






from sklearn.metrics import recall_score

import datasets


_a      	=  '''
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.
'''


_a      	=  '''
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.])}
'''


_a      	=  '''
@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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							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       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase=None       ,    UpperCAmelCase=1       ,    UpperCAmelCase="binary"       ,    UpperCAmelCase=None       ,    UpperCAmelCase="warn"       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			recall_score(
							    UpperCAmelCase       ,    UpperCAmelCase       ,    labels=UpperCAmelCase       ,    pos_label=UpperCAmelCase       ,    average=UpperCAmelCase       ,    sample_weight=UpperCAmelCase       ,    zero_division=UpperCAmelCase       ,    )
							return {"recall": float(UpperCAmelCase    ) if score.size == 1 else score}








import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "AutoTokenizer"
	UpperCamelCase__								=      ["tokenizer"]

	UpperCamelCase__								=      {
	    "semantic_prompt": 1,
	    "coarse_prompt": 2,
	    "fine_prompt": 2,
	}

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							super().__init__(UpperCAmelCase    )

							_UpperCAmelCase									=			speaker_embeddings

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase       ,    UpperCAmelCase="speaker_embeddings_path.json"       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							if speaker_embeddings_dict_path is not None:
													_UpperCAmelCase									=			get_file_from_repo(
													    UpperCAmelCase       ,    UpperCAmelCase       ,    subfolder=kwargs.pop('subfolder'       ,    UpperCAmelCase    )       ,    cache_dir=kwargs.pop('cache_dir'       ,    UpperCAmelCase    )       ,    force_download=kwargs.pop('force_download'       ,    UpperCAmelCase    )       ,    proxies=kwargs.pop('proxies'       ,    UpperCAmelCase    )       ,    resume_download=kwargs.pop('resume_download'       ,    UpperCAmelCase    )       ,    local_files_only=kwargs.pop('local_files_only'       ,    UpperCAmelCase    )       ,    use_auth_token=kwargs.pop('use_auth_token'       ,    UpperCAmelCase    )       ,    revision=kwargs.pop('revision'       ,    UpperCAmelCase    )       ,    )
													if speaker_embeddings_path is None:
																			logger.warning(
																			    F"""`{os.path.join(UpperCAmelCase       ,    UpperCAmelCase    )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""    )
																			_UpperCAmelCase									=			None
													else:
																			with open(UpperCAmelCase    ) as speaker_embeddings_json:
																									_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			None

							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(UpperCAmelCase       ,    **UpperCAmelCase    )

							return cls(tokenizer=UpperCAmelCase       ,    speaker_embeddings=UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase="speaker_embeddings_path.json"       ,    UpperCAmelCase="speaker_embeddings"       ,    UpperCAmelCase = False       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(UpperCAmelCase       ,    UpperCAmelCase       ,    'v2'    )       ,    exist_ok=UpperCAmelCase    )

													_UpperCAmelCase									=			{}

													_UpperCAmelCase									=			save_directory

													for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																									_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

																									_UpperCAmelCase									=			{}
																									for key in self.speaker_embeddings[prompt_key]:
																															np.save(
																															    os.path.join(
																															        embeddings_dict['repo_or_path']       ,    UpperCAmelCase       ,    F"""{prompt_key}_{key}"""    )       ,    voice_preset[key]       ,    allow_pickle=UpperCAmelCase       ,    )
																															_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    F"""{prompt_key}_{key}.npy"""    )

																									_UpperCAmelCase									=			tmp_dict

													with open(os.path.join(UpperCAmelCase       ,    UpperCAmelCase    )       ,    'w'    ) as fp:
																			json.dump(UpperCAmelCase       ,    UpperCAmelCase    )

							super().save_pretrained(UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.speaker_embeddings[voice_preset]

							_UpperCAmelCase									=			{}
							for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset_paths:
																			raise ValueError(
																			    F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""    )

													_UpperCAmelCase									=			get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path'       ,    '/'    )       ,    voice_preset_paths[key]       ,    subfolder=kwargs.pop('subfolder'       ,    UpperCAmelCase    )       ,    cache_dir=kwargs.pop('cache_dir'       ,    UpperCAmelCase    )       ,    force_download=kwargs.pop('force_download'       ,    UpperCAmelCase    )       ,    proxies=kwargs.pop('proxies'       ,    UpperCAmelCase    )       ,    resume_download=kwargs.pop('resume_download'       ,    UpperCAmelCase    )       ,    local_files_only=kwargs.pop('local_files_only'       ,    UpperCAmelCase    )       ,    use_auth_token=kwargs.pop('use_auth_token'       ,    UpperCAmelCase    )       ,    revision=kwargs.pop('revision'       ,    UpperCAmelCase    )       ,    )
													if path is None:
																			raise ValueError(
																			    F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"       ,    "/"    )       ,    voice_preset_paths[key]    )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""    )

													_UpperCAmelCase									=			np.load(UpperCAmelCase    )

							return voice_preset_dict

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset:
																			raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""    )

													if not isinstance(voice_preset[key]       ,    np.ndarray    ):
																			raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."""    )

													if len(voice_preset[key].shape    ) != self.preset_shape[key]:
																			raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."""    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase=None       ,    UpperCAmelCase="pt"       ,    UpperCAmelCase=256       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True       ,    UpperCAmelCase=False       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if voice_preset is not None and not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													if (
													    isinstance(UpperCAmelCase       ,    UpperCAmelCase    )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																			_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

													else:
																			if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ) and not voice_preset.endswith('.npz'    ):
																									_UpperCAmelCase									=			voice_preset + '.npz'

																			_UpperCAmelCase									=			np.load(UpperCAmelCase    )

							if voice_preset is not None:
													self._validate_voice_preset_dict(UpperCAmelCase       ,    **UpperCAmelCase    )
													_UpperCAmelCase									=			BatchFeature(data=UpperCAmelCase       ,    tensor_type=UpperCAmelCase    )

							_UpperCAmelCase									=			self.tokenizer(
							    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    padding='max_length'       ,    max_length=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

							if voice_preset is not None:
													_UpperCAmelCase									=			voice_preset

							return encoded_text
