



from sklearn.metrics import recall_score

import datasets


lowercase__          ="""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


lowercase__          ="""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **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.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **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'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions 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. Note that it 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** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **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.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 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 		UpperCamelCase__  (	datasets.Metric   ):
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    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 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: int ,    snake_case_			: Any ,    snake_case_			: str=None ,    snake_case_			: List[Any]=1 ,    snake_case_			: Optional[int]="binary" ,    snake_case_			: Any=None ,    snake_case_			: Any="warn" ,    ):
    __a			:  Union[str, Any]		    =  recall_score(
        snake_case_ ,    snake_case_ ,    labels=snake_case_ ,    pos_label=snake_case_ ,    average=snake_case_ ,    sample_weight=snake_case_ ,    zero_division=snake_case_ ,    )
    return {"recall": float(snake_case_       ) if score.size == 1 else score}


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							CommonSchedulerState

   # setable values
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   @classmethod
   def 				lowerCAmelCase      (cls			: int ,    snake_case_			: CommonSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray       ):
    return cls(common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_       )





@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							DDPMSchedulerState





class 		UpperCamelCase__  (	__lowercase   ,__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				[e.name for e in FlaxKarrasDiffusionSchedulers]

   _SCREAMING_SNAKE_CASE				:							jnp.dtype
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    return True
   @register_to_config
   def __init__(self			: Any ,    snake_case_			: int = 1_0_0_0 ,    snake_case_			: float = 0.0001 ,    snake_case_			: float = 0.02 ,    snake_case_			: str = "linear" ,    snake_case_			: Optional[jnp.ndarray] = None ,    snake_case_			: str = "fixed_small" ,    snake_case_			: bool = True ,    snake_case_			: str = "epsilon" ,    snake_case_			: jnp.dtype = jnp.floataa ,    ):
    __a			:  str		    =  dtype
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Optional[CommonSchedulerState] = None       ):
    if common is None:
     __a			:  Optional[Any]		    =  CommonSchedulerState.create(self       )

    # standard deviation of the initial noise distribution
    __a			:  int		    =  jnp.array(1.0 ,    dtype=self.dtype       )

    __a			:  List[Any]		    =  jnp.arange(0 ,    self.config.num_train_timesteps       ).round()[::-1]

    return DDPMSchedulerState.create(
        common=snake_case_ ,    init_noise_sigma=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[int] = None       ):
    return sample
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: int ,    snake_case_			: Tuple = ()       ):
    __a			:  Tuple		    =  self.config.num_train_timesteps // num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # rounding to avoid issues when num_inference_step is power of 3
    __a			:  Any		    =  (jnp.arange(0 ,    snake_case_       ) * step_ratio).round()[::-1]

    return state.replace(
        num_inference_steps=snake_case_ ,    timesteps=snake_case_ ,    )
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: Optional[Any] ,    snake_case_			: List[str]=None ,    snake_case_			: Union[str, Any]=None       ):
    __a			:  Optional[Any]		    =  state.common.alphas_cumprod[t]
    __a			:  List[str]		    =  jnp.where(t > 0 ,    state.common.alphas_cumprod[t - 1] ,    jnp.array(1.0 ,    dtype=self.dtype       )       )

    # 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
    __a			:  Optional[int]		    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

    if variance_type is None:
     __a			:  List[str]		    =  self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small":
     __a			:  Optional[Any]		    =  jnp.clip(snake_case_ ,    a_min=1E-20       )
    # for rl-diffuser https://arxiv.org/abs/2205.09991
    elif variance_type == "fixed_small_log":
     __a			:  int		    =  jnp.log(jnp.clip(snake_case_ ,    a_min=1E-20       )       )
    elif variance_type == "fixed_large":
     __a			:  List[str]		    =  state.common.betas[t]
    elif variance_type == "fixed_large_log":
     # Glide max_log
     __a			:  Union[str, Any]		    =  jnp.log(state.common.betas[t]       )
    elif variance_type == "learned":
     return predicted_variance
    elif variance_type == "learned_range":
     __a			:  Any		    =  variance
     __a			:  Dict		    =  state.common.betas[t]
     __a			:  Any		    =  (predicted_variance + 1) / 2
     __a			:  Optional[Any]		    =  frac * max_log + (1 - frac) * min_log

    return variance
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: int ,    snake_case_			: jnp.ndarray ,    snake_case_			: Optional[jax.random.KeyArray] = None ,    snake_case_			: bool = True ,    ):
    __a			:  int		    =  timestep

    if key is None:
     __a			:  Any		    =  jax.random.PRNGKey(0       )

    if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
     __a    ,	__a			:  List[str]		    =  jnp.split(snake_case_ ,    sample.shape[1] ,    axis=1       )
    else:
     __a			:  int		    =  None

    # 1. compute alphas, betas
    __a			:  Optional[int]		    =  state.common.alphas_cumprod[t]
    __a			:  Tuple		    =  jnp.where(t > 0 ,    state.common.alphas_cumprod[t - 1] ,    jnp.array(1.0 ,    dtype=self.dtype       )       )
    __a			:  Optional[int]		    =  1 - alpha_prod_t
    __a			:  Union[str, Any]		    =  1 - alpha_prod_t_prev

    # 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":
     __a			:  int		    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
     __a			:  Union[str, Any]		    =  model_output
    elif self.config.prediction_type == "v_prediction":
     __a			:  Tuple		    =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    else:
     raise ValueError(
         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
         ''' for the FlaxDDPMScheduler.'''       )

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
     __a			:  Dict		    =  jnp.clip(snake_case_ ,    -1 ,    1       )

    # 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
    __a			:  str		    =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
    __a			:  str		    =  state.common.alphas[t] ** 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
    __a			:  Tuple		    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    def random_variance():
     __a			:  Optional[int]		    =  jax.random.split(snake_case_ ,    num=1       )
     __a			:  Union[str, Any]		    =  jax.random.normal(snake_case_ ,    shape=model_output.shape ,    dtype=self.dtype       )
     return (self._get_variance(snake_case_ ,    snake_case_ ,    predicted_variance=snake_case_       ) ** 0.5) * noise

    __a			:  Dict		    =  jnp.where(t > 0 ,    random_variance() ,    jnp.zeros(model_output.shape ,    dtype=self.dtype       )       )

    __a			:  Tuple		    =  pred_prev_sample + variance

    if not return_dict:
     return (pred_prev_sample, state)

    return FlaxDDPMSchedulerOutput(prev_sample=snake_case_ ,    state=snake_case_       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return add_noise_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: DDPMSchedulerState ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    snake_case_			: jnp.ndarray ,    ):
    return get_velocity_common(state.common ,    snake_case_ ,    snake_case_ ,    snake_case_       )







   def __len__(self			: List[str]       ):
    return self.config.num_train_timesteps
