




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

_lowerCAmelCase  :      Tuple						=      random.Random()



def   lowerCAmelCase						(						_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Optional[Any]=1.0  ,							_lowerCAmelCase							:      Union[str, Any]=None  ,							_lowerCAmelCase							:      int=None		):




 """simple docstring"""







 if rng is None:
  UpperCAmelCase__							       =  global_rng

 UpperCAmelCase__							       =  []
 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      :Optional[int]       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :Tuple=7       ,					lowerCamelCase      :int=400       ,					lowerCamelCase      :List[str]=2000       ,					lowerCamelCase      :Tuple=2048       ,					lowerCamelCase      :Union[str, Any]=128       ,					lowerCamelCase      :List[Any]=1       ,					lowerCamelCase      :Tuple=512       ,					lowerCamelCase      :int=30       ,					lowerCamelCase      :Optional[Any]=4_4100       ,					)   ->	int:
       UpperCAmelCase__							       =  parent
       UpperCAmelCase__							       =  batch_size
       UpperCAmelCase__							       =  min_seq_length
       UpperCAmelCase__							       =  max_seq_length
       UpperCAmelCase__							       =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCAmelCase__							       =  spectrogram_length
       UpperCAmelCase__							       =  feature_size
       UpperCAmelCase__							       =  num_audio_channels
       UpperCAmelCase__							       =  hop_length
       UpperCAmelCase__							       =  chunk_length
       UpperCAmelCase__							       =  sampling_rate
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	int:
       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      :Any       ,					lowerCamelCase      :Tuple=False       ,					lowerCamelCase      :List[Any]=False					)   ->	List[Any]:






       def _flatten(lowerCamelCase      :Tuple					):
        return list(itertools.chain(*UpperCamelCase__					)					)

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



@require_torch
@require_torchaudio
class 	_UpperCamelCase					( _A		,				unittest.TestCase						):
      UpperCAmelCase_						=						TvltFeatureExtractor
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	List[str]:
       UpperCAmelCase__							       =  TvltFeatureExtractionTester(self					)
      def 		UpperCAmelCase_  (	self      :Optional[Any]					)   ->	Optional[Any]:
       UpperCAmelCase__							       =  self.feature_extraction_class(**self.feat_extract_dict					)
       self.assertTrue(hasattr(UpperCamelCase__       ,					"spectrogram_length"					)					)
       self.assertTrue(hasattr(UpperCamelCase__       ,					"feature_size"					)					)
       self.assertTrue(hasattr(UpperCamelCase__       ,					"num_audio_channels"					)					)
       self.assertTrue(hasattr(UpperCamelCase__       ,					"hop_length"					)					)
       self.assertTrue(hasattr(UpperCamelCase__       ,					"chunk_length"					)					)
       self.assertTrue(hasattr(UpperCamelCase__       ,					"sampling_rate"					)					)
      def 		UpperCAmelCase_  (	self      :Tuple					)   ->	int:
       UpperCAmelCase__							       =  self.feature_extraction_class(**self.feat_extract_dict					)

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

       UpperCAmelCase__							       =  feat_extract_first.to_dict()
       UpperCAmelCase__							       =  feat_extract_second.to_dict()
       UpperCAmelCase__							       =  dict_first.pop("mel_filters"					)
       UpperCAmelCase__							       =  dict_second.pop("mel_filters"					)
       self.assertTrue(np.allclose(UpperCamelCase__       ,					UpperCamelCase__					)					)
       self.assertEqual(UpperCamelCase__       ,					UpperCamelCase__					)
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	str:
       UpperCAmelCase__							       =  self.feature_extraction_class(**self.feat_extract_dict					)

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCAmelCase__							       =  os.path.join(UpperCamelCase__       ,					"feat_extract.json"					)
        feat_extract_first.to_json_file(UpperCamelCase__					)
        UpperCAmelCase__							       =  self.feature_extraction_class.from_json_file(UpperCamelCase__					)

       UpperCAmelCase__							       =  feat_extract_first.to_dict()
       UpperCAmelCase__							       =  feat_extract_second.to_dict()
       UpperCAmelCase__							       =  dict_first.pop("mel_filters"					)
       UpperCAmelCase__							       =  dict_second.pop("mel_filters"					)
       self.assertTrue(np.allclose(UpperCamelCase__       ,					UpperCamelCase__					)					)
       self.assertEqual(UpperCamelCase__       ,					UpperCamelCase__					)
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	Optional[int]:
       UpperCAmelCase__							       =  self.feature_extraction_class(**self.feat_extract_dict					)

       # create three inputs of length 800, 1000, and 1200
       UpperCAmelCase__							       =  [floats_list((1, x)					)[0] for x in range(800       ,					1400       ,					200					)]
       UpperCAmelCase__							       =  [np.asarray(UpperCamelCase__					) for speech_input in speech_inputs]

       # Test not batched input
       UpperCAmelCase__							       =  feature_extractor(np_speech_inputs[0]       ,					return_tensors="np"       ,					sampling_rate=4_4100					).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
       UpperCAmelCase__							       =  feature_extractor(UpperCamelCase__       ,					return_tensors="np"       ,					sampling_rate=4_4100					).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
       UpperCAmelCase__							       =  feature_extractor(
           UpperCamelCase__       ,					return_tensors="np"       ,					sampling_rate=4_4100       ,					mask_audio=UpperCamelCase__					).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.
       UpperCAmelCase__							       =  [floats_list((1, x)					)[0] for x in (800, 800, 800)]
       UpperCAmelCase__							       =  np.asarray(UpperCamelCase__					)
       UpperCAmelCase__							       =  feature_extractor(UpperCamelCase__       ,					return_tensors="np"       ,					sampling_rate=4_4100					).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      :Tuple       ,					lowerCamelCase      :Tuple					)   ->	List[Any]:
       UpperCAmelCase__							       =  load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,					"clean"       ,					split="validation"					)
       # automatic decoding with librispeech
       UpperCAmelCase__							       =  ds.sort("id"					).select(range(UpperCamelCase__					)					)[:num_samples]["audio"]

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







      def 		UpperCAmelCase_  (	self      :Union[str, Any]					)   ->	Optional[int]:
       UpperCAmelCase__							       =  self._load_datasamples(1					)
       UpperCAmelCase__							       =  TvltFeatureExtractor()
       UpperCAmelCase__							       =  feature_extractor(UpperCamelCase__       ,					return_tensors="pt"					).audio_values

       self.assertEquals(audio_values.shape       ,					(1, 1, 192, 128)					)

       UpperCAmelCase__							       =  torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]					)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,					UpperCamelCase__       ,					atol=1e-4					)					)







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu




@require_cpu
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       torch.nn.Linear(10			,							10    )
    __magic_name__				     =       torch.optim.SGD(model.parameters()			,							0.1    )
    __magic_name__				     =       Accelerator()
    __magic_name__				     =       accelerator.prepare(UpperCamelCase__    )
    try:
      pickle.loads(pickle.dumps(UpperCamelCase__    )    )
    except Exception as e:
      self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
    AcceleratorState._reset_state()

