


from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__magic_name__       					=      '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




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

							self.register_modules(
							    unet=a_				,							scheduler=a_				,							)

							lowerCamelCase_			:							List[str]      =       None




						def 					_UpperCamelCase			(   self				,							a_=0     ):
							if is_accelerate_available() and is_accelerate_version(">="				,							"0.14.0"     ):
								from accelerate import cpu_offload
							else:
								raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"     )

							lowerCamelCase_			:							Dict      =       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)

							for cpu_offloaded_model in [self.unet]:
								cpu_offload(a_				,							a_     )

							if self.safety_checker is not None:
								cpu_offload(self.safety_checker				,							execution_device=a_				,							offload_buffers=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."     )

							lowerCamelCase_			:							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)

							lowerCamelCase_			:							Tuple      =       None
							for cpu_offloaded_model in [self.unet]:
								lowerCamelCase_   ,lowerCamelCase_			:							str      =       cpu_offload_with_hook(a_				,							a_				,							prev_module_hook=a_     )

							if self.safety_checker is not None:
								lowerCamelCase_   ,lowerCamelCase_			:							List[str]      =       cpu_offload_with_hook(self.safety_checker				,							a_				,							prev_module_hook=a_     )

							# We'll offload the last model manually.
							lowerCamelCase_			:							Optional[Any]      =       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




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_=None     ):
							lowerCamelCase_			:							Optional[int]      =       (batch_size, num_channels, height, width)
							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."""     )

							if latents is None:
								lowerCamelCase_			:							int      =       randn_tensor(a_				,							generator=a_				,							device=a_				,							dtype=a_     )
							else:
								lowerCamelCase_			:							Dict      =       latents.to(device=a_				,							dtype=a_     )

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCamelCase_			:							Tuple      =       latents * self.scheduler.init_noise_sigma
							return latents




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = "pil"     ):
							if output_type not in ["pt", "np", "pil"]:
								raise ValueError(
								    F"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""     )

							# Equivalent to diffusers.VaeImageProcessor.denormalize
							lowerCamelCase_			:							Optional[Any]      =       (sample / 2 + 0.5).clamp(0				,							1     )
							if output_type == "pt":
								return sample

							# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
							lowerCamelCase_			:							Tuple      =       sample.cpu().permute(0				,							2				,							3				,							1     ).numpy()
							if output_type == "np":
								return sample

							# Output_type must be 'pil'
							lowerCamelCase_			:							List[str]      =       self.numpy_to_pil(a_     )
							return sample




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_=None     ):
							if self.unet.config.num_class_embeds is not None:
								if isinstance(a_				,							a_     ):
									lowerCamelCase_			:							Tuple      =       torch.tensor(a_				,							dtype=torch.int     )
								elif isinstance(a_				,							a_     ):
									assert batch_size == 1, "Batch size must be 1 if classes is an int"
									lowerCamelCase_			:							Optional[int]      =       torch.tensor([class_labels]				,							dtype=torch.int     )
								elif class_labels is None:
									# Randomly generate batch_size class labels
									# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
									lowerCamelCase_			:							str      =       torch.randint(0				,							self.unet.config.num_class_embeds				,							size=(batch_size,)     )
								lowerCamelCase_			:							Optional[Any]      =       class_labels.to(a_     )
							else:
								lowerCamelCase_			:							List[Any]      =       None
							return class_labels




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_				,							a_				,							a_     ):
							if num_inference_steps is None and timesteps is None:
								raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."     )

							if num_inference_steps is not None and timesteps is not None:
								logger.warning(
								    F"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
								    " `timesteps` will be used over `num_inference_steps`."     )

							if latents is not None:
								lowerCamelCase_			:							str      =       (batch_size, 3, img_size, img_size)
								if latents.shape != expected_shape:
									raise ValueError(F"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""     )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(a_				,							a_     ) or callback_steps <= 0)
							):
								raise ValueError(
								    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
								    F""" {type(a_     )}."""     )




						@torch.no_grad()
						@replace_example_docstring(a_     )
						def __call__(   self				,							a_ = 1				,							a_ = None				,							a_ = 1				,							a_ = None				,							a_ = None				,							a_ = None				,							a_ = "pil"				,							a_ = True				,							a_ = None				,							a_ = 1				,							):
							lowerCamelCase_			:							Dict      =       self.unet.config.sample_size
							lowerCamelCase_			:							int      =       self._execution_device

							# 1. Check inputs
							self.check_inputs(a_				,							a_				,							a_				,							a_				,							a_				,							a_     )

							# 2. Prepare image latents
							# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
							lowerCamelCase_			:							Any      =       self.prepare_latents(
							    batch_size=a_				,							num_channels=self.unet.config.in_channels				,							height=a_				,							width=a_				,							dtype=self.unet.dtype				,							device=a_				,							generator=a_				,							latents=a_				,							)

							# 3. Handle class_labels for class-conditional models
							lowerCamelCase_			:							str      =       self.prepare_class_labels(a_				,							a_				,							class_labels=a_     )

							# 4. Prepare timesteps
							if timesteps is not None:
								self.scheduler.set_timesteps(timesteps=a_				,							device=a_     )
								lowerCamelCase_			:							Optional[int]      =       self.scheduler.timesteps
								lowerCamelCase_			:							List[Any]      =       len(a_     )
							else:
								self.scheduler.set_timesteps(a_     )
								lowerCamelCase_			:							int      =       self.scheduler.timesteps

							# 5. Denoising loop
							# Multistep sampling: implements Algorithm 1 in the paper
							with self.progress_bar(total=a_     ) as progress_bar:
								for i, t in enumerate(a_     ):
									lowerCamelCase_			:							Dict      =       self.scheduler.scale_model_input(a_				,							a_     )
									lowerCamelCase_			:							List[Any]      =       self.unet(a_				,							a_				,							class_labels=a_				,							return_dict=a_     )[0]

									lowerCamelCase_			:							Dict      =       self.scheduler.step(a_				,							a_				,							a_				,							generator=a_     )[0]

									# call the callback, if provided
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
										callback(a_				,							a_				,							a_     )

        # 6. Post-process image sample
							lowerCamelCase_			:							str      =       self.postprocess_image(a_				,							output_type=a_     )

							# Offload last model to CPU
							if hasattr(self				,							"final_offload_hook"     ) and self.final_offload_hook is not None:
								self.final_offload_hook.offload()

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=a_     )





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCAmelCase_))







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-comp-10.jpg")      ,					1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")


if __name__ == "__main__":
							main()

