

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		:       Union[str, 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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=8				):







							'''simple docstring'''






							snake_case_          =   height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							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				):







							'''simple docstring'''






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







class 			lowercase    (	lowercase_      ):




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

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




				def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
											# get the original timestep using init_timestep
											snake_case_          =   min(int(num_inference_steps * strength	)				,       snake_case	)

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

											return timesteps, num_inference_steps - t_start




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=None	):
											if not isinstance(snake_case				,       (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(snake_case	)}'''	)

											snake_case_          =   image.to(device=snake_case				,       dtype=snake_case	)

											snake_case_          =   batch_size * num_images_per_prompt

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

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

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

																		snake_case_          =   self.movq.config.scaling_factor * init_latents

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

											snake_case_          =   init_latents.shape
											snake_case_          =   randn_tensor(snake_case				,       generator=snake_case				,       device=snake_case				,       dtype=snake_case	)

											# get latents
											snake_case_          =   self.scheduler.add_noise(snake_case				,       snake_case				,       snake_case	)

											snake_case_          =   init_latents

											return latents




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

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

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




				def    a							( self				,       snake_case=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.'	)

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

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

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

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




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def    a							( self	):
											if not hasattr(self.unet				,       '_hf_hook'	):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(snake_case				,       '_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(snake_case	)
				def __call__( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case = 512				,       snake_case = 512				,       snake_case = 100				,       snake_case = 4.0				,       snake_case = 0.3				,       snake_case = 1				,       snake_case = None				,       snake_case = "pil"				,       snake_case = True				,       ):
											snake_case_          =   self._execution_device

											snake_case_          =   guidance_scale > 1.0

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

											if do_classifier_free_guidance:
																		snake_case_          =   image_embeds.repeat_interleave(snake_case				,       dim=0	)
																		snake_case_          =   negative_image_embeds.repeat_interleave(snake_case				,       dim=0	)

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

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

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

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

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

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

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

											# post-processing
											snake_case_          =   self.movq.decode(snake_case				,       force_not_quantize=snake_case	)['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"]:
																		snake_case_          =   image * 0.5 + 0.5
																		snake_case_          =   image.clamp(0				,       1	)
																		snake_case_          =   image.cpu().permute(0				,       2				,       3				,       1	).float().numpy()

											if output_type == "pil":
																		snake_case_          =   self.numpy_to_pil(snake_case	)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case	)







def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   len(UpperCamelCase__				)
							for _ in range(UpperCamelCase__				):
														for i in range(_ % 2       ,							arr_size - 1       ,							2				):
																					if arr[i + 1] < arr[i]:
																												snake_case_    ,   snake_case_          =   arr[i + 1], arr[i]

							return arr


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	list(range(10, 0, -1))
					print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')




