"""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

UpperCAmelCase__			=    random.Random()






def      __UpperCAmelCase       (      lowercase ,lowercase=1.0 ,lowercase=None ,lowercase=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 							a					(		unittest.TestCase ):


    def __init__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any]=7 ,					__lowerCAmelCase						:	List[Any]=400 ,					__lowerCAmelCase						:	List[Any]=2000 ,					__lowerCAmelCase						:	Tuple=2048 ,					__lowerCAmelCase						:	Any=128 ,					__lowerCAmelCase						:	List[str]=1 ,					__lowerCAmelCase						:	str=512 ,					__lowerCAmelCase						:	Tuple=30 ,					__lowerCAmelCase						:	Optional[int]=4_4100 ,					):
      _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   lowerCAmelCase_	(       self						:	List[Any]       ):
      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   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Any=False ,					__lowerCAmelCase						:	int=False       ):
      def _flatten(__lowerCAmelCase						:	Tuple       ):
        return list(itertools.chain(*__lowerCAmelCase       )       )

      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(__lowerCAmelCase       ) for x in speech_inputs]
      return speech_inputs



@require_torch
@require_torchaudio
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	int  				=				TvltFeatureExtractor


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   TvltFeatureExtractionTester(self       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.feature_extraction_class(**self.feat_extract_dict       )

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

      _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(__lowerCAmelCase ,					__lowerCAmelCase       )       )
      self.assertEqual(__lowerCAmelCase ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.feature_extraction_class(**self.feat_extract_dict       )

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

      _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(__lowerCAmelCase ,					__lowerCAmelCase       )       )
      self.assertEqual(__lowerCAmelCase ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	int       ):
      # Initialize feature_extractor
      _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(__lowerCAmelCase       ) 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(__lowerCAmelCase ,					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(
          __lowerCAmelCase ,					return_tensors="""np""" ,					sampling_rate=4_4100 ,					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.
      _UpperCAmelCase    			=   [floats_list((1, x)       )[0] for x in (800, 800, 800)]
      _UpperCAmelCase    			=   np.asarray(__lowerCAmelCase       )
      _UpperCAmelCase    			=   feature_extractor(__lowerCAmelCase ,					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   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,					"""clean""" ,					split="""validation"""       )
      # automatic decoding with librispeech
      _UpperCAmelCase    			=   ds.sort("""id"""       ).select(range(__lowerCAmelCase       )       )[:num_samples]["""audio"""]

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


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   self._load_datasamples(1       )
      _UpperCAmelCase    			=   TvltFeatureExtractor()
      _UpperCAmelCase    			=   feature_extractor(__lowerCAmelCase ,					return_tensors="""pt"""       ).audio_values

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

      _UpperCAmelCase    			=   torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]       )
      self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,					__lowerCAmelCase ,					atol=1e-4       )       )

"""simple docstring"""


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Dict  				=				CustomTokenizer
    pass
