




"""simple docstring"""

from sklearn.metrics import mean_absolute_error

import datasets


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

_lowerCamelCase					:     List[str]				       =    '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


_lowerCamelCase					:     Optional[Any]				       =    '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'

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







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					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    a_					(							self				:   str    ):



					"""simple docstring"""

					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    a_					(							self				:   str ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Optional[Any]=None ,       _lowerCamelCase				:   Optional[Any]="uniform_average"    ):



					"""simple docstring"""

					A_					:			int						=						mean_absolute_error(_lowerCamelCase ,       _lowerCamelCase ,       sample_weight=_lowerCamelCase ,       multioutput=_lowerCamelCase    )

					return {"mae": mae_score}











"""simple docstring"""

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


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


_lowerCamelCase					:     Any				       =    '\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   UNetaDModel ,       _lowerCamelCase				:   CMStochasticIterativeScheduler    ):



					"""simple docstring"""

					super().__init__()

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

					A_					:			Optional[Any]						=						None







				def    a_					(							self				:   Any ,       _lowerCamelCase				:   Any=0    ):



					"""simple docstring"""

					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'''    )

					A_					:			Tuple						=						torch.device(F"""cuda:{gpu_id}"""    )

					if self.device.type != "cpu":
						self.to('''cpu''' ,       silence_dtype_warnings=_lowerCamelCase    )
						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(_lowerCamelCase ,       _lowerCamelCase    )

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







				def    a_					(							self				:   Optional[int] ,       _lowerCamelCase				:   Optional[Any]=0    ):



					"""simple docstring"""

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

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

					A_					:			Any						=						None
					for cpu_offloaded_model in [self.unet]:
						A_				,	A_					:			Optional[Any]						=						cpu_offload_with_hook(_lowerCamelCase ,       _lowerCamelCase ,       prev_module_hook=_lowerCamelCase    )

					if self.safety_checker is not None:
						A_				,	A_					:			List[Any]						=						cpu_offload_with_hook(self.safety_checker ,       _lowerCamelCase ,       prev_module_hook=_lowerCamelCase    )

					# We'll offload the last model manually.
					A_					:			Union[str, Any]						=						hook







				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					if not hasattr(self.unet ,       '''_hf_hook'''    ):
						return self.device
					for module in self.unet.modules():
						if (
						    hasattr(_lowerCamelCase ,       '''_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    a_					(							self				:   List[Any] ,       _lowerCamelCase				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   Optional[int] ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   str=None    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						(batch_size, num_channels, height, width)
					if isinstance(_lowerCamelCase ,       _lowerCamelCase    ) and len(_lowerCamelCase    ) != batch_size:
						raise ValueError(
						    F"""You have passed a list of generators of length {len(_lowerCamelCase    )}, 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:
						A_					:			Dict						=						randn_tensor(_lowerCamelCase ,       generator=_lowerCamelCase ,       device=_lowerCamelCase ,       dtype=_lowerCamelCase    )
					else:
						A_					:			List[Any]						=						latents.to(device=_lowerCamelCase ,       dtype=_lowerCamelCase    )

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







				def    a_					(							self				:   int ,       _lowerCamelCase				:   torch.FloatTensor ,       _lowerCamelCase				:   str = "pil"    ):



					"""simple docstring"""

					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
					A_					:			List[Any]						=						(sample / 2 + 0.5).clamp(0 ,       1    )
					if output_type == "pt":
						return sample

					# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
					A_					:			Union[str, Any]						=						sample.cpu().permute(0 ,       2 ,       3 ,       1    ).numpy()
					if output_type == "np":
						return sample

					# Output_type must be 'pil'
					A_					:			Optional[Any]						=						self.numpy_to_pil(_lowerCamelCase    )
					return sample







				def    a_					(							self				:   str ,       _lowerCamelCase				:   Any ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Dict=None    ):



					"""simple docstring"""

					if self.unet.config.num_class_embeds is not None:
						if isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
							A_					:			int						=						torch.tensor(_lowerCamelCase ,       dtype=torch.int    )
						elif isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
							assert batch_size == 1, "Batch size must be 1 if classes is an int"
							A_					:			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
							A_					:			Any						=						torch.randint(0 ,       self.unet.config.num_class_embeds ,       size=(batch_size,)    )
						A_					:			Tuple						=						class_labels.to(_lowerCamelCase    )
					else:
						A_					:			Union[str, Any]						=						None
					return class_labels







				def    a_					(							self				:   List[Any] ,       _lowerCamelCase				:   Any ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   Optional[Any] ,       _lowerCamelCase				:   Any ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Optional[Any]    ):



					"""simple docstring"""

					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:
						A_					:			List[Any]						=						(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(_lowerCamelCase ,       _lowerCamelCase    ) or callback_steps <= 0)
					):
						raise ValueError(
						    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
						    F""" {type(_lowerCamelCase    )}."""    )







				@torch.no_grad()
				@replace_example_docstring(_lowerCamelCase    )
				def __call__(							self				:   str ,       _lowerCamelCase				:   int = 1 ,       _lowerCamelCase				:   Optional[Union[torch.Tensor, List[int], int]] = None ,       _lowerCamelCase				:   int = 1 ,       _lowerCamelCase				:   List[int] = None ,       _lowerCamelCase				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       _lowerCamelCase				:   Optional[torch.FloatTensor] = None ,       _lowerCamelCase				:   Optional[str] = "pil" ,       _lowerCamelCase				:   bool = True ,       _lowerCamelCase				:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,       _lowerCamelCase				:   int = 1 ,       ):



					"""simple docstring"""

					A_					:			Dict						=						self.unet.config.sample_size
					A_					:			int						=						self._execution_device

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

					# 2. Prepare image latents
					# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
					A_					:			Optional[int]						=						self.prepare_latents(
					    batch_size=_lowerCamelCase ,       num_channels=self.unet.config.in_channels ,       height=_lowerCamelCase ,       width=_lowerCamelCase ,       dtype=self.unet.dtype ,       device=_lowerCamelCase ,       generator=_lowerCamelCase ,       latents=_lowerCamelCase ,       )

					# 3. Handle class_labels for class-conditional models
					A_					:			Tuple						=						self.prepare_class_labels(_lowerCamelCase ,       _lowerCamelCase ,       class_labels=_lowerCamelCase    )

					# 4. Prepare timesteps
					if timesteps is not None:
						self.scheduler.set_timesteps(timesteps=_lowerCamelCase ,       device=_lowerCamelCase    )
						A_					:			Optional[int]						=						self.scheduler.timesteps
						A_					:			Dict						=						len(_lowerCamelCase    )
					else:
						self.scheduler.set_timesteps(_lowerCamelCase    )
						A_					:			Dict						=						self.scheduler.timesteps

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

							A_					:			Optional[int]						=						self.scheduler.step(_lowerCamelCase ,       _lowerCamelCase ,       _lowerCamelCase ,       generator=_lowerCamelCase    )[0]

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

        # 6. Post-process image sample
					A_					:			Union[str, Any]						=						self.postprocess_image(_lowerCamelCase ,       output_type=_lowerCamelCase    )

					# 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=_lowerCamelCase    )





