




import numpy as np




class 	UpperCamelCase__			:

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__=5  ,						UpperCamelCase__=5  )  ->   List[Any]:
           lowerCamelCase     :   List[Any]    				=      depth
           lowerCamelCase     :   List[Any]    				=      0
           lowerCamelCase     :   Union[str, Any]    				=      None
           lowerCamelCase     :   Any    				=      None
           lowerCamelCase     :   List[str]    				=      min_leaf_size
           lowerCamelCase     :   List[Any]    				=      None







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Tuple:

           if labels.ndim != 1:
                  print("Error: Input labels must be one dimensional"  )

           return np.mean((labels - prediction) ** 2  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   int:

           if x.ndim != 1:
                  print("Error: Input data set must be one dimensional"  )
                  return
           if len(lowerCamelCase_  ) != len(lowerCamelCase_  ):
                  print("Error: X and y have different lengths"  )
                  return
           if y.ndim != 1:
                  print("Error: Data set labels must be one dimensional"  )
                  return

           if len(lowerCamelCase_  ) < 2 * self.min_leaf_size:
                  lowerCamelCase     :   Union[str, Any]    				=      np.mean(lowerCamelCase_  )
                  return

           if self.depth == 1:
                  lowerCamelCase     :   Any    				=      np.mean(lowerCamelCase_  )
                  return

           lowerCamelCase     :   Optional[int]    				=      0
           lowerCamelCase     :   Union[str, Any]    				=      self.mean_squared_error(lowerCamelCase_  ,						np.mean(lowerCamelCase_  )  ) * 2
           for i in range(len(lowerCamelCase_  )  ):
                  if len(x[:i]  ) < self.min_leaf_size:
                         continue
                  elif len(x[i:]  ) < self.min_leaf_size:
                         continue
                  else:
                         lowerCamelCase     :   str    				=      self.mean_squared_error(x[:i]  ,						np.mean(y[:i]  )  )
                         lowerCamelCase     :   List[Any]    				=      self.mean_squared_error(x[i:]  ,						np.mean(y[i:]  )  )
                         lowerCamelCase     :   int    				=      error_left + error_right
                         if error < min_error:
                                lowerCamelCase     :   Dict    				=      i
                                lowerCamelCase     :   List[Any]    				=      error

           if best_split != 0:
                  lowerCamelCase     :   int    				=      x[:best_split]
                  lowerCamelCase     :   Optional[int]    				=      y[:best_split]
                  lowerCamelCase     :   str    				=      x[best_split:]
                  lowerCamelCase     :   int    				=      y[best_split:]

                  lowerCamelCase     :   str    				=      x[best_split]
                  lowerCamelCase     :   Optional[int]    				=      DecisionTree(
                      depth=self.depth - 1  ,						min_leaf_size=self.min_leaf_size  )
                  lowerCamelCase     :   Tuple    				=      DecisionTree(
                      depth=self.depth - 1  ,						min_leaf_size=self.min_leaf_size  )
                  self.left.train(lowerCamelCase_  ,						lowerCamelCase_  )
                  self.right.train(lowerCamelCase_  ,						lowerCamelCase_  )
           else:
                  lowerCamelCase     :   Optional[int]    				=      np.mean(lowerCamelCase_  )

           return







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Union[str, Any]:

           if self.prediction is not None:
                  return self.prediction
           elif self.left or self.right is not None:
                  if x >= self.decision_boundary:
                         return self.right.predict(lowerCamelCase_  )
                  else:
                         return self.left.predict(lowerCamelCase_  )
           else:
                  print("Error: Decision tree not yet trained"  )
                  return None




class 	UpperCamelCase__			:

    '''simple docstring'''



    @staticmethod
    def 					_lowercase  (					UpperCamelCase__  ,						UpperCamelCase__  )  ->   Dict:
           lowerCamelCase     :   Union[str, Any]    				=      float(0  )
           for label in labels:
                  squared_error_sum += (label - prediction) ** 2

           return float(squared_error_sum / labels.size  )




def   A   (   )			->				Dict:

       '''simple docstring'''





       lowerCamelCase     :   str    				=      np.arange(-1.0				,1.0				,0.005					)
       lowerCamelCase     :   Union[str, Any]    				=      np.sin(a_					)

       lowerCamelCase     :   Any    				=      DecisionTree(depth=10				,min_leaf_size=10					)
       tree.train(a_				,a_					)

       lowerCamelCase     :   List[Any]    				=      (np.random.rand(10					) * 2) - 1
       lowerCamelCase     :   Optional[Any]    				=      np.array([tree.predict(a_					) for x in test_cases]					)
       lowerCamelCase     :   int    				=      np.mean((predictions - test_cases) ** 2					)

       print("Test values: " + str(a_					)					)
       print("Predictions: " + str(a_					)					)
       print("Average error: " + str(a_					)					)


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod(name='mean_squarred_error', verbose=True)












import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     logging.get_logger(__name__)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				np.array:
       lowerCamelCase     :   Optional[Any]    				=      f'''{sampling_rate}'''
       lowerCamelCase     :   Optional[Any]    				=      "1"
       lowerCamelCase     :   Tuple    				=      "f32le"
       lowerCamelCase     :   Any    				=      [
           "ffmpeg",
           "-i",
           "pipe:0",
           "-ac",
           ac,
           "-ar",
           ar,
           "-f",
           format_for_conversion,
           "-hide_banner",
           "-loglevel",
           "quiet",
           "pipe:1",
       ]

       try:
              lowerCamelCase     :   Optional[int]    				=      subprocess.Popen(_SCREAMING_SNAKE_CASE				,stdin=subprocess.PIPE				,stdout=subprocess.PIPE					)
       except FileNotFoundError:
              raise ValueError("ffmpeg was not found but is required to load audio files from filename"					)
       lowerCamelCase     :   Optional[int]    				=      ffmpeg_process.communicate(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Union[str, Any]    				=      output_stream[0]

       lowerCamelCase     :   Dict    				=      np.frombuffer(_SCREAMING_SNAKE_CASE				,np.floataa					)
       if audio.shape[0] == 0:
              raise ValueError("Malformed soundfile"					)
       return audio




@add_end_docstrings(lowerCAmelCase__  )
class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def __init__(					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Dict:
           # Default, might be overriden by the model.config.
           lowerCamelCase     :   Union[str, Any]    				=      5
           super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__  )

           if self.framework != "pt":
                  raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''  )

           self.check_model_type(UpperCamelCase__  )







    def __call__(					self  ,						UpperCamelCase__  ,						**UpperCamelCase__  ,						)  ->   Optional[int]:
           return super().__call__(UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   str:
           # No parameters on this pipeline right now
           lowerCamelCase     :   int    				=      {}
           if top_k is not None:
                  if top_k > self.model.config.num_labels:
                         lowerCamelCase     :   Any    				=      self.model.config.num_labels
                  lowerCamelCase     :   Any    				=      top_k
           return {}, {}, postprocess_params







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Dict:
           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  if inputs.startswith("http://"  ) or inputs.startswith("https://"  ):
                         # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                         # like http_huggingface_co.png
                         lowerCamelCase     :   str    				=      requests.get(UpperCamelCase__  ).content
                  else:
                         with open(UpperCamelCase__  ,						"rb"  ) as f:
                                lowerCamelCase     :   int    				=      f.read()

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  lowerCamelCase     :   str    				=      ffmpeg_read(UpperCamelCase__  ,						self.feature_extractor.sampling_rate  )

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  # Accepting `"array"` which is the key defined in `datasets` for
                  # better integration
                  if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                         raise ValueError(
                             "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
                             "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
                             "containing the sampling_rate associated with that array"  )

                  lowerCamelCase     :   Optional[int]    				=      inputs.pop("raw"  ,						UpperCamelCase__  )
                  if _inputs is None:
                         # Remove path which will not be used from `datasets`.
                         inputs.pop("path"  ,						UpperCamelCase__  )
                         lowerCamelCase     :   List[str]    				=      inputs.pop("array"  ,						UpperCamelCase__  )
                  lowerCamelCase     :   str    				=      inputs.pop("sampling_rate"  )
                  lowerCamelCase     :   Union[str, Any]    				=      _inputs
                  if in_sampling_rate != self.feature_extractor.sampling_rate:
                         import torch

                         if is_torchaudio_available():
                                from torchaudio import functional as F
                         else:
                                raise ImportError(
                                    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
                                    "The torchaudio package can be installed through: `pip install torchaudio`."  )

                         lowerCamelCase     :   Tuple    				=      F.resample(
                             torch.from_numpy(UpperCamelCase__  )  ,						UpperCamelCase__  ,						self.feature_extractor.sampling_rate  ).numpy()

           if not isinstance(UpperCamelCase__  ,						np.ndarray  ):
                  raise ValueError("We expect a numpy ndarray as input"  )
           if len(inputs.shape  ) != 1:
                  raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"  )

           lowerCamelCase     :   Optional[int]    				=      self.feature_extractor(
               UpperCamelCase__  ,						sampling_rate=self.feature_extractor.sampling_rate  ,						return_tensors="pt"  )
           return processed







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   str:
           lowerCamelCase     :   Optional[Any]    				=      self.model(**UpperCamelCase__  )
           return model_outputs







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=5  )  ->   Tuple:
           lowerCamelCase     :   Tuple    				=      model_outputs.logits[0].softmax(-1  )
           lowerCamelCase				,   lowerCamelCase     :   str    				=      probs.topk(UpperCamelCase__  )

           lowerCamelCase     :   str    				=      scores.tolist()
           lowerCamelCase     :   Optional[int]    				=      ids.tolist()

           lowerCamelCase     :   Optional[int]    				=      [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase__  ,						UpperCamelCase__  )]

           return labels






