
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


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							str     =	["input_features"]





     def __init__(self   :					str       ,					_A   :					List[str]=8_0       ,					_A   :					Union[str, Any]=1_6_0_0_0       ,					_A   :					Tuple=1_6_0       ,					_A   :					int=3_0       ,					_A   :					int=4_0_0       ,					_A   :					int=0.0       ,					_A   :					int=False       ,					**_A   :					str       ,					)		->		List[str]:
         super().__init__(
             feature_size=_A       ,					sampling_rate=_A       ,					padding_value=_A       ,					return_attention_mask=_A       ,					**_A       ,					)
         snake_case				  =      n_fft
         snake_case				  =      hop_length
         snake_case				  =      chunk_length
         snake_case				  =      chunk_length * sampling_rate
         snake_case				  =      self.n_samples // hop_length
         snake_case				  =      sampling_rate
         snake_case				  =      mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2       ,					num_mel_filters=_A       ,					min_frequency=0.0       ,					max_frequency=80_00.0       ,					sampling_rate=_A       ,					norm="slaney"       ,					mel_scale="slaney"       ,					)





     def 	UpperCAmelCase(self   :					Any       ,					_A   :					np.array     )		->		np.ndarray:
         snake_case				  =      spectrogram(
             _A       ,					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				  =      log_spec[:, :-1]
         snake_case				  =      np.maximum(_A       ,					log_spec.max() - 8.0     )
         snake_case				  =      (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(_A   :					List[np.ndarray]       ,					_A   :					List[np.ndarray]       ,					_A   :					float = 0.0     )		->		List[np.ndarray]:
         if attention_mask is not None:
             snake_case				  =      np.array(_A       ,					np.intaa     )
             snake_case				  =      []

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

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

         return normed_input_values





     def __call__(self   :					List[str]       ,					_A   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,					_A   :					bool = True       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[Union[str, TensorType]] = None       ,					_A   :					Optional[bool] = None       ,					_A   :					Optional[str] = "max_length"       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[bool] = None       ,					**_A   :					List[str]       ,					)		->		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				  =      isinstance(_A       ,					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				  =      is_batched_numpy or (
             isinstance(_A       ,					(list, tuple)     ) and (isinstance(raw_speech[0]       ,					(np.ndarray, tuple, list)     ))
         )

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

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

         snake_case				  =      BatchFeature({"input_features": raw_speech}     )

         # convert into correct format for padding

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

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

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

         snake_case				  =      [self._np_extract_fbank_features(_A     ) for waveform in input_features[0]]

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

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

         if return_tensors is not None:
             snake_case				  =      padded_inputs.convert_to_tensors(_A     )

         return padded_inputs





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



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def 		lowercase_    (     A__				,	A__=()				,	A__=None				,	A__="no"				,	A__="29500"	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      False
    snake_case				  =      False
    if any(key.startswith("KAGGLE"	) for key in os.environ.keys()	):
        snake_case				  =      True
    elif "IPython" in sys.modules:
        snake_case				  =      "google.colab" in str(sys.modules["IPython"].get_ipython()	)

    try:
        snake_case				  =      PrecisionType(mixed_precision.lower()	)
    except ValueError:
        raise ValueError(
            F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'	)

    if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"				,	A__	) is not None):
        # TPU launch
        import torch_xla.distributed.xla_multiprocessing as xmp

        if len(AcceleratorState._shared_state	) > 0:
            raise ValueError(
                "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                "your training function. Restart your notebook and make sure no cells initializes an "
                "`Accelerator`."	)
        if num_processes is None:
            snake_case				  =      8

        snake_case				  =      PrepareForLaunch(A__				,	distributed_type="TPU"	)
        print(F'Launching a training on {num_processes} TPU cores.'	)
        xmp.spawn(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
    elif in_colab:
        # No need for a distributed launch otherwise as it's either CPU or one GPU.
        if torch.cuda.is_available():
            print("Launching training on one GPU."	)
        else:
            print("Launching training on one CPU."	)
        function(*A__	)
    else:
        if num_processes is None:
            raise ValueError(
                "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."	)

        if num_processes > 1:
            # Multi-GPU launch
            from torch.multiprocessing import start_processes
            from torch.multiprocessing.spawn import ProcessRaisedException

            if len(AcceleratorState._shared_state	) > 0:
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                    "inside your training function. Restart your notebook and make sure no cells initializes an "
                    "`Accelerator`."	)

            if torch.cuda.is_initialized():
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                    "function."	)

            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=A__				,	master_addr="127.0.01"				,	master_port=A__				,	mixed_precision=A__	):
                snake_case				  =      PrepareForLaunch(A__				,	distributed_type="MULTI_GPU"	)
                print(F'Launching training on {num_processes} GPUs.'	)
                try:
                    start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
                except ProcessRaisedException as e:
                    if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                        raise RuntimeError(
                            "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                            "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                            "Please review your imports and test them when running the `notebook_launcher()` to identify "
                            "which one is problematic."	) from e

        else:
            # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
            if is_mps_available():
                snake_case				  =      "1"
                print("Launching training on MPS."	)
            elif torch.cuda.is_available():
                print("Launching training on one GPU."	)
            else:
                print("Launching training on CPU."	)
            function(*A__	)





def 		lowercase_    (     A__				,	A__=()				,	A__=2	)							->							Dict:
    """simple docstring"""

    from torch.multiprocessing import start_processes

    with tempfile.NamedTemporaryFile() as tmp_file:
        # torch.distributed will expect a few environment variable to be here. We set the ones common to each
        # process here (the other ones will be set be the launcher).
        with patch_environment(
            world_size=A__				,	master_addr="127.0.01"				,	master_port="29500"				,	accelerate_mixed_precision="no"				,	accelerate_debug_rdv_file=tmp_file.name				,	accelerate_use_cpu="yes"				,	):
            snake_case				  =      PrepareForLaunch(A__				,	debug=A__	)
            start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)

