



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 					lowercase				(      SCREAMING_SNAKE_CASE__				:		List[Any] ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any]=10 )     ->   str:
 _snake_case			:     Any 		=    []
 for _ in range(UpperCamelCase__ ):
  lrs.append(scheduler.get_lr()[0] )
  scheduler.step()
 return lrs



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Optional[int] ,				SCREAMING_SNAKE_CASE__				:		Any=10 )     ->   Any:
 _snake_case			:     int 		=    []
 for step in range(UpperCamelCase__ ):
  lrs.append(scheduler.get_lr()[0] )
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    _snake_case			:     Optional[Any] 		=    os.path.join(UpperCamelCase__ ,				"""schedule.bin""" )
    torch.save(scheduler.state_dict() ,				UpperCamelCase__ )

    _snake_case			:     str 		=    torch.load(UpperCamelCase__ )
    scheduler.load_state_dict(UpperCamelCase__ )
 return lrs







@require_torch
class 						snake_case  (		unittest.TestCase     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	str) -> Any:

    """simple docstring"""







    self.assertEqual(len(lowerCamelCase__) ,						len(lowerCamelCase__))
    for a, b in zip(lowerCamelCase__ ,						lowerCamelCase__):
     self.assertAlmostEqual(lowerCamelCase__ ,						lowerCamelCase__ ,						delta=lowerCamelCase__)

   def    UpperCamelCase_     (    self							:	List[Any]) -> Any:

    """simple docstring"""







    _snake_case			:     str 		=    torch.tensor([0.1, -0.2, -0.1] ,						requires_grad=lowerCamelCase__)
    _snake_case			:     List[Any] 		=    torch.tensor([0.4, 0.2, -0.5])
    _snake_case			:     List[Any] 		=    nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    _snake_case			:     List[Any] 		=    AdamW(params=[w] ,						lr=2E-1 ,						weight_decay=0.0)
    for _ in range(100):
     _snake_case			:     Optional[int] 		=    criterion(lowerCamelCase__ ,						lowerCamelCase__)
     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							:	Any) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     Union[str, Any] 		=    torch.tensor([0.1, -0.2, -0.1] ,						requires_grad=lowerCamelCase__)
    _snake_case			:     Any 		=    torch.tensor([0.4, 0.2, -0.5])
    _snake_case			:     int 		=    nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    _snake_case			:     str 		=    Adafactor(
        params=[w] ,						lr=1E-2 ,						eps=(1E-30, 1E-3) ,						clip_threshold=1.0 ,						decay_rate=-0.8 ,						betaa=lowerCamelCase__ ,						weight_decay=0.0 ,						relative_step=lowerCamelCase__ ,						scale_parameter=lowerCamelCase__ ,						warmup_init=lowerCamelCase__ ,						)
    for _ in range(1000):
     _snake_case			:     str 		=    criterion(lowerCamelCase__ ,						lowerCamelCase__)
     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 						snake_case  (		unittest.TestCase     ):



   '''simple docstring'''






   snake_case_    :       Optional[int]								=						nn.Linear(50    ,50     ) if is_torch_available() else None
   snake_case_    :       Optional[Any]								=						AdamW(m.parameters()    ,lr=10.0     ) if is_torch_available() else None
   snake_case_    :       int								=						10

   def    UpperCamelCase_     (    self							:	List[Any] ,						lowerCAmelCase							:	Tuple ,						lowerCAmelCase							:	Tuple ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Optional[Any]=None) -> Any:

    """simple docstring"""







    self.assertEqual(len(lowerCamelCase__) ,						len(lowerCamelCase__))
    for a, b in zip(lowerCamelCase__ ,						lowerCamelCase__):
     self.assertAlmostEqual(lowerCamelCase__ ,						lowerCamelCase__ ,						delta=lowerCamelCase__ ,						msg=lowerCamelCase__)


   def    UpperCamelCase_     (    self							:	Union[str, Any]) -> Optional[Any]:

    """simple docstring"""







    _snake_case			:     Optional[Any] 		=    {"""num_warmup_steps""": 2, """num_training_steps""": 10}
    # schedulers doct format
    # function: (sched_args_dict, expected_learning_rates)
    _snake_case			:     List[str] 		=    {
        get_constant_schedule: ({}, [10.0] * self.num_steps),
        get_constant_schedule_with_warmup: (
            {"""num_warmup_steps""": 4},
            [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
        ),
        get_linear_schedule_with_warmup: (
            {**common_kwargs},
            [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
        ),
        get_cosine_schedule_with_warmup: (
            {**common_kwargs},
            [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
        ),
        get_cosine_with_hard_restarts_schedule_with_warmup: (
            {**common_kwargs, """num_cycles""": 2},
            [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
        ),
        get_polynomial_decay_schedule_with_warmup: (
            {**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
            [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
        ),
        get_inverse_sqrt_schedule: (
            {"""num_warmup_steps""": 2},
            [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
        ),
    }

    for scheduler_func, data in scheds.items():
     _snake_case		,			_snake_case			:     Union[str, Any] 		=    data

     _snake_case			:     List[str] 		=    scheduler_func(self.optimizer ,						**lowerCamelCase__)
     self.assertEqual(len([scheduler.get_lr()[0]]) ,						1)
     _snake_case			:     List[Any] 		=    unwrap_schedule(lowerCamelCase__ ,						self.num_steps)
     self.assertListAlmostEqual(
         lowerCamelCase__ ,						lowerCamelCase__ ,						tol=1E-2 ,						msg=F'''failed for {scheduler_func} in normal scheduler''' ,						)

     _snake_case			:     Tuple 		=    scheduler_func(self.optimizer ,						**lowerCamelCase__)
     if scheduler_func.__name__ != "get_constant_schedule":
      LambdaScheduleWrapper.wrap_scheduler(lowerCamelCase__)  # wrap to test picklability of the schedule
     _snake_case			:     Optional[Any] 		=    unwrap_and_save_reload_schedule(lowerCamelCase__ ,						self.num_steps)
     self.assertListEqual(lowerCamelCase__ ,						lowerCamelCase__ ,						msg=F'''failed for {scheduler_func} in save and reload''')







class 						snake_case  :



   '''simple docstring'''






   def __init__(    self							:	str ,						lowerCAmelCase							:	Tuple) -> Tuple:

    """simple docstring"""







    _snake_case			:     List[Any] 		=    fn

   def __call__(    self							:	Any ,						*lowerCAmelCase							:	int ,						**lowerCAmelCase							:	List[str]) -> Any:

    """simple docstring"""







    return self.fn(*lowerCamelCase__ ,						**lowerCamelCase__)


   @classmethod
   def    UpperCamelCase_     (    self							:	int ,						lowerCAmelCase							:	List[Any]) -> Any:

    """simple docstring"""







    _snake_case			:     Optional[int] 		=    list(map(self ,						scheduler.lr_lambdas))






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


__A        =    logging.get_logger(__name__)







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''input_features''', '''is_longer''']


			def __init__(      self			,						lowerCamelCase__=64			,						lowerCamelCase__=48_000			,						lowerCamelCase__=480			,						lowerCamelCase__=10			,						lowerCamelCase__=1_024			,						lowerCamelCase__=0.0			,						lowerCamelCase__=False			,						lowerCamelCase__ = 0			,						lowerCamelCase__ = 14_000			,						lowerCamelCase__ = None			,						lowerCamelCase__ = "fusion"			,						lowerCamelCase__ = "repeatpad"			,						**lowerCamelCase__			,						)    ->      Tuple:



							'''simple docstring'''
							super().__init__(
							    feature_size=lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						padding_value=lowerCamelCase__			,						return_attention_mask=lowerCamelCase__			,						**lowerCamelCase__			,						)
							__lowerCamelCase		       =    top_db
							__lowerCamelCase		       =    truncation
							__lowerCamelCase		       =    padding
							__lowerCamelCase		       =    fft_window_size
							__lowerCamelCase		       =    (fft_window_size >> 1) + 1
							__lowerCamelCase		       =    hop_length
							__lowerCamelCase		       =    max_length_s
							__lowerCamelCase		       =    max_length_s * sampling_rate
							__lowerCamelCase		       =    sampling_rate
							__lowerCamelCase		       =    frequency_min
							__lowerCamelCase		       =    frequency_max
							__lowerCamelCase		       =    mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins			,						num_mel_filters=lowerCamelCase__			,						min_frequency=lowerCamelCase__			,						max_frequency=lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						norm=lowerCamelCase__			,						mel_scale='htk'			,						)
							__lowerCamelCase		       =    mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins			,						num_mel_filters=lowerCamelCase__			,						min_frequency=lowerCamelCase__			,						max_frequency=lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						norm='slaney'			,						mel_scale='slaney'			,						)


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



							'''simple docstring'''
							__lowerCamelCase		       =    copy.deepcopy(self.__dict__							)
							__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      np.ndarray:



							'''simple docstring'''
							__lowerCamelCase		       =    spectrogram(
							    lowerCamelCase__			,						window_function(self.fft_window_size			,						'hann'							)			,						frame_length=self.fft_window_size			,						hop_length=self.hop_length			,						power=2.0			,						mel_filters=lowerCamelCase__			,						log_mel='dB'			,						)
							return log_mel_spectrogram.T


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    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
											__lowerCamelCase		       =    [0]
							if len(ranges[2]							) == 0:
											# if the audio is too short, we just use the first chunk
											__lowerCamelCase		       =    [0]
							# randomly choose index for each part
							__lowerCamelCase		       =    np.random.choice(ranges[0]							)
							__lowerCamelCase		       =    np.random.choice(ranges[1]							)
							__lowerCamelCase		       =    np.random.choice(ranges[2]							)

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

							__lowerCamelCase		       =    torch.tensor(mel[None, None, :]							)
							__lowerCamelCase		       =    torch.nn.functional.interpolate(
							    lowerCamelCase__			,						size=[chunk_frames, 64]			,						mode='bilinear'			,						align_corners=lowerCamelCase__							)
							__lowerCamelCase		       =    mel_shrink[0][0].numpy()
							__lowerCamelCase		       =    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,						axis=0							)
							return mel_fusion


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      np.array:



							'''simple docstring'''
							if waveform.shape[0] > max_length:
											if truncation == "rand_trunc":
															__lowerCamelCase		       =    True
															# random crop to max_length (for compatibility) -> this should be handled by self.pad
															__lowerCamelCase		       =    len(lowerCamelCase__							) - max_length
															__lowerCamelCase		       =    np.random.randint(0			,						overflow + 1							)
															__lowerCamelCase		       =    waveform[idx : idx + max_length]
															__lowerCamelCase		       =    self._np_extract_fbank_features(lowerCamelCase__			,						self.mel_filters_slaney							)[None, :]
											elif truncation == "fusion":
															__lowerCamelCase		       =    self._np_extract_fbank_features(lowerCamelCase__			,						self.mel_filters							)
															__lowerCamelCase		       =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
															__lowerCamelCase		       =    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.
																			__lowerCamelCase		       =    np.stack([mel, mel, mel, mel]			,						axis=0							)
																			__lowerCamelCase		       =    False
															else:
																			__lowerCamelCase		       =    self._random_mel_fusion(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)
																			__lowerCamelCase		       =    True
											else:
															raise NotImplementedError(f"""data_truncating {truncation} not implemented"""							)

							else:
											__lowerCamelCase		       =    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":
																			__lowerCamelCase		       =    int(max_length / len(lowerCamelCase__							)							)
																			__lowerCamelCase		       =    np.stack(np.tile(lowerCamelCase__			,						n_repeat + 1							)							)[:max_length]
															if padding == "repeatpad":
																			__lowerCamelCase		       =    int(max_length / len(lowerCamelCase__							)							)
																			__lowerCamelCase		       =    np.stack(np.tile(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    np.pad(lowerCamelCase__			,						(0, max_length - waveform.shape[0])			,						mode='constant'			,						constant_values=0							)

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

							return input_mel, longer






			def __call__(      self			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						**lowerCamelCase__			,						)    ->      BatchFeature:



							'''simple docstring'''
							__lowerCamelCase		       =    truncation if truncation is not None else self.truncation
							__lowerCamelCase		       =    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.'							)

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

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

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

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

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

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

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

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

							__lowerCamelCase		       =    {'input_features': input_mel, 'is_longer': is_longer}
							__lowerCamelCase		       =    BatchFeature(lowerCamelCase__							)

							if return_tensors is not None:
											__lowerCamelCase		       =    input_features.convert_to_tensors(lowerCamelCase__							)

							return input_features


