





'''simple docstring'''

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

import numpy as np
import torch

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   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""input_features""", """is_longer"""]
 def __init__(							self :Dict		,			lowercase_ :List[str]=64		,			lowercase_ :Optional[int]=4_80_00		,			lowercase_ :str=4_80		,			lowercase_ :Optional[Any]=10		,			lowercase_ :Dict=10_24		,			lowercase_ :List[str]=0.0		,			lowercase_ :Tuple=False		,			lowercase_ :float = 0		,			lowercase_ :float = 1_40_00		,			lowercase_ :int = None		,			lowercase_ :str = "fusion"		,			lowercase_ :str = "repeatpad"		,			**lowercase_ :List[Any]		,			)->				Dict:
     super().__init__(
         feature_size=lowercase_		,			sampling_rate=lowercase_		,			padding_value=lowercase_		,			return_attention_mask=lowercase_		,			**lowercase_		,			)
     A__							=     top_db
     A__							=     truncation
     A__							=     padding
     A__							=     fft_window_size
     A__							=     (fft_window_size >> 1) + 1
     A__							=     hop_length
     A__							=     max_length_s
     A__							=     max_length_s * sampling_rate
     A__							=     sampling_rate
     A__							=     frequency_min
     A__							=     frequency_max
     A__							=     mel_filter_bank(
         num_frequency_bins=self.nb_frequency_bins		,			num_mel_filters=lowercase_		,			min_frequency=lowercase_		,			max_frequency=lowercase_		,			sampling_rate=lowercase_		,			norm=lowercase_		,			mel_scale="htk"		,			)
     A__							=     mel_filter_bank(
         num_frequency_bins=self.nb_frequency_bins		,			num_mel_filters=lowercase_		,			min_frequency=lowercase_		,			max_frequency=lowercase_		,			sampling_rate=lowercase_		,			norm="slaney"		,			mel_scale="slaney"		,			)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Dict[str, Any]:
     A__							=     copy.deepcopy(self.__dict__				)
     A__							=     self.__class__.__name__
     if "mel_filters" in output:
         del output["mel_filters"]
     if "mel_filters_slaney" in output:
         del output["mel_filters_slaney"]
     return output
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :np.array		,			lowercase_ :Optional[np.array] = None				)->				np.ndarray:
     A__							=     spectrogram(
         lowercase_		,			window_function(self.fft_window_size		,			"hann"				)		,			frame_length=self.fft_window_size		,			hop_length=self.hop_length		,			power=2.0		,			mel_filters=lowercase_		,			log_mel="dB"		,			)
     return log_mel_spectrogram.T
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :Dict		,			lowercase_ :List[Any]		,			lowercase_ :int				)->				Any:
     A__							=     np.array_split(list(range(0		,			total_frames - chunk_frames + 1				)				)		,			3				)
     if len(ranges[1]				) == 0:
         # if the audio is too short, we just use the first chunk
         A__							=     [0]
     if len(ranges[2]				) == 0:
         # if the audio is too short, we just use the first chunk
         A__							=     [0]
     # randomly choose index for each part
     A__							=     np.random.choice(ranges[0]				)
     A__							=     np.random.choice(ranges[1]				)
     A__							=     np.random.choice(ranges[2]				)

     A__							=     mel[idx_front : idx_front + chunk_frames, :]
     A__							=     mel[idx_middle : idx_middle + chunk_frames, :]
     A__							=     mel[idx_back : idx_back + chunk_frames, :]

     A__							=     torch.tensor(mel[None, None, :]				)
     A__							=     torch.nn.functional.interpolate(
         lowercase_		,			size=[chunk_frames, 64]		,			mode="bilinear"		,			align_corners=lowercase_				)
     A__							=     mel_shrink[0][0].numpy()
     A__							=     np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]		,			axis=0				)
     return mel_fusion
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :np.array		,			lowercase_ :Any		,			lowercase_ :Tuple		,			lowercase_ :int				)->				np.array:
     if waveform.shape[0] > max_length:
         if truncation == "rand_trunc":
             A__							=     True
             # random crop to max_length (for compatibility) -> this should be handled by self.pad
             A__							=     len(lowercase_				) - max_length
             A__							=     np.random.randint(0		,			overflow + 1				)
             A__							=     waveform[idx : idx + max_length]
             A__							=     self._np_extract_fbank_features(lowercase_		,			self.mel_filters_slaney				)[None, :]
         elif truncation == "fusion":
             A__							=     self._np_extract_fbank_features(lowercase_		,			self.mel_filters				)
             A__							=     max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
             A__							=     mel.shape[0]
             if chunk_frames == total_frames:
                 # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                 # In this case, we just use the whole audio.
                 A__							=     np.stack([mel, mel, mel, mel]		,			axis=0				)
                 A__							=     False
             else:
                 A__							=     self._random_mel_fusion(lowercase_		,			lowercase_		,			lowercase_				)
                 A__							=     True
         else:
             raise NotImplementedError(F"data_truncating {truncation} not implemented"				)

     else:
         A__							=     False
         # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
         if waveform.shape[0] < max_length:
             if padding == "repeat":
                 A__							=     int(max_length / len(lowercase_				)				)
                 A__							=     np.stack(np.tile(lowercase_		,			n_repeat + 1				)				)[:max_length]
             if padding == "repeatpad":
                 A__							=     int(max_length / len(lowercase_				)				)
                 A__							=     np.stack(np.tile(lowercase_		,			lowercase_				)				)
             A__							=     np.pad(lowercase_		,			(0, max_length - waveform.shape[0])		,			mode="constant"		,			constant_values=0				)

         if truncation == "fusion":
             A__							=     self._np_extract_fbank_features(lowercase_		,			self.mel_filters				)
             A__							=     np.stack([input_mel, input_mel, input_mel, input_mel]		,			axis=0				)
         else:
             A__							=     self._np_extract_fbank_features(lowercase_		,			self.mel_filters_slaney				)[None, :]

     return input_mel, longer



 def __call__(							self :List[Any]		,			lowercase_ :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,			lowercase_ :str = None		,			lowercase_ :Optional[str] = None		,			lowercase_ :Optional[int] = None		,			lowercase_ :Optional[int] = None		,			lowercase_ :Optional[Union[str, TensorType]] = None		,			**lowercase_ :Optional[int]		,			)->				BatchFeature:
     A__							=     truncation if truncation is not None else self.truncation
     A__							=     padding if padding else self.padding

     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."				)

     A__							=     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}"				)
     A__							=     is_batched_numpy or (
         isinstance(lowercase_		,			(list, tuple)				) and (isinstance(raw_speech[0]		,			(np.ndarray, tuple, list)				))
     )

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

     # always return batch
     if not is_batched:
         A__							=     [np.asarray(lowercase_				)]

     # convert to mel spectrogram, truncate and pad if needed.
     A__							=     [
         self._get_input_mel(lowercase_		,			max_length if max_length else self.nb_max_samples		,			lowercase_		,			lowercase_				)
         for waveform in raw_speech
     ]

     A__							=     []
     A__							=     []
     for mel, longer in padded_inputs:
         input_mel.append(lowercase_				)
         is_longer.append(lowercase_				)

     if truncation == "fusion" and sum(lowercase_				) == 0:
         # if no audio is longer than 10s, then randomly select one audio to be longer
         A__							=     np.random.randint(0		,			len(lowercase_				)				)
         A__							=     True

     if isinstance(input_mel[0]		,			lowercase_				):
         A__							=     [np.asarray(lowercase_		,			dtype=np.floataa				) for feature in input_mel]

     # is_longer is a list of bool
     A__							=     [[longer] for longer in is_longer]

     A__							=     {"input_features": input_mel, "is_longer": is_longer}
     A__							=     BatchFeature(lowercase_				)

     if return_tensors is not None:
         A__							=     input_features.convert_to_tensors(lowercase_				)

     return input_features










'''simple docstring'''

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      Adafactor,
      AdamW,
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_inverse_sqrt_schedule,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )





def 		UpperCamelCase							(				_lowerCamelCase   :   Any		,			_lowerCamelCase   :   Optional[Any]=10			):
    A__							=     []
    for _ in range(_lowerCamelCase			):
        lrs.append(scheduler.get_lr()[0]			)
        scheduler.step()
    return lrs





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]=10			):
    A__							=     []
    for step in range(_lowerCamelCase			):
        lrs.append(scheduler.get_lr()[0]			)
        scheduler.step()
        if step == num_steps // 2:
            with tempfile.TemporaryDirectory() as tmpdirname:
                A__							=     os.path.join(_lowerCamelCase		,			"schedule.bin"			)
                torch.save(scheduler.state_dict()		,			_lowerCamelCase			)

                A__							=     torch.load(_lowerCamelCase			)
                scheduler.load_state_dict(_lowerCamelCase			)
    return lrs






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Any		,			lowercase_ :List[Any]		,			lowercase_ :Optional[Any]				)->				int:
     self.assertEqual(len(lowercase_				)		,			len(lowercase_				)				)
     for a, b in zip(lowercase_		,			lowercase_				):
         self.assertAlmostEqual(lowercase_		,			lowercase_		,			delta=lowercase_				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Dict:
     A__							=     torch.tensor([0.1, -0.2, -0.1]		,			requires_grad=lowercase_				)
     A__							=     torch.tensor([0.4, 0.2, -0.5]				)
     A__							=     nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     A__							=     AdamW(params=[w]		,			lr=2E-1		,			weight_decay=0.0				)
     for _ in range(1_00				):
         A__							=     criterion(lowercase_		,			lowercase_				)
         loss.backward()
         optimizer.step()
         w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
         w.grad.zero_()
     self.assertListAlmostEqual(w.tolist()		,			[0.4, 0.2, -0.5]		,			tol=1E-2				)



 def 	UpperCAmelCase_				(							self :Tuple				)->				List[str]:
     A__							=     torch.tensor([0.1, -0.2, -0.1]		,			requires_grad=lowercase_				)
     A__							=     torch.tensor([0.4, 0.2, -0.5]				)
     A__							=     nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     A__							=     Adafactor(
         params=[w]		,			lr=1E-2		,			eps=(1E-30, 1E-3)		,			clip_threshold=1.0		,			decay_rate=-0.8		,			betaa=lowercase_		,			weight_decay=0.0		,			relative_step=lowercase_		,			scale_parameter=lowercase_		,			warmup_init=lowercase_		,			)
     for _ in range(10_00				):
         A__							=     criterion(lowercase_		,			lowercase_				)
         loss.backward()
         optimizer.step()
         w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
         w.grad.zero_()
     self.assertListAlmostEqual(w.tolist()		,			[0.4, 0.2, -0.5]		,			tol=1E-2				)






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 __lowercase  						=							nn.Linear(50     ,			50							) if is_torch_available() else None
 __lowercase  						=							AdamW(m.parameters()     ,			lr=1_0.0							) if is_torch_available() else None
 __lowercase  						=							10
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Any		,			lowercase_ :List[Any]		,			lowercase_ :List[Any]		,			lowercase_ :str=None				)->				Optional[int]:
     self.assertEqual(len(lowercase_				)		,			len(lowercase_				)				)
     for a, b in zip(lowercase_		,			lowercase_				):
         self.assertAlmostEqual(lowercase_		,			lowercase_		,			delta=lowercase_		,			msg=lowercase_				)



 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Any:
     A__							=     {"num_warmup_steps": 2, "num_training_steps": 10}
     # schedulers doct format
     # function: (sched_args_dict, expected_learning_rates)
     A__							=     {
         get_constant_schedule: ({}, [1_0.0] * self.num_steps),
         get_constant_schedule_with_warmup: (
             {"num_warmup_steps": 4},
             [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
         ),
         get_linear_schedule_with_warmup: (
             {**common_kwargs},
             [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
         ),
         get_cosine_schedule_with_warmup: (
             {**common_kwargs},
             [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
         ),
         get_cosine_with_hard_restarts_schedule_with_warmup: (
             {**common_kwargs, "num_cycles": 2},
             [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
         ),
         get_polynomial_decay_schedule_with_warmup: (
             {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
             [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
         ),
         get_inverse_sqrt_schedule: (
             {"num_warmup_steps": 2},
             [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
         ),
     }

     for scheduler_func, data in scheds.items():
         A__,      A__							=     data

         A__							=     scheduler_func(self.optimizer		,			**lowercase_				)
         self.assertEqual(len([scheduler.get_lr()[0]]				)		,			1				)
         A__							=     unwrap_schedule(lowercase_		,			self.num_steps				)
         self.assertListAlmostEqual(
             lowercase_		,			lowercase_		,			tol=1E-2		,			msg=F"failed for {scheduler_func} in normal scheduler"		,			)

         A__							=     scheduler_func(self.optimizer		,			**lowercase_				)
         if scheduler_func.__name__ != "get_constant_schedule":
             LambdaScheduleWrapper.wrap_scheduler(lowercase_				)  # wrap to test picklability of the schedule
         A__							=     unwrap_and_save_reload_schedule(lowercase_		,			self.num_steps				)
         self.assertListEqual(lowercase_		,			lowercase_		,			msg=F"failed for {scheduler_func} in save and reload"				)






class   UpperCAmelCase      :
 def __init__(							self :str		,			lowercase_ :List[str]				)->				Tuple:
     A__							=     fn
 def __call__(							self :List[Any]		,			*lowercase_ :Dict		,			**lowercase_ :Dict				)->				Tuple:
     return self.fn(*lowercase_		,			**lowercase_				)



 @classmethod
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Tuple				)->				List[Any]:
     A__							=     list(map(self		,			scheduler.lr_lambdas				)				)



