






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,
)


a_		:						Union[str, Any]             =    logging.get_logger(__name__)  # pylint: disable=invalid-name

a_		:						Tuple             =    '\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__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=8):
			SCREAMING_SNAKE_CASE						= height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			SCREAMING_SNAKE_CASE						= width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=512							,       _UpperCAmelCase=512):
			SCREAMING_SNAKE_CASE						= pil_image.resize((w, h)							,       resample=Image.BICUBIC							,       reducing_gap=1)
			SCREAMING_SNAKE_CASE						= np.array(pil_image.convert('RGB'))
			SCREAMING_SNAKE_CASE						= arr.astype(np.floataa) / 1_27.5 - 1
			SCREAMING_SNAKE_CASE						= np.transpose(_UpperCAmelCase							,       [2, 0, 1])
			SCREAMING_SNAKE_CASE						= torch.from_numpy(_UpperCAmelCase).unsqueeze(0)
			return image







class 		_snake_case  (  A__					):






			def __init__(       self		,					a		,					a		,					a		,					)      ->       Optional[Any]:
						super().__init__()

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Any:
						# get the original timestep using init_timestep
						SCREAMING_SNAKE_CASE						= min(int(num_inference_steps * strength)		,					a)

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

						return timesteps, num_inference_steps - t_start






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a=None)      ->       List[str]:
						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)}''')

						SCREAMING_SNAKE_CASE						= image.to(device=a		,					dtype=a)

						SCREAMING_SNAKE_CASE						= batch_size * num_images_per_prompt

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

									SCREAMING_SNAKE_CASE						= self.movq.config.scaling_factor * init_latents

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

						SCREAMING_SNAKE_CASE						= init_latents.shape
						SCREAMING_SNAKE_CASE						= randn_tensor(a		,					generator=a		,					device=a		,					dtype=a)

						# get latents
						SCREAMING_SNAKE_CASE						= self.scheduler.add_noise(a		,					a		,					a)

						SCREAMING_SNAKE_CASE						= init_latents

						return latents






			def    SCREAMING_SNAKE_CASE__    (       self		,					a=0)      ->       Tuple:
						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError('Please install accelerate via `pip install accelerate`')

						SCREAMING_SNAKE_CASE						= torch.device(f'''cuda:{gpu_id}''')

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a=0)      ->       Optional[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.')

						SCREAMING_SNAKE_CASE						= 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)

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

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






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						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		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self._execution_device

						SCREAMING_SNAKE_CASE						= guidance_scale > 1.0

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

						if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE						= image_embeds.repeat_interleave(a		,					dim=0)
									SCREAMING_SNAKE_CASE						= negative_image_embeds.repeat_interleave(a		,					dim=0)

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

						if not isinstance(a		,					a):
									SCREAMING_SNAKE_CASE						= [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''')

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

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

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

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

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

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

						if output_type == "pil":
									SCREAMING_SNAKE_CASE						= self.numpy_to_pil(a)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=a)












import os
from collections.abc import Iterator






def 				lowerCamelCase__				(_UpperCAmelCase = "."):
			for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= [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(_UpperCAmelCase)[1] in (".py", ".ipynb"):
												yield os.path.join(_UpperCAmelCase							,       _UpperCAmelCase).lstrip('./')






def 				lowerCamelCase__				(_UpperCAmelCase):
			return F'''{i * '  '}*''' if i else "\n##"






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






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


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




