



'''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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=1.0       ,_lowerCAmelCase=None       ,_lowerCAmelCase=None       )      ->      Tuple:
  if rng is None:
    __lowerCamelCase       :       Dict           = global_rng

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

  return values






class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




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




  def 						_lowercase     ( self      :	Any   )					->					Optional[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 						_lowercase     ( self      :	List[str]			,	_a      :	str=False			,	_a      :	Union[str, Any]=False   )					->					Dict:
    def _flatten(_a      :	Optional[int]   ):
      return list(itertools.chain(*_a   )   )

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






@require_torch
@require_torchaudio
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=TvltFeatureExtractor




  def 						_lowercase     ( self      :	List[str]   )					->					Dict:
    __lowerCamelCase       :       Optional[int]           = TvltFeatureExtractionTester(self   )




  def 						_lowercase     ( self      :	Dict   )					->					int:
    __lowerCamelCase       :       Union[str, Any]           = self.feature_extraction_class(**self.feat_extract_dict   )
    self.assertTrue(hasattr(_a			,	'spectrogram_length'   )   )
    self.assertTrue(hasattr(_a			,	'feature_size'   )   )
    self.assertTrue(hasattr(_a			,	'num_audio_channels'   )   )
    self.assertTrue(hasattr(_a			,	'hop_length'   )   )
    self.assertTrue(hasattr(_a			,	'chunk_length'   )   )
    self.assertTrue(hasattr(_a			,	'sampling_rate'   )   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       str           = self.feature_extraction_class(**self.feat_extract_dict   )

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

    __lowerCamelCase       :       Optional[Any]           = feat_extract_first.to_dict()
    __lowerCamelCase       :       Optional[Any]           = feat_extract_second.to_dict()
    __lowerCamelCase       :       List[Any]           = dict_first.pop('mel_filters'   )
    __lowerCamelCase       :       Any           = dict_second.pop('mel_filters'   )
    self.assertTrue(np.allclose(_a			,	_a   )   )
    self.assertEqual(_a			,	_a   )




  def 						_lowercase     ( self      :	int   )					->					List[str]:
    __lowerCamelCase       :       Optional[int]           = self.feature_extraction_class(**self.feat_extract_dict   )

    with tempfile.TemporaryDirectory() as tmpdirname:
      __lowerCamelCase       :       Union[str, Any]           = os.path.join(_a			,	'feat_extract.json'   )
      feat_extract_first.to_json_file(_a   )
      __lowerCamelCase       :       Tuple           = self.feature_extraction_class.from_json_file(_a   )

    __lowerCamelCase       :       Dict           = feat_extract_first.to_dict()
    __lowerCamelCase       :       Union[str, Any]           = feat_extract_second.to_dict()
    __lowerCamelCase       :       Union[str, Any]           = dict_first.pop('mel_filters'   )
    __lowerCamelCase       :       Optional[int]           = dict_second.pop('mel_filters'   )
    self.assertTrue(np.allclose(_a			,	_a   )   )
    self.assertEqual(_a			,	_a   )




  def 						_lowercase     ( self      :	List[str]   )					->					Optional[Any]:
    # Initialize feature_extractor
    __lowerCamelCase       :       Any           = self.feature_extraction_class(**self.feat_extract_dict   )

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

    # Test not batched input
    __lowerCamelCase       :       int           = 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
    __lowerCamelCase       :       str           = feature_extractor(_a			,	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
    __lowerCamelCase       :       List[str]           = feature_extractor(
        _a			,	return_tensors='np'			,	sampling_rate=4_4100			,	mask_audio=_a   ).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.
    __lowerCamelCase       :       Optional[Any]           = [floats_list((1, x)   )[0] for x in (800, 800, 800)]
    __lowerCamelCase       :       Optional[int]           = np.asarray(_a   )
    __lowerCamelCase       :       Optional[Any]           = feature_extractor(_a			,	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 						_lowercase     ( self      :	List[Any]			,	_a      :	Dict   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = load_dataset('hf-internal-testing/librispeech_asr_dummy'			,	'clean'			,	split='validation'   )
    # automatic decoding with librispeech
    __lowerCamelCase       :       Dict           = ds.sort('id'   ).select(range(_a   )   )[:num_samples]['audio']

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




  def 						_lowercase     ( self      :	Optional[int]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = self._load_datasamples(1   )
    __lowerCamelCase       :       List[str]           = TvltFeatureExtractor()
    __lowerCamelCase       :       Tuple           = feature_extractor(_a			,	return_tensors='pt'   ).audio_values

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

    __lowerCamelCase       :       List[Any]           = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]   )
    self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,	_a			,	atol=1e-4   )   )











'''simple docstring'''





_UpperCamelCase											=						tuple[float, float, float]
_UpperCamelCase											=						tuple[float, float, float]

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Vectorad:
  __lowerCamelCase       :       Any           = end_pointa[0] - end_pointa[0]
  __lowerCamelCase       :       str           = end_pointa[1] - end_pointa[1]
  __lowerCamelCase       :       Tuple           = end_pointa[2] - end_pointa[2]
  return (x, y, z)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Vectorad:
  __lowerCamelCase       :       List[str]           = ab[1] * ac[2] - ab[2] * ac[1]  # *i
  __lowerCamelCase       :       Dict           = (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
  __lowerCamelCase       :       List[Any]           = ab[0] * ac[1] - ab[1] * ac[0]  # *k
  return (x, y, z)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      bool:
  return tuple(round(_lowerCAmelCase       ,_lowerCAmelCase       ) for x in vector       ) == (0, 0, 0)


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 10       )      ->      bool:
  __lowerCamelCase       :       str           = create_vector(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = create_vector(_lowerCAmelCase       ,_lowerCAmelCase       )
  return is_zero_vector(get_ad_vectors_cross(_lowerCAmelCase       ,_lowerCAmelCase       )       ,_lowerCAmelCase       )






