


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,
)


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

lowercase_						=       """\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	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=8					):
			lowercase__   					=			height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			lowercase__   					=			width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=512  ,					SCREAMING_SNAKE_CASE_=512					):
			lowercase__   					=			pil_image.resize((w, h)  ,					resample=Image.BICUBIC  ,					reducing_gap=1					)
			lowercase__   					=			np.array(pil_image.convert("RGB"					)					)
			lowercase__   					=			arr.astype(np.floataa					) / 127.5 - 1
			lowercase__   					=			np.transpose(A__  ,					[2, 0, 1]					)
			lowercase__   					=			torch.from_numpy(A__					).unsqueeze(0					)
			return image




class 			_snake_case     (   __lowerCamelCase):




				def __init__(  self   :  Optional[Any],   __lowercase   :  UNetaDConditionModel,   __lowercase   :  DDPMScheduler,   __lowercase   :  VQModel,   ):
							super().__init__()

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




				def 							A__ (  self   :  List[Any],   __lowercase   :  Union[str, Any],   __lowercase   :  int,   __lowercase   :  Optional[int]  ):
							# get the original timestep using init_timestep
							lowercase__   					=			min(int(num_inference_steps * strength  ),   UpperCamelCase_  )

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

							return timesteps, num_inference_steps - t_start




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  List[str],   __lowercase   :  Union[str, Any],   __lowercase   :  Dict,   __lowercase   :  Dict,   __lowercase   :  Union[str, Any],   __lowercase   :  Optional[Any],   __lowercase   :  Any=None  ):
							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_  )}'''  )

							lowercase__   					=			image.to(device=UpperCamelCase_,   dtype=UpperCamelCase_  )

							lowercase__   					=			batch_size * num_images_per_prompt

							if image.shape[1] == 4:
										lowercase__   					=			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_  ):
													lowercase__   					=			[
													    self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(UpperCamelCase_  )
													]
													lowercase__   					=			torch.cat(UpperCamelCase_,   dim=0  )
										else:
													lowercase__   					=			self.movq.encode(UpperCamelCase_  ).latent_dist.sample(UpperCamelCase_  )

										lowercase__   					=			self.movq.config.scaling_factor * init_latents

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

							lowercase__   					=			init_latents.shape
							lowercase__   					=			randn_tensor(UpperCamelCase_,   generator=UpperCamelCase_,   device=UpperCamelCase_,   dtype=UpperCamelCase_  )

							# get latents
							lowercase__   					=			self.scheduler.add_noise(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_  )

							lowercase__   					=			init_latents

							return latents




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  str=0  ):
							if is_accelerate_available():
										from accelerate import cpu_offload
							else:
										raise ImportError("Please install accelerate via `pip install accelerate`"  )

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

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




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  Union[str, Any]=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."  )

							lowercase__   					=			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)

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

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




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 							A__ (  self   :  List[Any]  ):
							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   :  Tuple,   __lowercase   :  Union[torch.FloatTensor, List[torch.FloatTensor]],   __lowercase   :  Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],   __lowercase   :  Union[torch.FloatTensor, List[torch.FloatTensor]],   __lowercase   :  int = 512,   __lowercase   :  int = 512,   __lowercase   :  int = 100,   __lowercase   :  float = 4.0,   __lowercase   :  float = 0.3,   __lowercase   :  int = 1,   __lowercase   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None,   __lowercase   :  Optional[str] = "pil",   __lowercase   :  bool = True,   ):
							lowercase__   					=			self._execution_device

							lowercase__   					=			guidance_scale > 1.0

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

							if do_classifier_free_guidance:
										lowercase__   					=			image_embeds.repeat_interleave(UpperCamelCase_,   dim=0  )
										lowercase__   					=			negative_image_embeds.repeat_interleave(UpperCamelCase_,   dim=0  )

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

							if not isinstance(UpperCamelCase_,   UpperCamelCase_  ):
										lowercase__   					=			[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'''  )

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

							lowercase__   					=			self.movq.encode(UpperCamelCase_  )["latents"]
							lowercase__   					=			latents.repeat_interleave(UpperCamelCase_,   dim=0  )
							self.scheduler.set_timesteps(UpperCamelCase_,   device=UpperCamelCase_  )
							lowercase__					,    lowercase__   					=			self.get_timesteps(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_  )
							lowercase__   					=			timesteps[:1].repeat(batch_size * num_images_per_prompt  )
							lowercase__					,    lowercase__   					=			downscale_height_and_width(UpperCamelCase_,   UpperCamelCase_,   self.movq_scale_factor  )
							lowercase__   					=			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
										lowercase__   					=			torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

										lowercase__   					=			{"image_embeds": image_embeds}
										lowercase__   					=			self.unet(
										    sample=UpperCamelCase_,   timestep=UpperCamelCase_,   encoder_hidden_states=UpperCamelCase_,   added_cond_kwargs=UpperCamelCase_,   return_dict=UpperCamelCase_,   )[0]

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

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

							# post-processing
							lowercase__   					=			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"]:
										lowercase__   					=			image * 0.5 + 0.5
										lowercase__   					=			image.clamp(0,   1  )
										lowercase__   					=			image.cpu().permute(0,   2,   3,   1  ).float().numpy()

							if output_type == "pil":
										lowercase__   					=			self.numpy_to_pil(UpperCamelCase_  )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=UpperCamelCase_  )









import os
from collections.abc import Iterator



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "."					):
			for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			[d for d in dir_names if d != "scripts" and d[0] not in "._"]
						for filename in filenames:
									if filename == "__init__.py":
												continue
									if os.path.splitext(SCREAMING_SNAKE_CASE_					)[1] in (".py", ".ipynb"):
												yield os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					).lstrip("./"					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return f'''{i * "  "}*''' if i else "\n##"



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			old_path.split(os.sep					)
			for i, new_part in enumerate(new_path.split(os.sep					)					):
						if (i + 1 > len(SCREAMING_SNAKE_CASE_					) or old_parts[i] != new_part) and new_part:
									print(f'''{md_prefix(SCREAMING_SNAKE_CASE_					)} {new_part.replace("_"  ,					" "					).title()}'''					)
			return new_path



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "."					):
			lowercase__   					=			""
			for filepath in sorted(good_file_paths(SCREAMING_SNAKE_CASE_					)					):
						lowercase__					,    lowercase__   					=			os.path.split(SCREAMING_SNAKE_CASE_					)
						if filepath != old_path:
									lowercase__   					=			print_path(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			(filepath.count(os.sep					) + 1) if filepath else 0
						lowercase__   					=			f'''{filepath}/{filename}'''.replace(" "  ,					"%20"					)
						lowercase__   					=			os.path.splitext(filename.replace("_"  ,					" "					).title()					)[0]
						print(f'''{md_prefix(SCREAMING_SNAKE_CASE_					)} [{filename}]({url})'''					)


if __name__ == "__main__":
							print_directory_md(""".""")




