




import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__A        =    list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__A        =    [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f'''{len(upper_files)} files contain uppercase characters:''')
					print("\n".join(upper_files) + "\n")

__A        =    [file for file in filepaths if " " in file]
if space_files:
					print(f'''{len(space_files)} files contain space characters:''')
					print("\n".join(space_files) + "\n")

__A        =    [file for file in filepaths if "-" in file]
if hyphen_files:
					print(f'''{len(hyphen_files)} files contain hyphen characters:''')
					print("\n".join(hyphen_files) + "\n")

__A        =    [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f'''{len(nodir_files)} files are not in a directory:''')
					print("\n".join(nodir_files) + "\n")

__A        =    len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)









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

UpperCAmelCase						:   Dict			  =     '''
    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  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a=8  )      ->      Tuple:
					__A	:						List[str]       =				height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__A	:						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  _SCREAMING_SNAKE_CASE				(   a					,						a=5_12					,						a=5_12  )      ->      int:
					__A	:						Optional[Any]       =				pil_image.resize((w, h)					,						resample=Image.BICUBIC					,						reducing_gap=1  )
					__A	:						Union[str, Any]       =				np.array(pil_image.convert('RGB'  )  )
					__A	:						Optional[int]       =				arr.astype(np.floataa  ) / 127.5 - 1
					__A	:						int       =				np.transpose(a					,						[2, 0, 1]  )
					__A	:						Tuple       =				torch.from_numpy(a  ).unsqueeze(0  )
					return image





class 						_A(					snake_case__   ):


				"""simple docstring"""


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

									self.register_modules(
									    unet=_A     ,					scheduler=_A     ,					movq=_A     ,					)
									__A	:						Tuple       =				2 ** (len(self.movq.config.block_out_channels     ) - 1)



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									# get the original timestep using init_timestep
									__A	:						Optional[int]       =				min(int(num_inference_steps * strength     )     ,					_A     )

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

									return timesteps, num_inference_steps - t_start



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A=None     ):
									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     )}"""     )

									__A	:						Union[str, Any]       =				image.to(device=_A     ,					dtype=_A     )

									__A	:						Optional[Any]       =				batch_size * num_images_per_prompt

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

														__A	:						Tuple       =				self.movq.config.scaling_factor * init_latents

									__A	:						Optional[int]       =				torch.cat([init_latents]     ,					dim=0     )

									__A	:						Union[str, Any]       =				init_latents.shape
									__A	:						List[str]       =				randn_tensor(_A     ,					generator=_A     ,					device=_A     ,					dtype=_A     )

									# get latents
									__A	:						Optional[Any]       =				self.scheduler.add_noise(_A     ,					_A     ,					_A     )

									__A	:						Optional[int]       =				init_latents

									return latents



				def 					UpperCAmelCase_		(			self     ,					_A=0     ):
									if is_accelerate_available():
														from accelerate import cpu_offload
									else:
														raise ImportError('Please install accelerate via `pip install accelerate`'     )

									__A	:						Optional[int]       =				torch.device(F"""cuda:{gpu_id}"""     )

									__A	:						Union[str, Any]       =				[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(_A     ,					_A     )



				def 					UpperCAmelCase_		(			self     ,					_A=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.'     )

									__A	:						List[Any]       =				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)

									__A	:						int       =				None
									for cpu_offloaded_model in [self.unet, self.movq]:
														__A    ,				__A	:						Optional[int]       =				cpu_offload_with_hook(_A     ,					_A     ,					prev_module_hook=_A     )

									# We'll offload the last model manually.
									__A	:						List[str]       =				hook



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 					UpperCAmelCase_		(			self     ):
									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     ,					):
									__A	:						List[Any]       =				self._execution_device

									__A	:						Optional[Any]       =				guidance_scale > 1.0

									if isinstance(_A     ,					_A     ):
														__A	:						Optional[Any]       =				torch.cat(_A     ,					dim=0     )
									__A	:						Tuple       =				image_embeds.shape[0]
									if isinstance(_A     ,					_A     ):
														__A	:						List[Any]       =				torch.cat(_A     ,					dim=0     )

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

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

									if not isinstance(_A     ,					_A     ):
														__A	:						List[Any]       =				[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"""     )

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

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

														__A	:						Dict       =				{'image_embeds': image_embeds}
														__A	:						List[str]       =				self.unet(
														    sample=_A     ,					timestep=_A     ,					encoder_hidden_states=_A     ,					added_cond_kwargs=_A     ,					return_dict=_A     ,					)[0]

														if do_classifier_free_guidance:
																			__A    ,				__A	:						Dict       =				noise_pred.split(latents.shape[1]     ,					dim=1     )
																			__A    ,				__A	:						Optional[Any]       =				noise_pred.chunk(2     )
																			__A    ,				__A	:						List[str]       =				variance_pred.chunk(2     )
																			__A	:						str       =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			__A	:						List[str]       =				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    ,				__A	:						Optional[Any]       =				noise_pred.split(latents.shape[1]     ,					dim=1     )

														# compute the previous noisy sample x_t -> x_t-1
														__A	:						List[str]       =				self.scheduler.step(
														    _A     ,					_A     ,					_A     ,					generator=_A     ,					)[0]

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

									if output_type == "pil":
														__A	:						Any       =				self.numpy_to_pil(_A     )

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=_A     )




