
from sklearn.metrics import mean_absolute_error

import datasets


UpperCAmelCase__				:  Optional[int]       					=				'''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

UpperCAmelCase__				:  List[Any]       					=				'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


UpperCAmelCase__				:  Optional[Any]       					=				'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,     _KWARGS_DESCRIPTION   )
class 			UpperCamelCase_			(						datasets.Metric   ):




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	int:
										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(self._get_feature_types())     ,						reference_urls=[
										        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
										    ]     ,						)
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										if self.config_name == "multilist":
													return {
													    "predictions": datasets.Sequence(datasets.Value('float')),
													    "references": datasets.Sequence(datasets.Value('float')),
													}
										else:
													return {
													    "predictions": datasets.Value('float'),
													    "references": datasets.Value('float'),
													}






							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase=None     ,						UpperCamelCase="uniform_average") ->	List[str]:
										UpperCamelCase__    :		Tuple     =				mean_absolute_error(UpperCamelCase     ,						UpperCamelCase     ,						sample_weight=UpperCamelCase     ,						multioutput=UpperCamelCase)

										return {"mae": mae_score}





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__				:  Optional[int]       					=				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__				:  Optional[int]       					=				'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=8 )	->     List[Any]:
			UpperCamelCase__    :		Union[str, Any]     =				height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCamelCase__    :		Tuple     =				width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=512				,    __SCREAMING_SNAKE_CASE=512 )	->     Union[str, Any]:
			UpperCamelCase__    :		List[str]     =				pil_image.resize((w, h)				,    resample=Image.BICUBIC				,    reducing_gap=1 )
			UpperCamelCase__    :		List[str]     =				np.array(pil_image.convert('RGB' ) )
			UpperCamelCase__    :		Tuple     =				arr.astype(np.floataa ) / 127.5 - 1
			UpperCamelCase__    :		Tuple     =				np.transpose(__SCREAMING_SNAKE_CASE				,    [2, 0, 1] )
			UpperCamelCase__    :		int     =				torch.from_numpy(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
			return image



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						) ->	Any:
										super().__init__()

										self.register_modules(
										    unet=UpperCamelCase     ,						scheduler=UpperCamelCase     ,						movq=UpperCamelCase     ,						)
										UpperCamelCase__    :		str     =				2 ** (len(self.movq.config.block_out_channels) - 1)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
										# get the original timestep using init_timestep
										UpperCamelCase__    :		str     =				min(int(num_inference_steps * strength)     ,						UpperCamelCase)

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

										return timesteps, num_inference_steps - t_start
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase=None) ->	Optional[Any]:
										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)}""")

										UpperCamelCase__    :		int     =				image.to(device=UpperCamelCase     ,						dtype=UpperCamelCase)

										UpperCamelCase__    :		int     =				batch_size * num_images_per_prompt

										if image.shape[1] == 4:
													UpperCamelCase__    :		Tuple     =				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):
																UpperCamelCase__    :		Union[str, Any]     =				[
																    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(UpperCamelCase)
																]
																UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)
													else:
																UpperCamelCase__    :		Any     =				self.movq.encode(UpperCamelCase).latent_dist.sample(UpperCamelCase)

													UpperCamelCase__    :		List[Any]     =				self.movq.config.scaling_factor * init_latents

										UpperCamelCase__    :		List[Any]     =				torch.cat([init_latents]     ,						dim=0)

										UpperCamelCase__    :		Union[str, Any]     =				init_latents.shape
										UpperCamelCase__    :		Union[str, Any]     =				randn_tensor(UpperCamelCase     ,						generator=UpperCamelCase     ,						device=UpperCamelCase     ,						dtype=UpperCamelCase)

										# get latents
										UpperCamelCase__    :		Optional[int]     =				self.scheduler.add_noise(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				init_latents

										return latents
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	str:
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError('Please install accelerate via `pip install accelerate`')

										UpperCamelCase__    :		Any     =				torch.device(F"""cuda:{gpu_id}""")

										UpperCamelCase__    :		int     =				[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(UpperCamelCase     ,						UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	Tuple:
										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.')

										UpperCamelCase__    :		int     =				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)

										UpperCamelCase__    :		Dict     =				None
										for cpu_offloaded_model in [self.unet, self.movq]:
													UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				cpu_offload_with_hook(UpperCamelCase     ,						UpperCamelCase     ,						prev_module_hook=UpperCamelCase)

										# We'll offload the last model manually.
										UpperCamelCase__    :		List[Any]     =				hook
							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        lowerCAmelCase__ (				self) ->	Optional[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     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase = 5_12     ,						UpperCamelCase = 5_12     ,						UpperCamelCase = 1_00     ,						UpperCamelCase = 4.0     ,						UpperCamelCase = 0.3     ,						UpperCamelCase = 1     ,						UpperCamelCase = None     ,						UpperCamelCase = "pil"     ,						UpperCamelCase = True     ,						) ->	Optional[Any]:
										UpperCamelCase__    :		List[Any]     =				self._execution_device

										UpperCamelCase__    :		Union[str, Any]     =				guidance_scale > 1.0

										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Dict     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		List[Any]     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)

										UpperCamelCase__    :		Optional[Any]     =				image_embeds.shape[0]

										if do_classifier_free_guidance:
													UpperCamelCase__    :		List[str]     =				image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		Any     =				negative_image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		str     =				hint.repeat_interleave(UpperCamelCase     ,						dim=0)

										UpperCamelCase__    :		int     =				torch.cat([negative_image_embeds, image_embeds]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)
										UpperCamelCase__    :		Tuple     =				torch.cat([hint, hint]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)

										if not isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Tuple     =				[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""")

										UpperCamelCase__    :		List[str]     =				torch.cat([prepare_image(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) for i in image]     ,						dim=0)
										UpperCamelCase__    :		int     =				image.to(dtype=image_embeds.dtype     ,						device=UpperCamelCase)

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

													UpperCamelCase__    :		List[str]     =				{'image_embeds': image_embeds, 'hint': hint}
													UpperCamelCase__    :		List[Any]     =				self.unet(
													    sample=UpperCamelCase     ,						timestep=UpperCamelCase     ,						encoder_hidden_states=UpperCamelCase     ,						added_cond_kwargs=UpperCamelCase     ,						return_dict=UpperCamelCase     ,						)[0]

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

													# compute the previous noisy sample x_t -> x_t-1

													UpperCamelCase__    :		Union[str, Any]     =				self.scheduler.step(
													    UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						generator=UpperCamelCase     ,						)[0]

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

										if output_type == "pil":
													UpperCamelCase__    :		Dict     =				self.numpy_to_pil(UpperCamelCase)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=UpperCamelCase)



