





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       "AutoTokenizer"
 __lowerCamelCase    :				Tuple								=       ["tokenizer"]

 __lowerCamelCase    :				List[str]								=       {
     "semantic_prompt": 1,
     "coarse_prompt": 2,
     "fine_prompt": 2,
 }




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None		)     ->				int:
     super().__init__(_lowerCAmelCase		)

     _lowerCAmelCase						      =   speaker_embeddings




 @classmethod
 def 				_snake_case (						cls      ,  _lowerCAmelCase      ,  _lowerCAmelCase="speaker_embeddings_path.json"      ,  **_lowerCAmelCase		)     ->				List[Any]:

     if speaker_embeddings_dict_path is not None:
         _lowerCAmelCase						      =   get_file_from_repo(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  subfolder=kwargs.pop("subfolder"      ,  _lowerCAmelCase		)      ,  cache_dir=kwargs.pop("cache_dir"      ,  _lowerCAmelCase		)      ,  force_download=kwargs.pop("force_download"      ,  _lowerCAmelCase		)      ,  proxies=kwargs.pop("proxies"      ,  _lowerCAmelCase		)      ,  resume_download=kwargs.pop("resume_download"      ,  _lowerCAmelCase		)      ,  local_files_only=kwargs.pop("local_files_only"      ,  _lowerCAmelCase		)      ,  use_auth_token=kwargs.pop("use_auth_token"      ,  _lowerCAmelCase		)      ,  revision=kwargs.pop("revision"      ,  _lowerCAmelCase		)      ,  )
         if speaker_embeddings_path is None:
             logger.warning(
                 f'''`{os.path.join(_lowerCAmelCase      ,  _lowerCAmelCase		)}` 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`.'''		)
             _lowerCAmelCase						      =   None
         else:
             with open(_lowerCAmelCase		) as speaker_embeddings_json:
                 _lowerCAmelCase						      =   json.load(_lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   None

     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained(_lowerCAmelCase      ,  **_lowerCAmelCase		)

     return cls(tokenizer=_lowerCAmelCase      ,  speaker_embeddings=_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase="speaker_embeddings_path.json"      ,  _lowerCAmelCase="speaker_embeddings"      ,  _lowerCAmelCase = False      ,  **_lowerCAmelCase      ,  )     ->				List[str]:
     if self.speaker_embeddings is not None:
         os.makedirs(os.path.join(_lowerCAmelCase      ,  _lowerCAmelCase      ,  "v2"		)      ,  exist_ok=_lowerCAmelCase		)

         _lowerCAmelCase						      =   {}

         _lowerCAmelCase						      =   save_directory

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

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

                 _lowerCAmelCase						      =   tmp_dict

         with open(os.path.join(_lowerCAmelCase      ,  _lowerCAmelCase		)      ,  "w"		) as fp:
             json.dump(_lowerCAmelCase      ,  _lowerCAmelCase		)

     super().save_pretrained(_lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   self.speaker_embeddings[voice_preset]

     _lowerCAmelCase						      =   {}
     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}].'''		)

         _lowerCAmelCase						      =   get_file_from_repo(
             self.speaker_embeddings.get("repo_or_path"      ,  "/"		)      ,  voice_preset_paths[key]      ,  subfolder=kwargs.pop("subfolder"      ,  _lowerCAmelCase		)      ,  cache_dir=kwargs.pop("cache_dir"      ,  _lowerCAmelCase		)      ,  force_download=kwargs.pop("force_download"      ,  _lowerCAmelCase		)      ,  proxies=kwargs.pop("proxies"      ,  _lowerCAmelCase		)      ,  resume_download=kwargs.pop("resume_download"      ,  _lowerCAmelCase		)      ,  local_files_only=kwargs.pop("local_files_only"      ,  _lowerCAmelCase		)      ,  use_auth_token=kwargs.pop("use_auth_token"      ,  _lowerCAmelCase		)      ,  revision=kwargs.pop("revision"      ,  _lowerCAmelCase		)      ,  )
         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.'''		)

         _lowerCAmelCase						      =   np.load(_lowerCAmelCase		)

     return voice_preset_dict




 def 				_snake_case (						self      ,  _lowerCAmelCase = None		)     ->				Any:
     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      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="pt"      ,  _lowerCAmelCase=256      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  **_lowerCAmelCase      ,  )     ->				List[str]:
     if voice_preset is not None and not isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         if (
             isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		)
             and self.speaker_embeddings is not None
             and voice_preset in self.speaker_embeddings
         ):
             _lowerCAmelCase						      =   self._load_voice_preset(_lowerCAmelCase		)

         else:
             if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		) and not voice_preset.endswith(".npz"		):
                 _lowerCAmelCase						      =   voice_preset + ".npz"

             _lowerCAmelCase						      =   np.load(_lowerCAmelCase		)

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

     _lowerCAmelCase						      =   self.tokenizer(
         _lowerCAmelCase      ,  return_tensors=_lowerCAmelCase      ,  padding="max_length"      ,  max_length=_lowerCAmelCase      ,  return_attention_mask=_lowerCAmelCase      ,  return_token_type_ids=_lowerCAmelCase      ,  add_special_tokens=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )

     if voice_preset is not None:
         _lowerCAmelCase						      =   voice_preset

     return encoded_text









'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


_SCREAMING_SNAKE_CASE									=				"\\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"

_SCREAMING_SNAKE_CASE									=				"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"

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




 def 				_snake_case (						self		)     ->				Dict:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(self._get_feature_types()		)      ,  reference_urls=[
             "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
         ]      ,  )




 def 				_snake_case (						self		)     ->				Tuple:
     if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"		)		),
             "references": datasets.Sequence(datasets.Value("float"		)		),
         }
     else:
         return {
             "predictions": datasets.Value("float"		),
             "references": datasets.Value("float"		),
         }





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


