


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase_   :				Optional[Any]						      = logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				SCREAMING_SNAKE_CASE__						(       __snake_case    ):
      def __init__( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  )	->    Dict:
             super().__init__()
             self.register_modules(unet=__UpperCamelCase  ,							scheduler=__UpperCamelCase  )

      @torch.no_grad()
      def __call__( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : List[Any] = None  ,							SCREAMING_SNAKE_CASE__       : List[Any] = None  ,							SCREAMING_SNAKE_CASE__       : int = True  ,							)	->    List[str]:


             if audio_length_in_s is None:
                    a_      :	Tuple							=						self.unet.config.sample_size / self.unet.config.sample_rate

             a_      :	Optional[int]							=						audio_length_in_s * self.unet.config.sample_rate

             a_      :	str							=						2 ** len(self.unet.up_blocks  )
             if sample_size < 3 * down_scale_factor:
                    raise ValueError(
                        F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                        F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""  )

             a_      :	List[str]							=						int(__UpperCamelCase  )
             if sample_size % down_scale_factor != 0:
                    a_      :	Union[str, Any]							=						(
                        (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                    ) * down_scale_factor
                    logger.info(
                        F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                        F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                        ' process.'  )
             a_      :	Any							=						int(__UpperCamelCase  )

             a_      :	Tuple							=						next(iter(self.unet.parameters()  )  ).dtype
             a_      :	List[str]							=						(batch_size, self.unet.config.in_channels, sample_size)
             if isinstance(__UpperCamelCase  ,							__UpperCamelCase  ) and len(__UpperCamelCase  ) != batch_size:
                    raise ValueError(
                        F"""You have passed a list of generators of length {len(__UpperCamelCase  )}, but requested an effective batch"""
                        F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""  )

             a_      :	int							=						randn_tensor(__UpperCamelCase  ,							generator=__UpperCamelCase  ,							device=self.device  ,							dtype=__UpperCamelCase  )

             # set step values
             self.scheduler.set_timesteps(__UpperCamelCase  ,							device=audio.device  )
             a_      :	str							=						self.scheduler.timesteps.to(__UpperCamelCase  )

             for t in self.progress_bar(self.scheduler.timesteps  ):
                    # 1. predict noise model_output
                    a_      :	Any							=						self.unet(__UpperCamelCase  ,							__UpperCamelCase  ).sample

                    # 2. compute previous image: x_t -> t_t-1
                    a_      :	List[Any]							=						self.scheduler.step(__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ).prev_sample

             a_      :	Tuple							=						audio.clamp(-1  ,							1  ).float().cpu().numpy()

             a_      :	Optional[Any]							=						audio[:, :, :original_sample_size]

             if not return_dict:
                    return (audio,)

             return AudioPipelineOutput(audios=__UpperCamelCase  )






from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_   :				List[Any]						      = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


UpperCAmelCase_   :				Optional[Any]						      = '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. 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.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    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\'`.\n\n        - \'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.\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. This option 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\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_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])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


UpperCAmelCase_   :				Optional[Any]						      = '\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    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.f1_score.html']  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=1  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]="binary"  ,							SCREAMING_SNAKE_CASE__       : Dict=None  )	->    Any:
             a_      :	List[Any]							=						fa_score(
                 SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  ,							pos_label=SCREAMING_SNAKE_CASE__  ,							average=SCREAMING_SNAKE_CASE__  ,							sample_weight=SCREAMING_SNAKE_CASE__  )
             return {"f1": float(SCREAMING_SNAKE_CASE__  ) if score.size == 1 else score}


