




from typing import Any

import numpy as np

def   A   (   _SCREAMING_SNAKE_CASE					)			->				bool:

       '''simple docstring'''





       assert np.shape(_SCREAMING_SNAKE_CASE					)[0] == np.shape(_SCREAMING_SNAKE_CASE					)[1]

       # If matrix not symmetric, exit right away.
       if np.allclose(_SCREAMING_SNAKE_CASE				,matrix.T					) is False:
              return False

       # Get eigenvalues and eignevectors for a symmetric matrix.
       lowerCamelCase     :   Optional[int]    				=      np.linalg.eigh(_SCREAMING_SNAKE_CASE					)

       # Check sign of all eigenvalues.
       # np.all returns a value of type np.bool_
       return bool(np.all(eigen_values > 0					)					)

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Any:

       '''simple docstring'''





       lowerCamelCase     :   Tuple    				=      np.random.randn(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Optional[Any]    				=      np.dot(_SCREAMING_SNAKE_CASE				,random_matrix.T					)
       assert _is_matrix_spd(_SCREAMING_SNAKE_CASE					)
       return spd_matrix

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE = 1000				,_SCREAMING_SNAKE_CASE = 1e-8				,)			->				Any:

       '''simple docstring'''





       assert np.shape(_SCREAMING_SNAKE_CASE					)[0] == np.shape(_SCREAMING_SNAKE_CASE					)[1]
       assert np.shape(_SCREAMING_SNAKE_CASE					)[0] == np.shape(_SCREAMING_SNAKE_CASE					)[0]
       assert _is_matrix_spd(_SCREAMING_SNAKE_CASE					)

       # Initialize solution guess, residual, search direction.
       lowerCamelCase     :   Optional[Any]    				=      np.zeros((np.shape(_SCREAMING_SNAKE_CASE					)[0], 1)					)
       lowerCamelCase     :   Optional[int]    				=      np.copy(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Union[str, Any]    				=      np.copy(_SCREAMING_SNAKE_CASE					)

       # Set initial errors in solution guess and residual.
       lowerCamelCase     :   Tuple    				=      1e9
       lowerCamelCase     :   Dict    				=      1e9
       lowerCamelCase     :   str    				=      1e9

       # Set iteration counter to threshold number of iterations.
       lowerCamelCase     :   str    				=      0

       while error > tol:
              # Save this value so we only calculate the matrix-vector product once.
              lowerCamelCase     :   Optional[int]    				=      np.dot(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

              # The main algorithm.

              # Update search direction magnitude.
              lowerCamelCase     :   int    				=      np.dot(ra.T				,_SCREAMING_SNAKE_CASE					) / np.dot(pa.T				,_SCREAMING_SNAKE_CASE					)
              # Update solution guess.
              lowerCamelCase     :   Optional[int]    				=      xa + alpha * pa
              # Calculate new residual.
              lowerCamelCase     :   List[Any]    				=      ra - alpha * w
              # Calculate new Krylov subspace scale.
              lowerCamelCase     :   List[str]    				=      np.dot(r.T				,_SCREAMING_SNAKE_CASE					) / np.dot(ra.T				,_SCREAMING_SNAKE_CASE					)
              # Calculate new A conjuage search direction.
              lowerCamelCase     :   Union[str, Any]    				=      r + beta * pa

              # Calculate errors.
              lowerCamelCase     :   List[str]    				=      np.linalg.norm(r - ra					)
              lowerCamelCase     :   str    				=      np.linalg.norm(x - xa					)
              lowerCamelCase     :   List[Any]    				=      np.maximum(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

              # Update variables.
              lowerCamelCase     :   List[str]    				=      np.copy(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   List[str]    				=      np.copy(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Union[str, Any]    				=      np.copy(_SCREAMING_SNAKE_CASE					)

              # Update number of iterations.
              iterations += 1
              if iterations > max_iterations:
                     break

       return x

def   A   (   )			->				None:

       '''simple docstring'''





       lowerCamelCase     :   int    				=      3
       lowerCamelCase     :   int    				=      _create_spd_matrix(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Optional[int]    				=      np.random.randn(_SCREAMING_SNAKE_CASE				,1					)
       lowerCamelCase     :   Dict    				=      np.dot(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

       # Numpy solution.
       lowerCamelCase     :   int    				=      np.linalg.solve(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

       # Our implementation.
       lowerCamelCase     :   Optional[Any]    				=      conjugate_gradient(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

       # Ensure both solutions are close to x_true (and therefore one another).
       assert np.linalg.norm(x_numpy - x_true					) <= 1e-6
       assert np.linalg.norm(x_conjugate_gradient - x_true					) <= 1e-6


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       test_conjugate_gradient()












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






