


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase__  :Dict             =							logging.get_logger(__name__)

class 		__a				(    snake_case_      ):
  _a     :  List[Any]           =     ['input_features']
  def __init__(      self      ,   _SCREAMING_SNAKE_CASE=80      ,   _SCREAMING_SNAKE_CASE=16000      ,   _SCREAMING_SNAKE_CASE=160      ,   _SCREAMING_SNAKE_CASE=30      ,   _SCREAMING_SNAKE_CASE=400      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=False      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Union[str, Any]:




         """simple docstring"""





         super().__init__(
             feature_size=_SCREAMING_SNAKE_CASE      ,   sampling_rate=_SCREAMING_SNAKE_CASE      ,   padding_value=_SCREAMING_SNAKE_CASE      ,   return_attention_mask=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
         _UpperCAmelCase					=      n_fft
         _UpperCAmelCase					=      hop_length
         _UpperCAmelCase					=      chunk_length
         _UpperCAmelCase					=      chunk_length * sampling_rate
         _UpperCAmelCase					=      self.n_samples // hop_length
         _UpperCAmelCase					=      sampling_rate
         _UpperCAmelCase					=      mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2      ,   num_mel_filters=_SCREAMING_SNAKE_CASE      ,   min_frequency=0.0      ,   max_frequency=8000.0      ,   sampling_rate=_SCREAMING_SNAKE_CASE      ,   norm='slaney'      ,   mel_scale='slaney'      ,   )
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	np.ndarray:




         """simple docstring"""





         _UpperCAmelCase					=      spectrogram(
             _SCREAMING_SNAKE_CASE      ,   window_function(self.n_fft      ,   'hann'						)      ,   frame_length=self.n_fft      ,   hop_length=self.hop_length      ,   power=2.0      ,   mel_filters=self.mel_filters      ,   log_mel='log10'      ,   )
         _UpperCAmelCase					=      log_spec[:, :-1]
         _UpperCAmelCase					=      np.maximum(_SCREAMING_SNAKE_CASE      ,   log_spec.max() - 8.0						)
         _UpperCAmelCase					=      (log_spec + 4.0) / 4.0
         return log_spec
  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def      UpperCAmelCase__    (      _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 0.0						)			->	List[np.ndarray]:




         """simple docstring"""





         if attention_mask is not None:
                _UpperCAmelCase					=      np.array(_SCREAMING_SNAKE_CASE      ,   np.intaa						)
                _UpperCAmelCase					=      []

                for vector, length in zip(_SCREAMING_SNAKE_CASE      ,   attention_mask.sum(-1						)						):
                       _UpperCAmelCase					=      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
                       if length < normed_slice.shape[0]:
                              _UpperCAmelCase					=      padding_value

                       normed_input_values.append(_SCREAMING_SNAKE_CASE						)
         else:
                _UpperCAmelCase					=      [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

         return normed_input_values
  def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = "max_length"      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	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.__class__.__name__} was trained using a'''
                           f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                           f''' was sampled with {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.'						)

         _UpperCAmelCase					=      isinstance(_SCREAMING_SNAKE_CASE      ,   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}'''						)
         _UpperCAmelCase					=      is_batched_numpy or (
             isinstance(_SCREAMING_SNAKE_CASE      ,   (list, tuple)						) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list)						))
         )

         if is_batched:
                _UpperCAmelCase					=      [np.asarray([speech]      ,   dtype=np.floataa						).T for speech in raw_speech]
         elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE      ,   np.ndarray						):
                _UpperCAmelCase					=      np.asarray(_SCREAMING_SNAKE_CASE      ,   dtype=np.floataa						)
         elif isinstance(_SCREAMING_SNAKE_CASE      ,   np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
                _UpperCAmelCase					=      raw_speech.astype(np.floataa						)

         # always return batch
         if not is_batched:
                _UpperCAmelCase					=      [np.asarray([raw_speech]						).T]

         _UpperCAmelCase					=      BatchFeature({'input_features': raw_speech}						)

         # convert into correct format for padding

         _UpperCAmelCase					=      self.pad(
             _SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   max_length=max_length if max_length else self.n_samples      ,   truncation=_SCREAMING_SNAKE_CASE      ,   pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,   return_attention_mask=return_attention_mask or do_normalize      ,   )

         # zero-mean and unit-variance normalization
         if do_normalize:
                _UpperCAmelCase					=      self.zero_mean_unit_var_norm(
                    padded_inputs['input_features']      ,   attention_mask=padded_inputs['attention_mask']      ,   padding_value=self.padding_value      ,   )
                _UpperCAmelCase					=      np.stack(padded_inputs['input_features']      ,   axis=0						)

         # make sure list is in array format
         _UpperCAmelCase					=      padded_inputs.get('input_features'						).transpose(2      ,   0      ,   1						)

         _UpperCAmelCase					=      [self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

         if isinstance(input_features[0]      ,   _SCREAMING_SNAKE_CASE						):
                _UpperCAmelCase					=      [np.asarray(_SCREAMING_SNAKE_CASE      ,   dtype=np.floataa						) for feature in input_features]
         else:
                _UpperCAmelCase					=      input_features

         if return_attention_mask:
                # rescale from sample (48000) to feature (3000)
                _UpperCAmelCase					=      padded_inputs['attention_mask'][:, :: self.hop_length]

         if return_tensors is not None:
                _UpperCAmelCase					=      padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE						)

         return padded_inputs






  def      UpperCAmelCase__    (      self						)			->	Dict[str, Any]:




         """simple docstring"""





         _UpperCAmelCase					=      copy.deepcopy(self.__dict__						)
         _UpperCAmelCase					=      self.__class__.__name__
         if "mel_filters" in output:
                del output["mel_filters"]
         return output




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__  :Dict             =							'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''

def 	lowerCAmelCase__			(      a__:		Optional[Any]=None	)  ->		List[Any]:


							'''simple docstring'''





							if subparsers is not None:
														_UpperCAmelCase					=      subparsers.add_parser('tpu-config'      ,  description=_description	)
							else:
														_UpperCAmelCase					=      argparse.ArgumentParser('Accelerate tpu-config command'      ,  description=_description	)
							# Core arguments
							_UpperCAmelCase					=      parser.add_argument_group(
							    'Config Arguments'      ,  'Arguments that can be configured through `accelerate config`.'	)
							config_args.add_argument(
							    '--config_file'      ,  type=a__      ,  default=a__      ,  help='Path to the config file to use for accelerate.'      ,  )
							config_args.add_argument(
							    '--tpu_name'      ,  default=a__      ,  help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.'      ,  )
							config_args.add_argument(
							    '--tpu_zone'      ,  default=a__      ,  help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'      ,  )
							_UpperCAmelCase					=      parser.add_argument_group('TPU Arguments'      ,  'Arguments for options ran inside the TPU.'	)
							pod_args.add_argument(
							    '--use_alpha'      ,  action='store_true'      ,  help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'      ,  )
							pod_args.add_argument(
							    '--command_file'      ,  default=a__      ,  help='The path to the file containing the commands to run on the pod on startup.'      ,  )
							pod_args.add_argument(
							    '--command'      ,  action='append'      ,  nargs='+'      ,  help='A command to run on the pod. Can be passed multiple times.'      ,  )
							pod_args.add_argument(
							    '--install_accelerate'      ,  action='store_true'      ,  help='Whether to install accelerate on the pod. Defaults to False.'      ,  )
							pod_args.add_argument(
							    '--accelerate_version'      ,  default='latest'      ,  help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'      ,  )
							pod_args.add_argument(
							    '--debug'      ,  action='store_true'      ,  help='If set, will print the command that would be run instead of running it.'	)

							if subparsers is not None:
														parser.set_defaults(func=a__	)
							return parser

def 	lowerCAmelCase__			(      a__:		str	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(a__	):
														_UpperCAmelCase					=      load_config_from_file(args.config_file	)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					_UpperCAmelCase					=      defaults.command_file
														if not args.command and defaults.commands is not None:
																					_UpperCAmelCase					=      defaults.commands
														if not args.tpu_name:
																					_UpperCAmelCase					=      defaults.tpu_name
														if not args.tpu_zone:
																					_UpperCAmelCase					=      defaults.tpu_zone
							if args.accelerate_version == "dev":
														_UpperCAmelCase					=      'git+https://github.com/huggingface/accelerate.git'
							elif args.accelerate_version == "latest":
														_UpperCAmelCase					=      'accelerate -U'
							elif isinstance(parse(args.accelerate_version	)      ,  a__	):
														_UpperCAmelCase					=      F'''accelerate=={args.accelerate_version}'''

							if not args.command_file and not args.command:
														raise ValueError('You must specify either a command file or a command to run on the pod.'	)

							if args.command_file:
														with open(args.command_file      ,  'r'	) as f:
																					_UpperCAmelCase					=      [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]      ,  a__	):
														_UpperCAmelCase					=      [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							_UpperCAmelCase					=      ['cd /usr/share']
							if args.install_accelerate:
														new_cmd += [F'''pip install {args.accelerate_version}''']
							new_cmd += args.command
							_UpperCAmelCase					=      '; '.join(a__	)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_UpperCAmelCase					=      ['gcloud']
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'''Running {" ".join(a__	)}'''	)
														return
							subprocess.run(a__	)
							print('Successfully setup pod.'	)







def 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tpu_command_parser()
							_UpperCAmelCase					=      parser.parse_args()

							tpu_command_launcher(a__	)
