






"""simple docstring"""

from sklearn.metrics import precision_score

import datasets


lowercase__      =				"""
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).
"""


lowercase__      =				"""
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]
"""


lowercase__      =				"""
@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    ):





		'''simple docstring'''






		def      lowerCamelCase (     self							:			List[Any]			):
				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[Any]		,    a_							:			Tuple		,    a_							:			List[Any]		,    a_							:			Union[str, Any]=None		,    a_							:			Any=1		,    a_							:			str="binary"		,    a_							:			Optional[int]=None		,    a_							:			Union[str, Any]="warn"		,    ):
				lowerCAmelCase_     :							List[str]             =    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}








"""simple docstring"""

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       torch.FloatTensor
		a_			:       torch.FloatTensor







def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=0.9_99   , __UpperCamelCase="cosine"   , )		->					Any:

		"""simple docstring"""







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__UpperCamelCase ):
						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(__UpperCamelCase ):
						return math.exp(t * -12.0 )

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

		lowerCAmelCase_     :							List[str]             =    []
		for i in range(__UpperCamelCase ):
				lowerCAmelCase_     :							int             =    i / num_diffusion_timesteps
				lowerCAmelCase_     :							Optional[Any]             =    (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )   , __UpperCamelCase ) )
		return torch.tensor(__UpperCamelCase   , dtype=torch.floataa )



class 	__lowerCamelCase       (				A__				,							A__    ):





		'''simple docstring'''






		a_			:       Tuple      =							1





		@register_to_config
		def __init__(     self							:			Optional[int]		,    a_							:			int = 10_00		,    a_							:			float = 0.0001		,    a_							:			float = 0.02		,    a_							:			str = "linear"		,    a_							:			float = 0.0		,    a_							:			Optional[np.ndarray] = None		,    a_							:			bool = True		,    ):
				if trained_betas is not None:
						lowerCAmelCase_     :							Tuple             =    torch.from_numpy(a_			)
				elif beta_schedule == "linear":
						lowerCAmelCase_     :							int             =    torch.linspace(a_		,    a_		,    a_		,    dtype=torch.floataa			)
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						lowerCAmelCase_     :							Optional[Any]             =    (
						    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    a_		,    dtype=torch.floataa			) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						lowerCAmelCase_     :							Tuple             =    betas_for_alpha_bar(a_			)
				elif beta_schedule == "sigmoid":
						# GeoDiff sigmoid schedule
						lowerCAmelCase_     :							List[str]             =    torch.linspace(-6		,    6		,    a_			)
						lowerCAmelCase_     :							int             =    torch.sigmoid(a_			) * (beta_end - beta_start) + beta_start
				else:
						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

				lowerCAmelCase_     :							Union[str, Any]             =    1.0 - self.betas
				lowerCAmelCase_     :							Optional[int]             =    torch.cumprod(self.alphas		,    dim=0			)
				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)

				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)

				# standard deviation of the initial noise distribution
				lowerCAmelCase_     :							List[str]             =    1.0

				# setable values
				lowerCAmelCase_     :							Dict             =    None
				lowerCAmelCase_     :							List[str]             =    torch.from_numpy(np.arange(0		,    a_			)[::-1].copy()			)

				lowerCAmelCase_     :							int             =    eta





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			Optional[int] = None			):
				return sample





		def      lowerCamelCase (     self							:			Dict		,    a_							:			int		,    a_							:			int = 10		,    a_							:			int = 10		,    a_							:			Union[str, torch.device] = None		,    ):
				lowerCAmelCase_     :							Union[str, Any]             =    min(self.config.num_train_timesteps		,    a_			)
				lowerCAmelCase_     :							Dict             =    num_inference_steps

				lowerCAmelCase_     :							List[str]             =    []

				lowerCAmelCase_     :							Dict             =    {}
				for j in range(0		,    num_inference_steps - jump_length		,    a_			):
						lowerCAmelCase_     :							Dict             =    jump_n_sample - 1

				lowerCAmelCase_     :							List[Any]             =    num_inference_steps
				while t >= 1:
						lowerCAmelCase_     :							int             =    t - 1
						timesteps.append(a_			)

						if jumps.get(a_		,    0			) > 0:
								lowerCAmelCase_     :							Optional[int]             =    jumps[t] - 1
								for _ in range(a_			):
										lowerCAmelCase_     :							Any             =    t + 1
										timesteps.append(a_			)

				lowerCAmelCase_     :							List[Any]             =    np.array(a_			) * (self.config.num_train_timesteps // self.num_inference_steps)
				lowerCAmelCase_     :							int             =    torch.from_numpy(a_			).to(a_			)





		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Optional[Any]			):
				lowerCAmelCase_     :							Optional[Any]             =    t - self.config.num_train_timesteps // self.num_inference_steps

				lowerCAmelCase_     :							List[Any]             =    self.alphas_cumprod[t]
				lowerCAmelCase_     :							Dict             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCAmelCase_     :							List[str]             =    1 - alpha_prod_t
				lowerCAmelCase_     :							str             =    1 - alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
				# variance to pred_sample
				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
				# without eta.
				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
				lowerCAmelCase_     :							Dict             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			int		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			Optional[torch.Generator] = None		,    a_							:			bool = True		,    ):
				lowerCAmelCase_     :							Any             =    timestep
				lowerCAmelCase_     :							str             =    timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 1. compute alphas, betas
				lowerCAmelCase_     :							Any             =    self.alphas_cumprod[t]
				lowerCAmelCase_     :							List[str]             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCAmelCase_     :							Optional[Any]             =    1 - alpha_prod_t

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				lowerCAmelCase_     :							List[Any]             =    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
						lowerCAmelCase_     :							Optional[int]             =    torch.clamp(a_		,    -1		,    1			)

				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
				# substitute formula (7) in the algorithm coming from DDPM paper
				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
				# DDIM schedule gives the same results as DDPM with eta = 1.0
				# Noise is being reused in 7. and 8., but no impact on quality has
				# been observed.

				# 5. Add noise
				lowerCAmelCase_     :							Optional[Any]             =    model_output.device
				lowerCAmelCase_     :							List[str]             =    randn_tensor(model_output.shape		,    generator=a_		,    device=a_		,    dtype=model_output.dtype			)
				lowerCAmelCase_     :							List[Any]             =    self.eta * self._get_variance(a_			) ** 0.5

				lowerCAmelCase_     :							Optional[int]             =    0
				if t > 0 and self.eta > 0:
						lowerCAmelCase_     :							Optional[int]             =    std_dev_t * noise

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				lowerCAmelCase_     :							Union[str, Any]             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				lowerCAmelCase_     :							Dict             =    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
				lowerCAmelCase_     :							Union[str, Any]             =    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
				lowerCAmelCase_     :							Tuple             =    mask * prev_known_part + (1.0 - mask) * prev_unknown_part

				if not return_dict:
						return (
						    pred_prev_sample,
						    pred_original_sample,
						)

				return RePaintSchedulerOutput(prev_sample=a_		,    pred_original_sample=a_			)





		def      lowerCamelCase (     self							:			Dict		,    a_							:			Any		,    a_							:			Dict		,    a_							:			Optional[int]=None			):
				lowerCAmelCase_     :							Dict             =    self.config.num_train_timesteps // self.num_inference_steps

				for i in range(a_			):
						lowerCAmelCase_     :							Optional[Any]             =    self.betas[timestep + i]
						if sample.device.type == "mps":
								# randn does not work reproducibly on mps
								lowerCAmelCase_     :							str             =    randn_tensor(sample.shape		,    dtype=sample.dtype		,    generator=a_			)
								lowerCAmelCase_     :							List[str]             =    noise.to(sample.device			)
						else:
								lowerCAmelCase_     :							Tuple             =    randn_tensor(sample.shape		,    generator=a_		,    device=sample.device		,    dtype=sample.dtype			)

						# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
						lowerCAmelCase_     :							int             =    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

				return sample





		def      lowerCamelCase (     self							:			Any		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.IntTensor		,    ):
				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)





		def __len__(     self							:			Union[str, Any]			):
				return self.config.num_train_timesteps
