



# 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 	lowerCamelCase_    :






						'''simple docstring'''
						a__   =  42

						# setable values
						a__   =  42
						a__   =  42
						a__   =  None







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Optional[int]	,	__lowerCamelCase      :			CommonSchedulerState	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			jnp.ndarray )      ->				Optional[int]:
									return cls(common=__lowerCamelCase	,	init_noise_sigma=__lowerCamelCase	,	timesteps=__lowerCamelCase )







@dataclass
class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  42







class 	lowerCamelCase_    ( _A						,_A						):






						'''simple docstring'''
						a__   =  [e.name for e in FlaxKarrasDiffusionSchedulers]

						a__   =  42







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									return True







						@register_to_config
						def __init__(	self      :			Tuple	,	__lowerCamelCase      :			int = 10_00	,	__lowerCamelCase      :			float = 0.0001	,	__lowerCamelCase      :			float = 0.02	,	__lowerCamelCase      :			str = "linear"	,	__lowerCamelCase      :			Optional[jnp.ndarray] = None	,	__lowerCamelCase      :			str = "fixed_small"	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			str = "epsilon"	,	__lowerCamelCase      :			jnp.dtype = jnp.floataa	,	)      ->				List[str]:
									A							:					str              =						dtype







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Optional[CommonSchedulerState] = None )      ->				DDPMSchedulerState:
									if common is None:
												A							:					int              =						CommonSchedulerState.create(self )

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

									A							:					Union[str, Any]              =						jnp.arange(0	,	self.config.num_train_timesteps ).round()[::-1]

									return DDPMSchedulerState.create(
									    common=__lowerCamelCase	,	init_noise_sigma=__lowerCamelCase	,	timesteps=__lowerCamelCase	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			DDPMSchedulerState	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			Optional[int] = None )      ->				jnp.ndarray:
									return sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			DDPMSchedulerState	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Tuple = () )      ->				DDPMSchedulerState:
									A							:					List[str]              =						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							:					int              =						(jnp.arange(0	,	__lowerCamelCase ) * step_ratio).round()[::-1]

									return state.replace(
									    num_inference_steps=__lowerCamelCase	,	timesteps=__lowerCamelCase	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			DDPMSchedulerState	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			int=None )      ->				Any:
									A							:					str              =						state.common.alphas_cumprod[t]
									A							:					Union[str, Any]              =						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							:					str              =						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

									if variance_type is None:
												A							:					Tuple              =						self.config.variance_type

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

									return variance







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			DDPMSchedulerState	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			Optional[jax.random.KeyArray] = None	,	__lowerCamelCase      :			bool = True	,	)      ->				Union[FlaxDDPMSchedulerOutput, Tuple]:
									A							:					Union[str, Any]              =						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							:					Any              =						jnp.split(__lowerCamelCase	,	sample.shape[1]	,	axis=1 )
									else:
												A							:					Tuple              =						None

									# 1. compute alphas, betas
									A							:					Dict              =						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							:					Any              =						1 - alpha_prod_t
									A							:					str              =						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							:					Optional[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							:					Dict              =						(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(__lowerCamelCase	,	-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							:					Optional[int]              =						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									A							:					Dict              =						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							:					List[str]              =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
												A							:					str              =						jax.random.split(__lowerCamelCase	,	num=1 )
												A							:					Any              =						jax.random.normal(__lowerCamelCase	,	shape=model_output.shape	,	dtype=self.dtype )
												return (self._get_variance(__lowerCamelCase	,	__lowerCamelCase	,	predicted_variance=__lowerCamelCase ) ** 0.5) * noise

									A							:					List[Any]              =						jnp.where(t > 0	,	random_variance()	,	jnp.zeros(model_output.shape	,	dtype=self.dtype ) )

									A							:					List[Any]              =						pred_prev_sample + variance

									if not return_dict:
												return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase	,	state=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			DDPMSchedulerState	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			jnp.ndarray	,	)      ->				jnp.ndarray:
									return add_noise_common(state.common	,	__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			DDPMSchedulerState	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			jnp.ndarray	,	__lowerCamelCase      :			jnp.ndarray	,	)      ->				jnp.ndarray:
									return get_velocity_common(state.common	,	__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )







						def __len__(	self      :			str )      ->				str:
									return self.config.num_train_timesteps




from sklearn.metrics import recall_score

import datasets


__SCREAMING_SNAKE_CASE           =  """
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.
"""


__SCREAMING_SNAKE_CASE           =  """
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.])}
"""


__SCREAMING_SNAKE_CASE           =  """
@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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				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.recall_score.html"]	,	)







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