case     ,       __snake_case     ,       __snake_case  )     ,       covariance_within_classes(__snake_case     ,       __snake_case     ,       __snake_case  )     ,       )
										UpperCAmelCase_    :      Tuple            =						eigenvectors[:, ::-1][:, :dimensions]
										UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						np.linalg.svd(__snake_case  )
										UpperCAmelCase_    :      List[str]            =						svd_matrix[:, 0:dimensions]
										UpperCAmelCase_    :      Dict            =						np.dot(filtered_svd_matrix.T     ,       __snake_case  )
										logging.info('Linear Discriminant Analysis computed'  )

										return projected_data
					else:
										logging.basicConfig(level=logging.ERROR     ,       format='%(message)s'     ,       force=__snake_case  )
										logging.error('Dataset empty'  )
										raise AssertionError




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]  )
					UpperCAmelCase_    :      Any            =						np.array([0, 0, 0, 1, 1]  )
					UpperCAmelCase_    :      Optional[int]            =						2
					UpperCAmelCase_    :      Dict            =						2

					# Assert that the function raises an AssertionError if dimensions > classes
					with pytest.raises(__snake_case  ) as error_info:
										UpperCAmelCase_    :      Union[str, Any]            =						linear_discriminant_analysis(
										    __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
										if isinstance(__snake_case     ,       np.ndarray  ):
															raise AssertionError(
															    'Did not raise AssertionError for dimensions > classes'  )
										assert error_info.type is AssertionError




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]  )
					UpperCAmelCase_    :      Optional[Any]            =						2
					UpperCAmelCase_    :      Optional[Any]            =						np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]  )

					with pytest.raises(__snake_case  ) as error_info:
										UpperCAmelCase_    :      Dict            =						principal_component_analysis(__snake_case     ,       __snake_case  )
										if not np.allclose(__snake_case     ,       __snake_case  ):
															raise AssertionError
										assert error_info.type is AssertionError


if __name__ == "__main__":
		import doctest

		doctest.testmod()














from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

__UpperCAmelCase								=						'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							int     ,       __snake_case    :							Tuple=8  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Dict            =						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCAmelCase_    :      List[Any]            =						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor




def      lowercase__      (     __snake_case    :							Any     ,       __snake_case    :							int=512     ,       __snake_case    :							Dict=512  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						pil_image.resize((w, h)     ,       resample=Image.BICUBIC     ,       reducing_gap=1  )
					UpperCAmelCase_    :      Dict            =						np.array(pil_image.convert('RGB'  )  )
					UpperCAmelCase_    :      Any            =						arr.astype(np.floataa  ) / 127.5 - 1
					UpperCAmelCase_    :      Dict            =						np.transpose(__snake_case     ,       [2, 0, 1]  )
					UpperCAmelCase_    :      List[str]            =						torch.from_numpy(__snake_case  ).unsqueeze(0  )
					return image






class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Union[str, Any]:
							super().__init__()

							self.register_modules(
							    unet=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		movq=_UpperCamelCase				,		)
							UpperCAmelCase_    :      Tuple            =						2 ** (len(self.movq.config.block_out_channels   ) - 1)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							# get the original timestep using init_timestep
							UpperCAmelCase_    :      Any            =						min(int(num_inference_steps * strength   )				,		_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						max(num_inference_steps - init_timestep				,		0   )
							UpperCAmelCase_    :      str            =						self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							Tuple:
							if not isinstance(_UpperCamelCase				,		(torch.Tensor, PIL.Image.Image, list)   ):
												raise ValueError(
												    f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase   )}"   )

							UpperCAmelCase_    :      List[str]            =						image.to(device=_UpperCamelCase				,		dtype=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						batch_size * num_images_per_prompt

							if image.shape[1] == 4:
												UpperCAmelCase_    :      List[str]            =						image

							else:
												if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and len(_UpperCamelCase   ) != batch_size:
																	raise ValueError(
																	    f"You have passed a list of generators of length {len(_UpperCamelCase   )}, but requested an effective batch"
																	    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

												elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
																	UpperCAmelCase_    :      Any            =						[
																	    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(_UpperCamelCase   )
																	]
																	UpperCAmelCase_    :      Tuple            =						torch.cat(_UpperCamelCase				,		dim=0   )
												else:
																	UpperCAmelCase_    :      Union[str, Any]            =						self.movq.encode(_UpperCamelCase   ).latent_dist.sample(_UpperCamelCase   )

												UpperCAmelCase_    :      int            =						self.movq.config.scaling_factor * init_latents

							UpperCAmelCase_    :      Optional[int]            =						torch.cat([init_latents]				,		dim=0   )

							UpperCAmelCase_    :      Tuple            =						init_latents.shape
							UpperCAmelCase_    :      List[Any]            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=_UpperCamelCase   )

							# get latents
							UpperCAmelCase_    :      str            =						self.scheduler.add_noise(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						init_latents

							return latents



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Any:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'   )

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

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=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.'   )

							UpperCAmelCase_    :      str            =						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)

							UpperCAmelCase_    :      Dict            =						None
							for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						cpu_offload_with_hook(_UpperCamelCase				,		_UpperCamelCase				,		prev_module_hook=_UpperCamelCase   )

							# We'll offload the last model manually.
							UpperCAmelCase_    :      Any            =						hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 1_0_0				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 0.3				,		_UpperCamelCase = 1				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							str:
							UpperCAmelCase_    :      Any            =						self._execution_device

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

							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						torch.cat(_UpperCamelCase				,		dim=0   )
							UpperCAmelCase_    :      Optional[Any]            =						image_embeds.shape[0]
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						torch.cat(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      int            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )
												UpperCAmelCase_    :      int            =						negative_image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.cat([negative_image_embeds, image_embeds]				,		dim=0   ).to(dtype=self.unet.dtype				,		device=_UpperCamelCase   )

							if not isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Tuple            =						[image]
							if not all(isinstance(_UpperCamelCase				,		(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
												raise ValueError(
												    f"Input is in incorrect format: {[type(_UpperCamelCase   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"   )

							UpperCAmelCase_    :      str            =						torch.cat([prepare_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ) for i in image]				,		dim=0   )
							UpperCAmelCase_    :      Any            =						image.to(dtype=image_embeds.dtype				,		device=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						self.movq.encode(_UpperCamelCase   )['latents']
							UpperCAmelCase_    :      List[Any]            =						latents.repeat_interleave(_UpperCamelCase				,		dim=0   )
							self.scheduler.set_timesteps(_UpperCamelCase				,		device=_UpperCamelCase   )
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						self.get_timesteps(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						timesteps[:1].repeat(batch_size * num_images_per_prompt   )
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						downscale_height_and_width(_UpperCamelCase				,		_UpperCamelCase				,		self.movq_scale_factor   )
							UpperCAmelCase_    :      Dict            =						self.prepare_latents(
							    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		image_embeds.dtype				,		_UpperCamelCase				,		_UpperCamelCase   )
							for i, t in enumerate(self.progress_bar(_UpperCamelCase   )   ):
												# expand the latents if we are doing classifier free guidance
												UpperCAmelCase_    :      Optional[Any]            =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

												UpperCAmelCase_    :      str            =						{'image_embeds': image_embeds}
												UpperCAmelCase_    :      Union[str, Any]            =						self.unet(
												    sample=_UpperCamelCase				,		timestep=_UpperCamelCase				,		encoder_hidden_states=_UpperCamelCase				,		added_cond_kwargs=_UpperCamelCase				,		return_dict=_UpperCamelCase				,		)[0]

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

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_    :      List[str]            =						self.scheduler.step(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase				,		)[0]

							# post-processing
							UpperCAmelCase_    :      Optional[Any]            =						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"]:
												UpperCAmelCase_    :      List[str]            =						image * 0.5 + 0.5
												UpperCAmelCase_    :      List[Any]            =						image.clamp(0				,		1   )
												UpperCAmelCase_    :      Dict            =						image.cpu().permute(0				,		2				,		3				,		1   ).float().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[Any]            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase   )






