
from sklearn.metrics import recall_score

import datasets


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				'\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n'


SCREAMING_SNAKE_CASE							:str							 =				'\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `\'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. Note that it can result in an F-score that is not between precision and recall.\n    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {\'recall\': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {\'recall\': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {\'recall\': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'recall\': array([1., 0., 0.])}\n'


SCREAMING_SNAKE_CASE							:Any							 =				'\n@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}\n'





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




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Dict ):
					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.recall_score.html"]							,)







	def 				UpperCamelCase_  (       self					: Any							,A					: List[str]							,A					: str							,A					: Optional[int]=None							,A					: int=1							,A					: Optional[int]="binary"							,A					: Any=None							,A					: str="warn"							,):
					__A   			= recall_score(
					    A							,A							,labels=A							,pos_label=A							,average=A							,sample_weight=A							,zero_division=A							,)
					return {"recall": float(A ) if score.size == 1 else score}

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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : torch.FloatTensor
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None




def    lowerCAmelCase_ (     __A,  __A=0.999,  __A="cosine",  )     ->				Tuple:







      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__A							):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

      elif alpha_transform_type == "exp":

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

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

      UpperCAmelCase__								=  []
      for i in range(__A							):
            UpperCAmelCase__								=  i / num_diffusion_timesteps
            UpperCAmelCase__								=  (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(__A							) / alpha_bar_fn(__A							),  __A							)							)
      return torch.tensor(__A,  dtype=torch.floataa							)







class      A							(				UpperCAmelCase_		,				UpperCAmelCase_     ):


   @register_to_config
   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						int = 1_0_0_0       ,  __UpperCAmelCase     :						str = "fixed_small_log"       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[float] = 1.0       ,  __UpperCAmelCase     :						str = "epsilon"       ,  __UpperCAmelCase     :						str = "squaredcos_cap_v2"       ,  )						->				Optional[int]:
         """simple docstring"""



         if beta_schedule != "squaredcos_cap_v2":
               raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"		)

         UpperCAmelCase__								=  betas_for_alpha_bar(__UpperCAmelCase		)

         UpperCAmelCase__								=  1.0 - self.betas
         UpperCAmelCase__								=  torch.cumprod(self.alphas       ,  dim=0		)
         UpperCAmelCase__								=  torch.tensor(1.0		)

         # standard deviation of the initial noise distribution
         UpperCAmelCase__								=  1.0

         # setable values
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  torch.from_numpy(np.arange(0       ,  __UpperCAmelCase		)[::-1].copy()		)

         UpperCAmelCase__								=  variance_type


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None		)						->				torch.FloatTensor:
         """simple docstring"""



         return sample


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Union[str, torch.device] = None		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  num_inference_steps
         UpperCAmelCase__								=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         UpperCAmelCase__								=  (np.arange(0       ,  __UpperCAmelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
         UpperCAmelCase__								=  torch.from_numpy(__UpperCAmelCase		).to(__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Optional[int]=None       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						List[str]=None		)						->				Tuple:
         """simple docstring"""



         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

         UpperCAmelCase__								=  self.alphas_cumprod[t]
         UpperCAmelCase__								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase__								=  1 - alpha_prod_t
         UpperCAmelCase__								=  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / 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
         UpperCAmelCase__								=  beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
               UpperCAmelCase__								=  self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
               UpperCAmelCase__								=  torch.log(torch.clamp(__UpperCAmelCase       ,  min=1E-20		)		)
               UpperCAmelCase__								=  torch.exp(0.5 * variance		)
         elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               UpperCAmelCase__								=  variance.log()
               UpperCAmelCase__								=  beta.log()

               UpperCAmelCase__								=  (predicted_variance + 1) / 2
               UpperCAmelCase__								=  frac * max_log + (1 - frac) * min_log

         return variance


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						List[str]=None       ,  __UpperCAmelCase     :						bool = True       ,  )						->				Union[UnCLIPSchedulerOutput, Tuple]:
         """simple docstring"""



         UpperCAmelCase__								=  timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               UpperCAmelCase__		,      UpperCAmelCase__								=  torch.split(__UpperCAmelCase       ,  sample.shape[1]       ,  dim=1		)
         else:
               UpperCAmelCase__								=  None

         # 1. compute alphas, betas
         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

         UpperCAmelCase__								=  self.alphas_cumprod[t]
         UpperCAmelCase__								=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase__								=  1 - alpha_prod_t
         UpperCAmelCase__								=  1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
               UpperCAmelCase__								=  self.alphas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / alpha_prod_t_prev
               UpperCAmelCase__								=  1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
               UpperCAmelCase__								=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               UpperCAmelCase__								=  model_output
         else:
               raise ValueError(
                   f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
                   " for the UnCLIPScheduler."		)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
               UpperCAmelCase__								=  torch.clamp(
                   __UpperCAmelCase       ,  -self.config.clip_sample_range       ,  self.config.clip_sample_range		)

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         UpperCAmelCase__								=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         UpperCAmelCase__								=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         UpperCAmelCase__								=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         UpperCAmelCase__								=  0
         if t > 0:
               UpperCAmelCase__								=  randn_tensor(
                   model_output.shape       ,  dtype=model_output.dtype       ,  generator=__UpperCAmelCase       ,  device=model_output.device		)

               UpperCAmelCase__								=  self._get_variance(
                   __UpperCAmelCase       ,  predicted_variance=__UpperCAmelCase       ,  prev_timestep=__UpperCAmelCase       ,  )

               if self.variance_type == "fixed_small_log":
                     UpperCAmelCase__								=  variance
               elif self.variance_type == "learned_range":
                     UpperCAmelCase__								=  (0.5 * variance).exp()
               else:
                     raise ValueError(
                         f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                         " for the UnCLIPScheduler."		)

               UpperCAmelCase__								=  variance * variance_noise

         UpperCAmelCase__								=  pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__UpperCAmelCase       ,  pred_original_sample=__UpperCAmelCase		)





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.IntTensor       ,  )						->				torch.FloatTensor:
         """simple docstring"""



         UpperCAmelCase__								=  self.alphas_cumprod.to(device=original_samples.device       ,  dtype=original_samples.dtype		)
         UpperCAmelCase__								=  timesteps.to(original_samples.device		)

         UpperCAmelCase__								=  alphas_cumprod[timesteps] ** 0.5
         UpperCAmelCase__								=  sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  (1 - alphas_cumprod[timesteps]) ** 0.5
         UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples



