





'''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,
)


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

lowerCAmelCase_ : Optional[Any]	  =       "\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  _lowerCamelCase	(__lowerCamelCase	:  Optional[Any]       ,	__lowerCamelCase	:  str       ,	__lowerCamelCase	:  List[str]=8  ) ->     Optional[Any]:
			a__	 =			height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			a__	 =			width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor







def  _lowerCamelCase	(__lowerCamelCase	:  List[str]       ,	__lowerCamelCase	:  Any=512       ,	__lowerCamelCase	:  Optional[int]=512  ) ->     Any:
			a__	 =			pil_image.resize((w, h)       ,	resample=Image.BICUBIC       ,	reducing_gap=1  )
			a__	 =			np.array(pil_image.convert("RGB"  )  )
			a__	 =			arr.astype(np.floataa  ) / 127.5 - 1
			a__	 =			np.transpose(snake_case_       ,	[2, 0, 1]  )
			a__	 =			torch.from_numpy(snake_case_  ).unsqueeze(0  )
			return image






class    UpperCamelCase__	(		SCREAMING_SNAKE_CASE__  ):







	def __init__(					self      : Dict			,			lowerCamelCase      : List[str]			,			lowerCamelCase      : Optional[int]			,			lowerCamelCase      : Dict			,			):




				'''simple docstring'''
				super().__init__()

				self.register_modules(
				    unet=__lowerCamelCase			,			scheduler=__lowerCamelCase			,			movq=__lowerCamelCase			,			)
				a__	 =			2 ** (len(self.movq.config.block_out_channels		) - 1)







	def     __a (					self      : int			,			lowerCamelCase      : List[str]			,			lowerCamelCase      : Tuple			,			lowerCamelCase      : str		):




				'''simple docstring'''
				a__	 =			min(int(num_inference_steps * strength		)			,			__lowerCamelCase		)

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

				return timesteps, num_inference_steps - t_start







	def     __a (					self      : List[str]			,			lowerCamelCase      : int			,			lowerCamelCase      : List[str]			,			lowerCamelCase      : Optional[int]			,			lowerCamelCase      : Optional[Any]			,			lowerCamelCase      : Any			,			lowerCamelCase      : List[Any]			,			lowerCamelCase      : Any=None		):




				'''simple docstring'''




				if not isinstance(__lowerCamelCase			,			(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(__lowerCamelCase		)}'''		)

				a__	 =			image.to(device=__lowerCamelCase			,			dtype=__lowerCamelCase		)

				a__	 =			batch_size * num_images_per_prompt

				if image.shape[1] == 4:
							a__	 =			image

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

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

							a__	 =			self.movq.config.scaling_factor * init_latents

				a__	 =			torch.cat([init_latents]			,			dim=0		)

				a__	 =			init_latents.shape
				a__	 =			randn_tensor(__lowerCamelCase			,			generator=__lowerCamelCase			,			device=__lowerCamelCase			,			dtype=__lowerCamelCase		)

				# get latents
				a__	 =			self.scheduler.add_noise(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase		)

				a__	 =			init_latents

				return latents







	def     __a (					self      : str			,			lowerCamelCase      : List[str]=0		):




				'''simple docstring'''




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

				a__	 =			torch.device(F'''cuda:{gpu_id}'''		)

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







	def     __a (					self      : Dict			,			lowerCamelCase      : Tuple=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."		)

				a__	 =			torch.device(F'''cuda:{gpu_id}'''		)

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

				a__	 =			None
				for cpu_offloaded_model in [self.unet, self.movq]:
							a__	 =			cpu_offload_with_hook(__lowerCamelCase			,			__lowerCamelCase			,			prev_module_hook=__lowerCamelCase		)

				# We'll offload the last model manually.
				a__	 =			hook







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




				'''simple docstring'''




				if not hasattr(self.unet			,			"_hf_hook"		):
							return self.device
				for module in self.unet.modules():
							if (
							    hasattr(__lowerCamelCase			,			"_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(__lowerCamelCase		)
	def __call__(					self      : int			,			lowerCamelCase      : Optional[int]			,			lowerCamelCase      : str			,			lowerCamelCase      : Optional[int]			,			lowerCamelCase      : int = 5_1_2			,			lowerCamelCase      : Optional[int] = 5_1_2			,			lowerCamelCase      : int = 1_0_0			,			lowerCamelCase      : Tuple = 4.0			,			lowerCamelCase      : Dict = 0.3			,			lowerCamelCase      : Any = 1			,			lowerCamelCase      : Union[str, Any] = None			,			lowerCamelCase      : Dict = "pil"			,			lowerCamelCase      : List[str] = True			,			):




				'''simple docstring'''
				a__	 =			self._execution_device

				a__	 =			guidance_scale > 1.0

				if isinstance(__lowerCamelCase			,			__lowerCamelCase		):
							a__	 =			torch.cat(__lowerCamelCase			,			dim=0		)
				a__	 =			image_embeds.shape[0]
				if isinstance(__lowerCamelCase			,			__lowerCamelCase		):
							a__	 =			torch.cat(__lowerCamelCase			,			dim=0		)

				if do_classifier_free_guidance:
							a__	 =			image_embeds.repeat_interleave(__lowerCamelCase			,			dim=0		)
							a__	 =			negative_image_embeds.repeat_interleave(__lowerCamelCase			,			dim=0		)

				a__	 =			torch.cat([negative_image_embeds, image_embeds]			,			dim=0		).to(dtype=self.unet.dtype			,			device=__lowerCamelCase		)

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

				a__	 =			torch.cat([prepare_image(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase		) for i in image]			,			dim=0		)
				a__	 =			image.to(dtype=image_embeds.dtype			,			device=__lowerCamelCase		)

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

							a__	 =			{'''image_embeds''': image_embeds}
							a__	 =			self.unet(
							    sample=__lowerCamelCase			,			timestep=__lowerCamelCase			,			encoder_hidden_states=__lowerCamelCase			,			added_cond_kwargs=__lowerCamelCase			,			return_dict=__lowerCamelCase			,			)[0]

							if do_classifier_free_guidance:
										a__	 =			noise_pred.split(latents.shape[1]			,			dim=1		)
										a__	 =			noise_pred.chunk(2		)
										a__	 =			variance_pred.chunk(2		)
										a__	 =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
										a__	 =			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__	 =			noise_pred.split(latents.shape[1]			,			dim=1		)

							# compute the previous noisy sample x_t -> x_t-1
							a__	 =			self.scheduler.step(
							    __lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			generator=__lowerCamelCase			,			)[0]

				# post-processing
				a__	 =			self.movq.decode(__lowerCamelCase			,			force_not_quantize=__lowerCamelCase		)['''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__	 =			image * 0.5 + 0.5
							a__	 =			image.clamp(0			,			1		)
							a__	 =			image.cpu().permute(0			,			2			,			3			,			1		).float().numpy()

				if output_type == "pil":
							a__	 =			self.numpy_to_pil(__lowerCamelCase		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=__lowerCamelCase		)










"""simple docstring"""



def 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







    def get_matched_characters(snake_case_    :    str							,snake_case_    :    str		) -> str:
        __A       :		Any							=	[]
        __A       :		Any							=	min(len(_stra		)							,len(_stra		)		) // 2
        for i, l in enumerate(_stra		):
            __A       :		Dict							=	int(max(0							,i - limit		)		)
            __A       :		Tuple							=	int(min(i + limit + 1							,len(_stra		)		)		)
            if l in _stra[left:right]:
                matched.append(snake_case_		)
                __A       :		Any							=	F"""{_stra[0:_stra.index(snake_case_		)]} {_stra[_stra.index(snake_case_		) + 1:]}"""

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

    # transposition
    __A       :		Dict							=	(
        len([(ca, ca) for ca, ca in zip(snake_case_							,snake_case_		) if ca != ca]		) // 2
    )

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	0
    for ca, ca in zip(stra[:4]							,stra[:4]		):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("""hello""", """world"""))




