



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


lowercase_								=     logging.get_logger(__name__)
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Tuple     		=["""input_features""", """attention_mask"""]




				def __init__(						self					:						str,					__lowercase					:						Optional[int]=80,					__lowercase					:						Optional[int]=1_6000,					__lowercase					:						Any=80,					__lowercase					:						Optional[Any]=0.0,					__lowercase					:						Optional[Any]=True,					__lowercase					:						Optional[Any]=True,					__lowercase					:						Any=True,					**__lowercase					:						str,					):
							super().__init__(feature_size=__lowercase,					sampling_rate=__lowercase,					padding_value=__lowercase,					**__lowercase						)
							lowercase__					  =				num_mel_bins
							lowercase__					  =				do_ceptral_normalize
							lowercase__					  =				normalize_means
							lowercase__					  =				normalize_vars
							lowercase__					  =				True




				def 							A__      (						self					:						Dict,					__lowercase					:						np.ndarray,					):
							lowercase__					  =				waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
							lowercase__					  =				torch.from_numpy(__lowercase						).unsqueeze(0						)
							lowercase__					  =				ta_kaldi.fbank(__lowercase,					num_mel_bins=self.num_mel_bins,					sample_frequency=self.sampling_rate						)
							return features.numpy()




				@staticmethod
				def 							A__      (						__lowercase					:						np.ndarray,					__lowercase					:						int,					__lowercase					:						Optional[bool] = True,					__lowercase					:						Optional[bool] = True,					__lowercase					:						float = 0.0,					):
							# make sure we normalize float32 arrays
							if normalize_means:
										lowercase__					  =				x[:input_length].mean(axis=0						)
										lowercase__					  =				np.subtract(__lowercase,					__lowercase						)
							if normalize_vars:
										lowercase__					  =				x[:input_length].std(axis=0						)
										lowercase__					  =				np.divide(__lowercase,					__lowercase						)

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

							# make sure array is in float32
							lowercase__					  =				x.astype(np.floataa						)

							return x




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						List[np.ndarray],					__lowercase					:						Optional[np.ndarray] = None						):
							lowercase__					  =				attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [
							    self.utterance_cmvn(__lowercase,					__lowercase,					self.normalize_means,					self.normalize_vars,					self.padding_value						)
							    for x, n in zip(__lowercase,					__lowercase						)
							]




				def __call__(						self					:						Union[str, Any],					__lowercase					:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],					__lowercase					:						Union[bool, str, PaddingStrategy] = False,					__lowercase					:						Optional[int] = None,					__lowercase					:						bool = False,					__lowercase					:						Optional[int] = None,					__lowercase					:						Optional[Union[str, TensorType]] = None,					__lowercase					:						Optional[int] = None,					__lowercase					:						Optional[bool] = None,					**__lowercase					:						Optional[Any],					):

							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."						)

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

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

							# always return batch
							if not is_batched:
										lowercase__					  =				[raw_speech]

							# extract fbank features
							lowercase__					  =				[self._extract_fbank_features(__lowercase						) for waveform in raw_speech]

							# convert into correct format for padding
							lowercase__					  =				BatchFeature({"input_features": features}						)

							lowercase__					  =				self.pad(
							    __lowercase,					padding=__lowercase,					max_length=__lowercase,					truncation=__lowercase,					pad_to_multiple_of=__lowercase,					return_attention_mask=__lowercase,					**__lowercase,					)

							# make sure list is in array format
							lowercase__					  =				padded_inputs.get("input_features"						)
							if isinstance(input_features[0],					__lowercase						):
										lowercase__					  =				[np.asarray(__lowercase,					dtype=np.floataa						) for feature in input_features]

							lowercase__					  =				padded_inputs.get("attention_mask"						)
							if attention_mask is not None:
										lowercase__					  =				[np.asarray(__lowercase,					dtype=np.intaa						) for array in attention_mask]

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

							if return_tensors is not None:
										lowercase__					  =				padded_inputs.convert_to_tensors(__lowercase						)

							return padded_inputs









# 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def __lowerCAmelCase (     ):
			lowercase__					  =				ArgumentParser("Accelerate CLI tool"						,  usage="accelerate <command> [<args>]"						,  allow_abbrev=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.add_subparsers(help="accelerate command helpers"  )

			# Register commands
			get_config_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			env_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			launch_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			tpu_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			test_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )

			# Let's go
			lowercase__					  =				parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_						,  "func"  ):
						parser.print_help()
						exit(1  )

			# Run
			args.func(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							main()




