



from sklearn.metrics import precision_score

import datasets


__UpperCAmelCase						  =     '''
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
'''


__UpperCAmelCase						  =     '''
Args:
    predictions (`list` of `int`): Predicted class labels.
    references (`list` of `int`): Actual class labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.
    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.

        - 0: Returns 0 when there is a zero division.
        - 1: Returns 1 when there is a zero division.
        - \'warn\': Raises warnings and then returns 0 when there is a zero division.

Returns:
    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.

Examples:

    Example 1-A simple binary example
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'precision\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'precision\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(results)
        {\'precision\': 0.23529411764705882}

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'precision\': 0.3333333333333333}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)
        >>> print([round(res, 2) for res in results[\'precision\']])
        [0.67, 0.0, 0.0]
'''


__UpperCAmelCase						  =     '''
@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}
}
'''


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


						def   _lowerCamelCase			( self						:  str			):
													return datasets.MetricInfo(
													    description=_DESCRIPTION      ,       citation=_CITATION      ,       inputs_description=_KWARGS_DESCRIPTION      ,       features=datasets.Features(
													        {
													            "predictions": datasets.Sequence(datasets.Value("int32"			)			),
													            "references": datasets.Sequence(datasets.Value("int32"			)			),
													        }
													        if self.config_name == "multilabel"
													        else {
													            "predictions": datasets.Value("int32"			),
													            "references": datasets.Value("int32"			),
													        }			)      ,       reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html"]      ,       )







						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  List[Any]      ,       _a						:  Union[str, Any]      ,       _a						:  int=None      ,       _a						:  List[Any]=1      ,       _a						:  Optional[int]="binary"      ,       _a						:  Union[str, Any]=None      ,       _a						:  Tuple="warn"      ,       ):
													a__:       Optional[int]											=precision_score(
													    _a      ,       _a      ,       labels=_a      ,       pos_label=_a      ,       average=_a      ,       sample_weight=_a      ,       zero_division=_a      ,       )
													return {"precision": float(_a			) if score.size == 1 else score}










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


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


__UpperCAmelCase						  =     '''
    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__ (		_a   ):


						def __init__( self						:  str      ,       _a						:  UNetaDModel      ,       _a						:  CMStochasticIterativeScheduler			):
													super().__init__()

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

													a__:       Union[str, Any]											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  Any=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"			)

													a__:       Optional[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)

													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   _lowerCamelCase			( self						:  List[str]      ,       _a						:  Optional[int]=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__:       Union[str, 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__:       Optional[Any]											=None
													for cpu_offloaded_model in [self.unet]:
																				a__							,			a__:       Optional[Any]											=cpu_offload_with_hook(_a      ,       _a      ,       prev_module_hook=_a			)

													if self.safety_checker is not None:
																				a__							,			a__:       Optional[Any]											=cpu_offload_with_hook(self.safety_checker      ,       _a      ,       prev_module_hook=_a			)

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


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   _lowerCamelCase			( self						:  Tuple			):
													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   _lowerCamelCase			( self						:  Dict      ,       _a						:  List[Any]      ,       _a						:  Optional[int]      ,       _a						:  Dict      ,       _a						:  List[Any]      ,       _a						:  List[str]      ,       _a						:  Union[str, Any]      ,       _a						:  str      ,       _a						:  int=None			):
													a__:       List[str]											=(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:
																				a__:       Dict											=randn_tensor(_a      ,       generator=_a      ,       device=_a      ,       dtype=_a			)
													else:
																				a__:       List[Any]											=latents.to(device=_a      ,       dtype=_a			)

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


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  torch.FloatTensor      ,       _a						:  str = "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
													a__:       Tuple											=(sample / 2 + 0.5).clamp(0      ,       1			)
													if output_type == "pt":
																				return sample

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

													# Output_type must be 'pil'
													a__:       List[Any]											=self.numpy_to_pil(_a			)
													return sample


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


						def   _lowerCamelCase			( self						:  Union[str, Any]      ,       _a						:  Optional[Any]      ,       _a						:  Optional[int]      ,       _a						:  List[Any]      ,       _a						:  List[str]      ,       _a						:  Optional[int]      ,       _a						:  Optional[Any]			):
													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__:       Tuple											=(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						:  Dict      ,       _a						:  int = 1      ,       _a						:  Optional[Union[torch.Tensor, List[int], int]] = None      ,       _a						:  int = 1      ,       _a						:  List[int] = None      ,       _a						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,       _a						:  Optional[torch.FloatTensor] = None      ,       _a						:  Optional[str] = "pil"      ,       _a						:  bool = True      ,       _a						:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,       _a						:  int = 1      ,       ):
													a__:       int											=self.unet.config.sample_size
													a__:       Union[str, Any]											=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)
													a__:       Tuple											=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
													a__:       int											=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			)
																				a__:       str											=self.scheduler.timesteps
																				a__:       List[str]											=len(_a			)
													else:
																				self.scheduler.set_timesteps(_a			)
																				a__:       Optional[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			):
																											a__:       Any											=self.scheduler.scale_model_input(_a      ,       _a			)
																											a__:       Dict											=self.unet(_a      ,       _a      ,       class_labels=_a      ,       return_dict=_a			)[0]

																											a__:       int											=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
													a__:       List[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			)





