



"""simple docstring"""






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				List[str]:
  for param, grad_param in zip(model_a.parameters()  ,						model_b.parameters()	):
    if not param.requires_grad:
      continue
    if not did_step:
      # Grads should not be in sync
      assert (
          torch.allclose(param.grad  ,						grad_param.grad	) is False
      ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
    else:
      # Grads should be in sync
      assert (
          torch.allclose(param.grad  ,						grad_param.grad	) is True
      ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=True	)						->				Optional[int]:
  model.train()
  _snake_case   	=      model(lowerCAmelCase_	)
  _snake_case   	=      F.mse_loss(lowerCAmelCase_  ,						target.to(output.device	)	)
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=False	)						->				Dict:
  set_seed(42	)
  _snake_case   	=      RegressionModel()
  _snake_case   	=      deepcopy(lowerCAmelCase_	)
  _snake_case   	=      RegressionDataset(length=80	)
  _snake_case   	=      DataLoader(lowerCAmelCase_  ,						batch_size=16	)
  model.to(accelerator.device	)
  if sched:
    _snake_case   	=      AdamW(params=model.parameters()  ,						lr=1E-3	)
    _snake_case   	=      AdamW(params=ddp_model.parameters()  ,						lr=1E-3	)
    _snake_case   	=      LambdaLR(lowerCAmelCase_  ,						lr_lambda=lambda lowerCAmelCase_	: epoch**0.65	)
    _snake_case   	=      LambdaLR(lowerCAmelCase_  ,						lr_lambda=lambda lowerCAmelCase_	: epoch**0.65	)
  # Make a copy of `model`
  if sched:
    _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      accelerator.prepare(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  else:
    _snake_case	,     _snake_case   	=      accelerator.prepare(lowerCAmelCase_  ,						lowerCAmelCase_	)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  # Test when on a single CPU or GPU that the context manager does nothing
  _snake_case	,     _snake_case	,     _snake_case   	=      get_training_setup(lowerCAmelCase_	)
  # Use a single batch
  _snake_case	,     _snake_case   	=      next(iter(lowerCAmelCase_	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    _snake_case	,     _snake_case   	=      accelerator.gather((ddp_input, ddp_target)	)
    _snake_case	,     _snake_case   	=      input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(lowerCAmelCase_	):
        step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    else:
      # Sync grads
      step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

    # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
    check_model_parameters(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    for param, ddp_param in zip(model.parameters()  ,						ddp_model.parameters()	):
      if not param.requires_grad:
        continue
      assert torch.allclose(
          param.grad  ,						ddp_param.grad	), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

    # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
    _snake_case   	=      ddp_input[torch.randperm(len(lowerCAmelCase_	)	)]




def 			snake_case		(		lowerCAmelCase_	)						->				Any:
  # Test on distributed setup that context manager behaves properly
  _snake_case	,     _snake_case	,     _snake_case   	=      get_training_setup(lowerCAmelCase_	)
  # Use a single batch
  _snake_case	,     _snake_case   	=      next(iter(lowerCAmelCase_	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    _snake_case	,     _snake_case   	=      accelerator.gather((ddp_input, ddp_target)	)
    _snake_case	,     _snake_case   	=      input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(lowerCAmelCase_	):
        step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    else:
      # Sync grads
      step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

    # DDP model and model should only be in sync when not (iteration % 2 == 0)
    for param, ddp_param in zip(model.parameters()  ,						ddp_model.parameters()	):
      if not param.requires_grad:
        continue
      if iteration % 2 == 0:
        # Grads should not be in sync
        assert (
            torch.allclose(param.grad  ,						ddp_param.grad	) is False
        ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
      else:
        # Grads should be in sync
        assert (
            torch.allclose(param.grad  ,						ddp_param.grad	) is True
        ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
    _snake_case   	=      ddp_input[torch.randperm(len(lowerCAmelCase_	)	)]




def 			snake_case		(		lowerCAmelCase_=False  ,						lowerCAmelCase_=False	)						->				Union[str, Any]:
  _snake_case   	=      Accelerator(
      split_batches=lowerCAmelCase_  ,						dispatch_batches=lowerCAmelCase_  ,						gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  _snake_case	,     _snake_case	,     _snake_case   	=      get_training_setup(lowerCAmelCase_	)
  for iteration, batch in enumerate(lowerCAmelCase_	):
    _snake_case	,     _snake_case   	=      batch.values()
    # Gather the distributed inputs and targs for the base model
    _snake_case	,     _snake_case   	=      accelerator.gather((ddp_input, ddp_target)	)
    _snake_case	,     _snake_case   	=      input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    # Do "gradient accumulation" (noop)
    with accelerator.accumulate(lowerCAmelCase_	):
      step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

    # DDP model and model should only be in sync when not (iteration % 2 == 0)
    for param, ddp_param in zip(model.parameters()  ,						ddp_model.parameters()	):
      if not param.requires_grad:
        continue
      if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCAmelCase_	) - 1):
        # Grads should be in sync
        assert (
            torch.allclose(param.grad  ,						ddp_param.grad	) is True
        ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
      else:
        # Grads should not be in sync
        assert (
            torch.allclose(param.grad  ,						ddp_param.grad	) is False
        ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
    _snake_case   	=      ddp_input[torch.randperm(len(lowerCAmelCase_	)	)]
  GradientState._reset_state()




def 			snake_case		(		lowerCAmelCase_=False  ,						lowerCAmelCase_=False	)						->				List[str]:
  _snake_case   	=      Accelerator(
      split_batches=lowerCAmelCase_  ,						dispatch_batches=lowerCAmelCase_  ,						gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  _snake_case	,     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      get_training_setup(lowerCAmelCase_  ,						lowerCAmelCase_	)
  for iteration, batch in enumerate(lowerCAmelCase_	):
    _snake_case	,     _snake_case   	=      batch.values()
    # Gather the distributed inputs and targs for the base model
    _snake_case	,     _snake_case   	=      accelerator.gather((ddp_input, ddp_target)	)
    _snake_case	,     _snake_case   	=      input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    model.train()
    ddp_model.train()
    step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    opt.step()

    if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase_	)):
      if split_batches:
        sched.step()
      else:
        for _ in range(accelerator.num_processes	):
          sched.step()
    opt.zero_grad()
    # Perform gradient accumulation under wrapper
    with accelerator.accumulate(lowerCAmelCase_	):
      step_model(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
      ddp_opt.step()
      ddp_sched.step()
      ddp_opt.zero_grad()

    # Learning rates should be the same
    assert (
        opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
    ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
    _snake_case   	=      (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCAmelCase_	))
    if accelerator.num_processes > 1:
      check_model_parameters(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
  GradientState._reset_state()




def 			snake_case		(		)						->				int:
  _snake_case   	=      Accelerator()

  _snake_case   	=      RegressionDataset(length=80	)
  _snake_case   	=      DataLoader(lowerCAmelCase_  ,						batch_size=16	)
  _snake_case   	=      RegressionDataset(length=96	)
  _snake_case   	=      DataLoader(lowerCAmelCase_  ,						batch_size=16	)
  _snake_case	,     _snake_case   	=      accelerator.prepare(lowerCAmelCase_  ,						lowerCAmelCase_	)
  assert accelerator.gradient_state.active_dataloader is None
  for iteration, _ in enumerate(lowerCAmelCase_	):
    assert id(accelerator.gradient_state.active_dataloader	) == id(lowerCAmelCase_	)
    if iteration < len(lowerCAmelCase_	) - 1:
      assert not accelerator.gradient_state.end_of_dataloader
      if iteration == 1:
        for batch_num, _ in enumerate(lowerCAmelCase_	):
          assert id(accelerator.gradient_state.active_dataloader	) == id(lowerCAmelCase_	)
          if batch_num < len(lowerCAmelCase_	) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
          else:
            assert accelerator.gradient_state.end_of_dataloader
    else:
      assert accelerator.gradient_state.end_of_dataloader
  assert accelerator.gradient_state.active_dataloader is None




def 			snake_case		(		)						->				Tuple:
  _snake_case   	=      Accelerator()
  _snake_case   	=      accelerator.state
  if state.local_process_index == 0:
    print('''**Test `accumulate` gradient accumulation with dataloader break**'''	)
  test_dataloader_break()
  if state.distributed_type == DistributedType.NO:
    if state.local_process_index == 0:
      print('''**Test NOOP `no_sync` context manager**'''	)
    test_noop_sync(lowerCAmelCase_	)
  if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
    if state.local_process_index == 0:
      print('''**Test Distributed `no_sync` context manager**'''	)
    test_distributed_sync(lowerCAmelCase_	)
  if state.distributed_type == DistributedType.MULTI_GPU:
    for split_batch in [True, False]:
      for dispatch_batches in [True, False]:
        if state.local_process_index == 0:
          print(
              '''**Test `accumulate` gradient accumulation, '''  ,						f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""  ,						)
        test_gradient_accumulation(lowerCAmelCase_  ,						lowerCAmelCase_	)

    # Currently will break on torch 2.0 +, need to investigate why
  if is_torch_version('''<'''  ,						'''2.0'''	) or state.distributed_type == DistributedType.NO:
    if state.local_process_index == 0:
      print(
          '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''  ,						'''`split_batches=False`, `dispatch_batches=False`**'''  ,						)
    test_gradient_accumulation_with_opt_and_scheduler()
    if state.distributed_type == DistributedType.MULTI_GPU:
      for split_batch in [True, False]:
        for dispatch_batches in [True, False]:
          if not split_batch and not dispatch_batches:
            continue
          if state.local_process_index == 0:
            print(
                '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''  ,						f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""  ,						)
          test_gradient_accumulation_with_opt_and_scheduler(lowerCAmelCase_  ,						lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
     main()










"""simple docstring"""






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset

snake_case   					=				random.Random()




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1.0  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				List[str]:
  if rng is None:
    _snake_case   	=      global_rng

  _snake_case   	=      []
  for batch_idx in range(shape[0]	):
    values.append([]	)
    for _ in range(shape[1]	):
      values[-1].append(rng.random() * scale	)

  return values



class     UpperCAmelCase    (	unittest.TestCase  ):



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[Any]=7							,       __lowerCamelCase    :	List[Any]=4_0_0							,       __lowerCamelCase    :	Any=2_0_0_0							,       __lowerCamelCase    :	Any=2_0_4_8							,       __lowerCamelCase    :	Any=1_2_8							,       __lowerCamelCase    :	Any=1							,       __lowerCamelCase    :	Optional[int]=5_1_2							,       __lowerCamelCase    :	Tuple=3_0							,       __lowerCamelCase    :	List[Any]=4_4_1_0_0							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      min_seq_length
     _snake_case   	=      max_seq_length
     _snake_case   	=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     _snake_case   	=      spectrogram_length
     _snake_case   	=      feature_size
     _snake_case   	=      num_audio_channels
     _snake_case   	=      hop_length
     _snake_case   	=      chunk_length
     _snake_case   	=      sampling_rate



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     return {
         "spectrogram_length": self.spectrogram_length,
         "feature_size": self.feature_size,
         "num_audio_channels": self.num_audio_channels,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "sampling_rate": self.sampling_rate,
     }





   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Any=False							,       __lowerCamelCase    :	int=False				):



     """simple docstring"""
     def _flatten(__lowerCamelCase    :	List[str]				):
       return list(itertools.chain(*__lowerCamelCase				)				)

     if equal_length:
       _snake_case   	=      [floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
     else:
       # make sure that inputs increase in size
       _snake_case   	=      [
           floats_list((x, self.feature_size)				)
           for x in range(self.min_seq_length							,       self.max_seq_length							,       self.seq_length_diff				)
       ]
     if numpify:
       _snake_case   	=      [np.asarray(__lowerCamelCase				) for x in speech_inputs]
     return speech_inputs



@require_torch
@require_torchaudio
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Tuple           =  TvltFeatureExtractor



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      TvltFeatureExtractionTester(self				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''spectrogram_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''feature_size'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''num_audio_channels'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''hop_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''chunk_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''sampling_rate'''				)				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      feat_extract_first.save_pretrained(__lowerCamelCase				)[0]
       check_json_file_has_correct_format(__lowerCamelCase				)
       _snake_case   	=      self.feature_extraction_class.from_pretrained(__lowerCamelCase				)

     _snake_case   	=      feat_extract_first.to_dict()
     _snake_case   	=      feat_extract_second.to_dict()
     _snake_case   	=      dict_first.pop('''mel_filters'''				)
     _snake_case   	=      dict_second.pop('''mel_filters'''				)
     self.assertTrue(np.allclose(__lowerCamelCase							,       __lowerCamelCase				)				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      os.path.join(__lowerCamelCase							,       '''feat_extract.json'''				)
       feat_extract_first.to_json_file(__lowerCamelCase				)
       _snake_case   	=      self.feature_extraction_class.from_json_file(__lowerCamelCase				)

     _snake_case   	=      feat_extract_first.to_dict()
     _snake_case   	=      feat_extract_second.to_dict()
     _snake_case   	=      dict_first.pop('''mel_filters'''				)
     _snake_case   	=      dict_second.pop('''mel_filters'''				)
     self.assertTrue(np.allclose(__lowerCamelCase							,       __lowerCamelCase				)				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     # Initialize feature_extractor
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     # create three inputs of length 800, 1000, and 1200
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      [np.asarray(__lowerCamelCase				) for speech_input in speech_inputs]

     # Test not batched input
     _snake_case   	=      feature_extractor(np_speech_inputs[0]							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).audio_values

     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

     # Test batched
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).audio_values

     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

     # Test audio masking
     _snake_case   	=      feature_extractor(
         __lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0							,       mask_audio=__lowerCamelCase				).audio_values

     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

     # Test 2-D numpy arrays are batched.
     _snake_case   	=      [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     _snake_case   	=      np.asarray(__lowerCamelCase				)
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).audio_values
     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''							,       '''clean'''							,       split='''validation'''				)
     # automatic decoding with librispeech
     _snake_case   	=      ds.sort('''id'''				).select(range(__lowerCamelCase				)				)[:num_samples]['''audio''']

     return [x["array"] for x in speech_samples]





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self._load_datasamples(1				)
     _snake_case   	=      TvltFeatureExtractor()
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''pt'''				).audio_values

     self.assertEquals(audio_values.shape							,       (1, 1, 1_9_2, 1_2_8)				)

     _snake_case   	=      torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]				)
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,       __lowerCamelCase							,       atol=1E-4				)				)





