

from __future__ import annotations

import time

import numpy as np

UpperCAmelCase__ :     List[str]				    =    [8, 5, 9, 7]
UpperCAmelCase__ :     List[str]				    =    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCAmelCase__ :     Union[str, Any]				    =    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class        a__       :







  """simple docstring"""




  def __init__(			self    : int				,				UpperCAmelCase__    : list[int]				,				UpperCAmelCase__    : list[list[int]]				,				UpperCAmelCase__    : list[list[int]]				,				)					->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   claim_vector
        SCREAMING_SNAKE_CASE :      List[str]       =   allocated_resources_table
        SCREAMING_SNAKE_CASE :      Optional[int]       =   maximum_claim_table


  def  _lowercase      (			self    : str		)					->list[int]:
        """simple docstring"""







        return [
            sum(p_item[i] for p_item in self.__allocated_resources_table		)
            for i in range(len(self.__allocated_resources_table[0]		)		)
        ]


  def  _lowercase      (			self    : List[str]		)					->list[int]:
        """simple docstring"""







        return np.array(self.__claim_vector		) - np.array(
            self.__processes_resource_summation()		)


  def  _lowercase      (			self    : List[str]		)					->list[list[int]]:
        """simple docstring"""







        return [
            list(np.array(self.__maximum_claim_table[i]		) - np.array(UpperCAmelCase__		)		)
            for i, allocated_resource in enumerate(self.__allocated_resources_table		)
        ]


  def  _lowercase      (			self    : Dict		)					->dict[int, list[int]]:
        """simple docstring"""







        return {self.__need().index(UpperCAmelCase__		): i for i in self.__need()}


  def  _lowercase      (			self    : str				,				**UpperCAmelCase__    : List[str]		)					->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.__need()
        SCREAMING_SNAKE_CASE :      int       =   self.__allocated_resources_table
        SCREAMING_SNAKE_CASE :      Dict       =   self.__available_resources()
        SCREAMING_SNAKE_CASE :      Any       =   self.__need_index_manager()
        for kw, val in kwargs.items():
              if kw and val is True:
                    self.__pretty_data()
        print("""_""" * 5_0 + """\n"""		)
        while need_list:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   False
              for each_need in need_list:
                    SCREAMING_SNAKE_CASE :      List[Any]       =   True
                    for index, need in enumerate(UpperCAmelCase__		):
                          if need > available_resources[index]:
                                SCREAMING_SNAKE_CASE :      Any       =   False
                                break
                    if execution:
                          SCREAMING_SNAKE_CASE :      Optional[Any]       =   True
                          # get the original index of the process from ind_ctrl db
                          for original_need_index, need_clone in need_index_manager.items():
                                if each_need == need_clone:
                                      SCREAMING_SNAKE_CASE :      Any       =   original_need_index
                          print(f"Process {process_number + 1} is executing."		)
                          # remove the process run from stack
                          need_list.remove(UpperCAmelCase__		)
                          # update available/freed resources stack
                          SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.array(UpperCAmelCase__		) + np.array(
                              alloc_resources_table[process_number]		)
                          print(
                              """Updated available resource stack for processes: """
                              + """ """.join([str(UpperCAmelCase__		) for x in available_resources]		)		)
                          break
              if safe:
                    print("""The process is in a safe state.\n"""		)
              else:
                    print("""System in unsafe state. Aborting...\n"""		)
                    break




  def  _lowercase      (			self    : Tuple		)					->Optional[Any]:
        """simple docstring"""







        print(""" """ * 9 + """Allocated Resource Table"""		)
        for item in self.__allocated_resources_table:
              print(
                  f"P{self.__allocated_resources_table.index(UpperCAmelCase__		) + 1}"
                  + """ """.join(f"{it:>8}" for it in item		)
                  + """\n"""		)
        print(""" """ * 9 + """System Resource Table"""		)
        for item in self.__maximum_claim_table:
              print(
                  f"P{self.__maximum_claim_table.index(UpperCAmelCase__		) + 1}"
                  + """ """.join(f"{it:>8}" for it in item		)
                  + """\n"""		)
        print(
            """Current Usage by Active Processes: """
            + """ """.join(str(UpperCAmelCase__		) for x in self.__claim_vector		)		)
        print(
            """Initial Available Resources:       """
            + """ """.join(str(UpperCAmelCase__		) for x in self.__available_resources()		)		)
        time.sleep(1		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =["""input_features""", """attention_mask"""]


  def __init__(			self    : List[str]				,				UpperCAmelCase__    : Optional[Any]=8_0				,				UpperCAmelCase__    : List[str]=1_6_0_0_0				,				UpperCAmelCase__    : Optional[int]=8_0				,				UpperCAmelCase__    : List[str]=0.0				,				UpperCAmelCase__    : List[Any]=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Tuple=True				,				**UpperCAmelCase__    : Optional[int]				,				)					->Optional[int]:
        """simple docstring"""







        super().__init__(feature_size=UpperCAmelCase__				,				sampling_rate=UpperCAmelCase__				,				padding_value=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_mel_bins
        SCREAMING_SNAKE_CASE :      Dict       =   do_ceptral_normalize
        SCREAMING_SNAKE_CASE :      Optional[int]       =   normalize_means
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   normalize_vars
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   True


  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : np.ndarray				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        SCREAMING_SNAKE_CASE :      Dict       =   torch.from_numpy(UpperCAmelCase__		).unsqueeze(0		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   ta_kaldi.fbank(UpperCAmelCase__				,				num_mel_bins=self.num_mel_bins				,				sample_frequency=self.sampling_rate		)
        return features.numpy()


  @staticmethod
  def  _lowercase      (			UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : float = 0.0				,				)					->np.ndarray:
        """simple docstring"""







        if normalize_means:
              SCREAMING_SNAKE_CASE :      Tuple       =   x[:input_length].mean(axis=0		)
              SCREAMING_SNAKE_CASE :      List[Any]       =   np.subtract(UpperCAmelCase__				,				UpperCAmelCase__		)
        if normalize_vars:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   x[:input_length].std(axis=0		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.divide(UpperCAmelCase__				,				UpperCAmelCase__		)

        if input_length < x.shape[0]:
              SCREAMING_SNAKE_CASE :      Tuple       =   padding_value

        # make sure array is in float32
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   x.astype(np.floataa		)

        return x


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : List[np.ndarray]				,				UpperCAmelCase__    : Optional[np.ndarray] = None		)					->List[np.ndarray]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(UpperCAmelCase__				,				UpperCAmelCase__				,				self.normalize_means				,				self.normalize_vars				,				self.padding_value		)
            for x, n in zip(UpperCAmelCase__				,				UpperCAmelCase__		)
        ]




  def __call__(			self    : str				,				UpperCAmelCase__    : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,				UpperCAmelCase__    : Union[bool, str, PaddingStrategy] = False				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : bool = False				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				**UpperCAmelCase__    : Optional[int]				,				)					->BatchFeature:
        """simple docstring"""







        if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                        f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                        f" {self.sampling_rate} and not {sampling_rate}."		)
        else:
              logger.warning(
                  """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                  """Failing to do so can result in silent errors that might be hard to debug."""		)

        SCREAMING_SNAKE_CASE :      Tuple       =   isinstance(UpperCAmelCase__				,				np.ndarray		) and len(raw_speech.shape		) > 1
        if is_batched_numpy and len(raw_speech.shape		) > 2:
              raise ValueError(f"Only mono-channel audio is supported for input to {self}"		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   is_batched_numpy or (
            isinstance(UpperCAmelCase__				,				(list, tuple)		) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)		))
        )

        if is_batched:
              SCREAMING_SNAKE_CASE :      Dict       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for speech in raw_speech]
        elif not is_batched and not isinstance(UpperCAmelCase__				,				np.ndarray		):
              SCREAMING_SNAKE_CASE :      Any       =   np.asarray(UpperCAmelCase__				,				dtype=np.floataa		)
        elif isinstance(UpperCAmelCase__				,				np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
              SCREAMING_SNAKE_CASE :      Tuple       =   raw_speech.astype(np.floataa		)

        # always return batch
        if not is_batched:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   [raw_speech]

        # extract fbank features
        SCREAMING_SNAKE_CASE :      Tuple       =   [self._extract_fbank_features(UpperCAmelCase__		) for waveform in raw_speech]

        # convert into correct format for padding
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   BatchFeature({"""input_features""": features}		)

        SCREAMING_SNAKE_CASE :      str       =   self.pad(
            UpperCAmelCase__				,				padding=UpperCAmelCase__				,				max_length=UpperCAmelCase__				,				truncation=UpperCAmelCase__				,				pad_to_multiple_of=UpperCAmelCase__				,				return_attention_mask=UpperCAmelCase__				,				**UpperCAmelCase__				,				)

        # make sure list is in array format
        SCREAMING_SNAKE_CASE :      Dict       =   padded_inputs.get("""input_features"""		)
        if isinstance(input_features[0]				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for feature in input_features]

        SCREAMING_SNAKE_CASE :      Dict       =   padded_inputs.get("""attention_mask"""		)
        if attention_mask is not None:
              SCREAMING_SNAKE_CASE :      int       =   [np.asarray(UpperCAmelCase__				,				dtype=np.intaa		) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
              SCREAMING_SNAKE_CASE :      Tuple       =   (
                  np.array(UpperCAmelCase__				,				dtype=np.intaa		)
                  if self._get_padding_strategies(UpperCAmelCase__				,				max_length=UpperCAmelCase__		) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              SCREAMING_SNAKE_CASE :      str       =   self.normalize(
                  padded_inputs["""input_features"""]				,				attention_mask=UpperCAmelCase__		)

        if return_tensors is not None:
              SCREAMING_SNAKE_CASE :      Any       =   padded_inputs.convert_to_tensors(UpperCAmelCase__		)

        return padded_inputs




