





def  _SCREAMING_SNAKE_CASE				(   a  )      ->      list[int]:
					__A	:						List[Any]       =				[0 for i in range(len(a  )  )]

					# initialize interval's left pointer and right pointer
					__A    ,				__A	:						List[Any]       =				0, 0

					for i in range(1					,						len(a  )  ):
										# case when current index is inside the interval
										if i <= right_pointer:
															__A	:						Optional[Any]       =				min(right_pointer - i + 1					,						z_result[i - left_pointer]  )
															__A	:						Dict       =				min_edge

										while go_next(a					,						a					,						a  ):
															z_result[i] += 1

										# if new index's result gives us more right interval,
										# we've to update left_pointer and right_pointer
										if i + z_result[i] - 1 > right_pointer:
															__A    ,				__A	:						Optional[Any]       =				i, i + z_result[i] - 1

					return z_result







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      bool:
					return i + z_result[i] < len(a  ) and s[z_result[i]] == s[i + z_result[i]]







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      int:
					__A	:						Union[str, Any]       =				0
					# concatenate 'pattern' and 'input_str' and call z_function
					# with concatenated string
					__A	:						Optional[Any]       =				z_function(pattern + input_str  )

					for val in z_result:
										# if value is greater then length of the pattern string
										# that means this index is starting position of substring
										# which is equal to pattern string
										if val >= len(a  ):
															answer += 1

					return answer


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						:   List[Any]			  =     logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase						:   Dict			  =     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a=8  )      ->      Tuple:
					__A	:						List[str]       =				height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__A	:						Optional[int]       =				width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







def  _SCREAMING_SNAKE_CASE				(   a					,						a=5_12					,						a=5_12  )      ->      int:
					__A	:						Optional[Any]       =				pil_image.resize((w, h)					,						resample=Image.BICUBIC					,						reducing_gap=1  )
					__A	:						Union[str, Any]       =				np.array(pil_image.convert('RGB'  )  )
					__A	:						Optional[int]       =				arr.astype(np.floataa  ) / 127.5 - 1
					__A	:						int       =				np.transpose(a					,						[2, 0, 1]  )
					__A	:						Tuple       =				torch.from_numpy(a  ).unsqueeze(0  )
					return image





class 						_A(					snake_case__   ):


				"""simple docstring"""


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

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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									# get the original timestep using init_timestep
									__A	:						Optional[int]       =				min(int(num_inference_steps * strength     )     ,					_A     )

									__A	:						Dict       =				max(num_inference_steps - init_timestep     ,					0     )
									__A	:						Tuple       =				self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A=None     ):
									if not isinstance(_A     ,					(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(_A     )}"""     )

									__A	:						Union[str, Any]       =				image.to(device=_A     ,					dtype=_A     )

									__A	:						Optional[Any]       =				batch_size * num_images_per_prompt

									if image.shape[1] == 4:
														__A	:						int       =				image

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

														elif isinstance(_A     ,					_A     ):
																			__A	:						str       =				[
																			    self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(_A     )
																			]
																			__A	:						str       =				torch.cat(_A     ,					dim=0     )
														else:
																			__A	:						List[str]       =				self.movq.encode(_A     ).latent_dist.sample(_A     )

														__A	:						Tuple       =				self.movq.config.scaling_factor * init_latents

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

									__A	:						Union[str, Any]       =				init_latents.shape
									__A	:						List[str]       =				randn_tensor(_A     ,					generator=_A     ,					device=_A     ,					dtype=_A     )

									# get latents
									__A	:						Optional[Any]       =				self.scheduler.add_noise(_A     ,					_A     ,					_A     )

									__A	:						Optional[int]       =				init_latents

									return latents



				def 					UpperCAmelCase_		(			self     ,					_A=0     ):
									if is_accelerate_available():
														from accelerate import cpu_offload
									else:
														raise ImportError('Please install accelerate via `pip install accelerate`'     )

									__A	:						Optional[int]       =				torch.device(F"""cuda:{gpu_id}"""     )

									__A	:						Union[str, Any]       =				[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(_A     ,					_A     )



				def 					UpperCAmelCase_		(			self     ,					_A=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.'     )

									__A	:						List[Any]       =				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)

									__A	:						int       =				None
									for cpu_offloaded_model in [self.unet, self.movq]:
														__A    ,				__A	:						Optional[int]       =				cpu_offload_with_hook(_A     ,					_A     ,					prev_module_hook=_A     )

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



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 					UpperCAmelCase_		(			self     ):
									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     ,					_A     ,					_A     ,					_A     ,					_A = 512     ,					_A = 512     ,					_A = 100     ,					_A = 4.0     ,					_A = 0.3     ,					_A = 1     ,					_A = None     ,					_A = "pil"     ,					_A = True     ,					):
									__A	:						List[Any]       =				self._execution_device

									__A	:						Optional[Any]       =				guidance_scale > 1.0

									if isinstance(_A     ,					_A     ):
														__A	:						Optional[Any]       =				torch.cat(_A     ,					dim=0     )
									__A	:						Tuple       =				image_embeds.shape[0]
									if isinstance(_A     ,					_A     ):
														__A	:						List[Any]       =				torch.cat(_A     ,					dim=0     )

									if do_classifier_free_guidance:
														__A	:						Union[str, Any]       =				image_embeds.repeat_interleave(_A     ,					dim=0     )
														__A	:						Optional[int]       =				negative_image_embeds.repeat_interleave(_A     ,					dim=0     )

									__A	:						List[str]       =				torch.cat([negative_image_embeds, image_embeds]     ,					dim=0     ).to(dtype=self.unet.dtype     ,					device=_A     )

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

									__A	:						Dict       =				torch.cat([prepare_image(_A     ,					_A     ,					_A     ) for i in image]     ,					dim=0     )
									__A	:						Any       =				image.to(dtype=image_embeds.dtype     ,					device=_A     )

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

														__A	:						Dict       =				{'image_embeds': image_embeds}
														__A	:						List[str]       =				self.unet(
														    sample=_A     ,					timestep=_A     ,					encoder_hidden_states=_A     ,					added_cond_kwargs=_A     ,					return_dict=_A     ,					)[0]

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

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

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

									if output_type == "pil":
														__A	:						Any       =				self.numpy_to_pil(_A     )

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=_A     )




