

'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
    from ..models.auto import AutoTokenizer


class        a__  :


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      raise NotImplementedError()






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      raise NotImplementedError()




class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       tokenizer
      _lowercase      :				Dict      					=       skip_prompt
      _lowercase      :				Optional[int]      					=       decode_kwargs

      # variables used in the streaming process
      _lowercase      :				Optional[Any]      					=       []
      _lowercase      :				Dict      					=       0
      _lowercase      :				Optional[Any]      					=       True


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if len(value.shape  ) > 1 and value.shape[0] > 1:
         raise ValueError("TextStreamer only supports batch size 1"  )
      elif len(value.shape  ) > 1:
         _lowercase      :				Dict      					=       value[0]

      if self.skip_prompt and self.next_tokens_are_prompt:
         _lowercase      :				List[Any]      					=       False
         return

      # Add the new token to the cache and decodes the entire thing.
      self.token_cache.extend(value.tolist()  )
      _lowercase      :				Optional[int]      					=       self.tokenizer.decode(self.token_cache      ,    **self.decode_kwargs  )

      # After the symbol for a new line, we flush the cache.
      if text.endswith("\n"  ):
         _lowercase      :				Dict      					=       text[self.print_len :]
         _lowercase      :				Tuple      					=       []
         _lowercase      :				Tuple      					=       0
      # If the last token is a CJK character, we print the characters.
      elif len(_UpperCamelCase  ) > 0 and self._is_chinese_char(ord(text[-1]  )  ):
         _lowercase      :				Dict      					=       text[self.print_len :]
         self.print_len += len(_UpperCamelCase  )
      # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
      # which may change with the subsequent token -- there are probably smarter ways to do this!)
      else:
         _lowercase      :				Union[str, Any]      					=       text[self.print_len : text.rfind(" "  ) + 1]
         self.print_len += len(_UpperCamelCase  )

      self.on_finalized_text(_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if len(self.token_cache  ) > 0:
         _lowercase      :				Union[str, Any]      					=       self.tokenizer.decode(self.token_cache      ,    **self.decode_kwargs  )
         _lowercase      :				List[Any]      					=       text[self.print_len :]
         _lowercase      :				Optional[Any]      					=       []
         _lowercase      :				Optional[int]      					=       0
      else:
         _lowercase      :				Any      					=       ""

      _lowercase      :				Any      					=       True
      self.on_finalized_text(_UpperCamelCase      ,    stream_end=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False  ):





      """simple docstring"""





      print(_UpperCamelCase      ,    flush=_UpperCamelCase      ,    end="" if not stream_end else None  )






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if (
          (cp >= 0X4E_00 and cp <= 0X9F_FF)
          or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
          or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
          or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
          or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
          or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
          or (cp >= 0XF9_00 and cp <= 0XFA_FF)
          or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
      ):  #
         return True

      return False




class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )
      _lowercase      :				Dict      					=       Queue()
      _lowercase      :				Optional[Any]      					=       None
      _lowercase      :				Any      					=       timeout


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False  ):





      """simple docstring"""





      self.text_queue.put(_UpperCamelCase      ,    timeout=self.timeout  )
      if stream_end:
         self.text_queue.put(self.stop_signal      ,    timeout=self.timeout  )


   def __iter__(			self  ):





      """simple docstring"""





      return self






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       self.text_queue.get(timeout=self.timeout  )
      if value == self.stop_signal:
         raise StopIteration()
      else:
         return value









'''simple docstring'''
from sklearn.metrics import mean_squared_error

import datasets


_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'

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


_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        a__  (							datasets.Metric  ):


   def 						_lowerCamelCase       (			self  ):





      """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 						_lowerCamelCase       (			self  ):





      """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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="uniform_average"      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				str      					=       mean_squared_error(
          _UpperCamelCase      ,    _UpperCamelCase      ,    sample_weight=_UpperCamelCase      ,    multioutput=_UpperCamelCase      ,    squared=_UpperCamelCase  )

      return {"mse": mse}






