code: 
  
  
  
  
  
  
  """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}
  
style_context: 
  
  
  
  
  
  
  """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
  