




'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case : List[Any]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{'vocab_file': 'spm_char.model'}

_snake_case : Union[str, Any]  				=						{
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

_snake_case : List[str]  				=						{
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}

class      A       (						_a       ):
							lowercase_							     =							VOCAB_FILES_NAMES
							lowercase_							     =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_							     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							     =							['input_ids', 'attention_mask']







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    int="<s>"				,     lowerCAmelCase_			:    List[str]="</s>"				,     lowerCAmelCase_			:    str="<unk>"				,     lowerCAmelCase_			:    int="<pad>"				,     lowerCAmelCase_			:    Optional[Dict[str, Any]] = None				,     **lowerCAmelCase_			:    List[str]				,     )     ->				None:


														"""simple docstring"""


														_a      	=							{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=lowerCAmelCase_				,     eos_token=lowerCAmelCase_				,     unk_token=lowerCAmelCase_				,     pad_token=lowerCAmelCase_				,     sp_model_kwargs=self.sp_model_kwargs				,     **lowerCAmelCase_				,     )

														_a      	=							vocab_file

														_a      	=							spm.SentencePieceProcessor(**self.sp_model_kwargs   )
														self.sp_model.Load(lowerCAmelCase_   )







							@property
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														return self.sp_model.get_piece_size()







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														_a      	=							{self.convert_ids_to_tokens(lowerCAmelCase_   ): i for i in range(self.vocab_size   )}
														vocab.update(self.added_tokens_encoder   )
														return vocab







							def __getstate__(    self			:    int   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.__dict__.copy()
														_a      	=							None
														return state







							def __setstate__(    self			:    Any				,     lowerCAmelCase_			:    Optional[int]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							d

														# for backward compatibility
														if not hasattr(self				,     '''sp_model_kwargs'''   ):
																					_a      	=							{}

														_a      	=							spm.SentencePieceProcessor(**self.sp_model_kwargs   )
														self.sp_model.Load(self.vocab_file   )







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    str   )     ->				List[str]:


														"""simple docstring"""


														return self.sp_model.encode(lowerCAmelCase_				,     out_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														return self.sp_model.piece_to_id(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.sp_model.IdToPiece(lowerCAmelCase_   )
														return token







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    str   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							[]
														_a      	=							''''''
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(lowerCAmelCase_   ) + token
																												_a      	=							[]
																					else:
																												current_sub_tokens.append(lowerCAmelCase_   )
														out_string += self.sp_model.decode(lowerCAmelCase_   )
														return out_string.strip()







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    List[Any]=None   )     ->				List[int]:


														"""simple docstring"""


														if token_ids_a is None:
																					return token_ids_a + [self.eos_token_id]
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return token_ids_a + token_ids_a + [self.eos_token_id]







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None				,     lowerCAmelCase_			:    bool = False   )     ->				List[int]:


														"""simple docstring"""


														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCAmelCase_				,     token_ids_a=lowerCAmelCase_				,     already_has_special_tokens=lowerCAmelCase_   )

														_a      	=							[1]
														if token_ids_a is None:
																					return ([0] * len(lowerCAmelCase_   )) + suffix_ones
														return ([0] * len(lowerCAmelCase_   )) + ([0] * len(lowerCAmelCase_   )) + suffix_ones







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[str] = None   )     ->				Tuple[str]:


														"""simple docstring"""


														if not os.path.isdir(lowerCAmelCase_   ):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
																					return
														_a      	=							os.path.join(
														    lowerCAmelCase_				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

														if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase_   ) and os.path.isfile(self.vocab_file   ):
																					copyfile(self.vocab_file				,     lowerCAmelCase_   )
														elif not os.path.isfile(self.vocab_file   ):
																					with open(lowerCAmelCase_				,     '''wb'''   ) as fi:
																												_a      	=							self.sp_model.serialized_model_proto()
																												fi.write(lowerCAmelCase_   )

														return (out_vocab_file,)








from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__							:      List[str]         =    """\
@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}
}
"""

SCREAMING_SNAKE_CASE__							:      Tuple         =    """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""







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





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      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   )					->	Optional[int]:
      """simple docstring"""





      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       ,      snake_case       ,      snake_case       ,      snake_case=None       ,      snake_case="uniform_average"       ,      snake_case=True   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[str]   		=	mean_squared_error(
          snake_case       ,      snake_case       ,      sample_weight=snake_case       ,      multioutput=snake_case       ,      squared=snake_case   )

      return {"mse": mse}

