


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


_snake_case	:						Tuple         =							logging.get_logger(__name__)

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[int]	    =    ["input_features"]

							def __init__(				self			:    int       ,      lowerCamelCase			:    str=80       ,      lowerCamelCase			:    List[Any]=16000       ,      lowerCamelCase			:    Dict=160       ,      lowerCamelCase			:    int=30       ,      lowerCamelCase			:    Any=400       ,      lowerCamelCase			:    Optional[Any]=0.0       ,      lowerCamelCase			:    Any=False       ,      **lowerCamelCase			:    Optional[Any]       ,      )      ->       Dict:
														super().__init__(
														    feature_size=lowerCamelCase       ,      sampling_rate=lowerCamelCase       ,      padding_value=lowerCamelCase       ,      return_attention_mask=lowerCamelCase       ,      **lowerCamelCase       ,      )
														__snake_case						:     Union[str, Any]									=  n_fft
														__snake_case						:     List[Any]									=  hop_length
														__snake_case						:     Optional[Any]									=  chunk_length
														__snake_case						:     List[str]									=  chunk_length * sampling_rate
														__snake_case						:     str									=  self.n_samples // hop_length
														__snake_case						:     Any									=  sampling_rate
														__snake_case						:     Dict									=  mel_filter_bank(
														    num_frequency_bins=1 + n_fft // 2       ,      num_mel_filters=lowerCamelCase       ,      min_frequency=0.0       ,      max_frequency=80_00.0       ,      sampling_rate=lowerCamelCase       ,      norm="slaney"       ,      mel_scale="slaney"       ,      )

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    np.array							)      ->       np.ndarray:
														__snake_case						:     Union[str, Any]									=  spectrogram(
														    lowerCamelCase       ,      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"       ,      )
														__snake_case						:     Dict									=  log_spec[:, :-1]
														__snake_case						:     Any									=  np.maximum(lowerCamelCase       ,      log_spec.max() - 8.0							)
														__snake_case						:     Union[str, Any]									=  (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        __snake_case   (				lowerCamelCase			:    List[np.ndarray]       ,      lowerCamelCase			:    List[np.ndarray]       ,      lowerCamelCase			:    float = 0.0							)      ->       List[np.ndarray]:
														if attention_mask is not None:
																					__snake_case						:     List[str]									=  np.array(lowerCamelCase       ,      np.intaa							)
																					__snake_case						:     int									=  []

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

																												normed_input_values.append(lowerCamelCase							)
														else:
																					__snake_case						:     List[str]									=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

														return normed_input_values

							def __call__(				self			:    Any       ,      lowerCamelCase			:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    Optional[int] = None       ,      lowerCamelCase			:    Optional[Union[str, TensorType]] = None       ,      lowerCamelCase			:    Optional[bool] = None       ,      lowerCamelCase			:    Optional[str] = "max_length"       ,      lowerCamelCase			:    Optional[int] = None       ,      lowerCamelCase			:    Optional[int] = None       ,      lowerCamelCase			:    Optional[bool] = None       ,      **lowerCamelCase			:    Optional[Any]       ,      )      ->       BatchFeature:

														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."							)

														__snake_case						:     Any									=  isinstance(lowerCamelCase       ,      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}'							)
														__snake_case						:     Union[str, Any]									=  is_batched_numpy or (
														    isinstance(lowerCamelCase       ,      (list, tuple)							) and (isinstance(raw_speech[0]       ,      (np.ndarray, tuple, list)							))
														)

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

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

														__snake_case						:     List[str]									=  BatchFeature({"input_features": raw_speech}							)

														# convert into correct format for padding

														__snake_case						:     Union[str, Any]									=  self.pad(
														    lowerCamelCase       ,      padding=lowerCamelCase       ,      max_length=max_length if max_length else self.n_samples       ,      truncation=lowerCamelCase       ,      pad_to_multiple_of=lowerCamelCase       ,      return_attention_mask=return_attention_mask or do_normalize       ,      )

														# zero-mean and unit-variance normalization
														if do_normalize:
																					__snake_case						:     List[str]									=  self.zero_mean_unit_var_norm(
																					    padded_inputs["input_features"]       ,      attention_mask=padded_inputs["attention_mask"]       ,      padding_value=self.padding_value       ,      )
																					__snake_case						:     Optional[int]									=  np.stack(padded_inputs["input_features"]       ,      axis=0							)

														# make sure list is in array format
														__snake_case						:     Optional[int]									=  padded_inputs.get("input_features"							).transpose(2       ,      0       ,      1							)

														__snake_case						:     Tuple									=  [self._np_extract_fbank_features(lowerCamelCase							) for waveform in input_features[0]]

														if isinstance(input_features[0]       ,      lowerCamelCase							):
																					__snake_case						:     Dict									=  [np.asarray(lowerCamelCase       ,      dtype=np.floataa							) for feature in input_features]
														else:
																					__snake_case						:     Any									=  input_features

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

														if return_tensors is not None:
																					__snake_case						:     Dict									=  padded_inputs.convert_to_tensors(lowerCamelCase							)

														return padded_inputs






							def        __snake_case   (				self			:    str							)      ->       Dict[str, Any]:
														__snake_case						:     Tuple									=  copy.deepcopy(self.__dict__							)
														__snake_case						:     str									=  self.__class__.__name__
														if "mel_filters" in output:
																					del output["mel_filters"]
														return output







# Copyright 2021 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

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowerCamelCase      :						Optional[Any]							=  """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""







def SCREAMING_SNAKE_CASE							(			)  ->			Dict:



       """simple docstring"""

       A__ 					=       _ask_options(
           '''In which compute environment are you running?'''					,							['''This machine''', '''AWS (Amazon SageMaker)''']					,							_convert_compute_environment					,							)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A__ 					=       get_sagemaker_input()
       else:
              A__ 					=       get_cluster_input()
       return config







def SCREAMING_SNAKE_CASE							(			lowercase_=None		)  ->			List[Any]:



       """simple docstring"""

       if subparsers is not None:
              A__ 					=       subparsers.add_parser('''config'''					,							description=lowercase_		)
       else:
              A__ 					=       argparse.ArgumentParser('''Accelerate config command'''					,							description=lowercase_		)

       parser.add_argument(
           '''--config_file'''					,							default=lowercase_					,							help=(
               '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
               '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
               '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
               '''with \'huggingface\'.'''
           )					,							)

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Any:



       """simple docstring"""

       A__ 					=       get_user_input()
       if args.config_file is not None:
              A__ 					=       args.config_file
       else:
              if not os.path.isdir(lowercase_		):
                     os.makedirs(lowercase_		)
              A__ 					=       default_yaml_config_file

       if config_file.endswith('''.json'''		):
              config.to_json_file(lowercase_		)
       else:
              config.to_yaml_file(lowercase_		)
       print(f"""accelerate configuration saved at {config_file}"""		)







def SCREAMING_SNAKE_CASE							(			)  ->			Union[str, Any]:



       """simple docstring"""

       A__ 					=       config_command_parser()
       A__ 					=       parser.parse_args()
       config_command(lowercase_		)


if __name__ == "__main__":
  main()


