





"""simple docstring"""


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,
)


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

__magic_name__       :					List[Any]						=			"""
    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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=8     ):
						UpperCamelCase				:		Union[str, Any]				   =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCamelCase				:		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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=512							,     SCREAMING_SNAKE_CASE=512     ):
						UpperCamelCase				:		Optional[Any]				   =			pil_image.resize((w, h)							,     resample=Image.BICUBIC							,     reducing_gap=1     )
						UpperCamelCase				:		Any				   =			np.array(pil_image.convert("""RGB"""     )     )
						UpperCamelCase				:		List[Any]				   =			arr.astype(np.floataa     ) / 1_27.5 - 1
						UpperCamelCase				:		Union[str, Any]				   =			np.transpose(SCREAMING_SNAKE_CASE							,     [2, 0, 1]     )
						UpperCamelCase				:		List[Any]				   =			torch.from_numpy(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )
						return image






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







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






									'''simple docstring'''







									super().__init__()

									self.register_modules(
									    unet=_A						,   scheduler=_A						,   movq=_A						,   )
									UpperCamelCase				:		List[str]				   =			2 ** (len(self.movq.config.block_out_channels    ) - 1)







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			min(int(num_inference_steps * strength    )						,   _A    )

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

									return timesteps, num_inference_steps - t_start







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A=None    ):






									'''simple docstring'''







									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    )}"""    )

									UpperCamelCase				:		Optional[int]				   =			image.to(device=_A						,   dtype=_A    )

									UpperCamelCase				:		int				   =			batch_size * num_images_per_prompt

									if image.shape[1] == 4:
															UpperCamelCase				:		List[str]				   =			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    ):
																					UpperCamelCase				:		str				   =			[
																					    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_A    )
																					]
																					UpperCamelCase				:		Union[str, Any]				   =			torch.cat(_A						,   dim=0    )
															else:
																					UpperCamelCase				:		List[str]				   =			self.movq.encode(_A    ).latent_dist.sample(_A    )

															UpperCamelCase				:		Dict				   =			self.movq.config.scaling_factor * init_latents

									UpperCamelCase				:		Tuple				   =			torch.cat([init_latents]						,   dim=0    )

									UpperCamelCase				:		List[str]				   =			init_latents.shape
									UpperCamelCase				:		int				   =			randn_tensor(_A						,   generator=_A						,   device=_A						,   dtype=_A    )

									# get latents
									UpperCamelCase				:		int				   =			self.scheduler.add_noise(_A						,   _A						,   _A    )

									UpperCamelCase				:		Optional[Any]				   =			init_latents

									return latents







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







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

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

									UpperCamelCase				:		Optional[int]				   =			[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(_A						,   _A    )







			def   _a      (      self						,   _A=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."""    )

									UpperCamelCase				:		List[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)

									UpperCamelCase				:		Dict				   =			None
									for cpu_offloaded_model in [self.unet, self.movq]:
															UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			cpu_offload_with_hook(_A						,   _A						,   prev_module_hook=_A    )

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







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






									'''simple docstring'''







									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 = 5_1_2						,   _A = 5_1_2						,   _A = 1_0_0						,   _A = 4.0						,   _A = 0.3						,   _A = 1						,   _A = None						,   _A = "pil"						,   _A = True						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self._execution_device

									UpperCamelCase				:		List[Any]				   =			guidance_scale > 1.0

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Dict				   =			torch.cat(_A						,   dim=0    )
									UpperCamelCase				:		Tuple				   =			image_embeds.shape[0]
									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		int				   =			torch.cat(_A						,   dim=0    )

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

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

									if not isinstance(_A						,   _A    ):
															UpperCamelCase				:		Tuple				   =			[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"""    )

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

									UpperCamelCase				:		str				   =			self.movq.encode(_A    )["""latents"""]
									UpperCamelCase				:		int				   =			latents.repeat_interleave(_A						,   dim=0    )
									self.scheduler.set_timesteps(_A						,   device=_A    )
									UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			self.get_timesteps(_A						,   _A						,   _A    )
									UpperCamelCase				:		Union[str, Any]				   =			timesteps[:1].repeat(batch_size * num_images_per_prompt    )
									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			downscale_height_and_width(_A						,   _A						,   self.movq_scale_factor    )
									UpperCamelCase				:		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
															UpperCamelCase				:		Optional[Any]				   =			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

															UpperCamelCase				:		Dict				   =			{"""image_embeds""": image_embeds}
															UpperCamelCase				:		int				   =			self.unet(
															    sample=_A						,   timestep=_A						,   encoder_hidden_states=_A						,   added_cond_kwargs=_A						,   return_dict=_A						,   )[0]

															if do_classifier_free_guidance:
																					UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			noise_pred.split(latents.shape[1]						,   dim=1    )
																					UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			noise_pred.chunk(2    )
																					UpperCamelCase   ,						UpperCamelCase				:		Optional[Any]				   =			variance_pred.chunk(2    )
																					UpperCamelCase				:		Tuple				   =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					UpperCamelCase				:		Union[str, Any]				   =			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[Any]				   =			self.scheduler.step(
															    _A						,   _A						,   _A						,   generator=_A						,   )[0]

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

									if output_type == "pil":
															UpperCamelCase				:		Optional[Any]				   =			self.numpy_to_pil(_A    )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_A    )










"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						UpperCamelCase				:		str				   =			""""""
						UpperCamelCase				:		Dict				   =			""""""

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(SCREAMING_SNAKE_CASE     ) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			0, 0

						# length[i] shows the length of palindromic substring with center i
						UpperCamelCase				:		List[Any]				   =			[1 for i in range(len(SCREAMING_SNAKE_CASE     )     )]

						# for each character in new_string find corresponding palindromic string
						UpperCamelCase				:		Optional[Any]				   =			0
						for j in range(len(SCREAMING_SNAKE_CASE     )     ):
												UpperCamelCase				:		Union[str, Any]				   =			1 if j > r else min(length[l + r - j] // 2							,     r - j + 1     )
												while (
												    j - k >= 0
												    and j + k < len(SCREAMING_SNAKE_CASE     )
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												UpperCamelCase				:		int				   =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		UpperCamelCase				:		int				   =			j - k + 1  # noqa: E741
																		UpperCamelCase				:		List[Any]				   =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		UpperCamelCase				:		List[str]				   =			length[j]
																		UpperCamelCase				:		List[str]				   =			j

    # create that string
						UpperCamelCase				:		Optional[Any]				   =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
		import doctest

		doctest.testmod()



