'''simple docstring'''




def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    list:
    __lowerCamelCase		=  len(UpperCamelCase__				)
    __lowerCamelCase		=  []
    for i in range(len(UpperCamelCase__				) - pat_len + 1				):
        __lowerCamelCase		=  True
        for j in range(UpperCamelCase__				):
            if s[i + j] != pattern[j]:
                __lowerCamelCase		=  False
                break
        if match_found:
            position.append(UpperCamelCase__				)
    return position


if __name__ == "__main__":
  assert naive_pattern_search("ABCDEFG", "DE") == [3]
  print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))








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   )






