


'''simple docstring'''
import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


UpperCAmelCase_ :     int								=      random.Random()


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					str=1.0,				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					Dict=None			):
 if rng is None:
  __A  :		Dict       =			global_rng

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

 return values

@require_torch
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=7      ,      __lowercase=400      ,      __lowercase=2_000      ,      __lowercase=1      ,      __lowercase=0.0      ,      __lowercase=16_000      ,      __lowercase=True      ,      __lowercase=80      ,      __lowercase=16      ,      __lowercase=64      ,      __lowercase="hann_window"      ,      __lowercase=80      ,      __lowercase=7_600      ,      __lowercase=1E-10      ,      __lowercase=True      ,      ):

  """simple docstring"""







  __A  :		List[Any]       =			parent
  __A  :		Any       =			batch_size
  __A  :		Dict       =			min_seq_length
  __A  :		List[str]       =			max_seq_length
  __A  :		Optional[int]       =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  __A  :		Optional[int]       =			feature_size
  __A  :		List[Any]       =			padding_value
  __A  :		List[str]       =			sampling_rate
  __A  :		Optional[Any]       =			do_normalize
  __A  :		str       =			num_mel_bins
  __A  :		List[Any]       =			hop_length
  __A  :		Union[str, Any]       =			win_length
  __A  :		List[Any]       =			win_function
  __A  :		Optional[int]       =			fmin
  __A  :		Tuple       =			fmax
  __A  :		List[str]       =			mel_floor
  __A  :		Tuple       =			return_attention_mask


 def  snake_case__   (      self						):

  """simple docstring"""







  return {
      "feature_size": self.feature_size,
      "padding_value": self.padding_value,
      "sampling_rate": self.sampling_rate,
      "do_normalize": self.do_normalize,
      "num_mel_bins": self.num_mel_bins,
      "hop_length": self.hop_length,
      "win_length": self.win_length,
      "win_function": self.win_function,
      "fmin": self.fmin,
      "fmax": self.fmax,
      "mel_floor": self.mel_floor,
      "return_attention_mask": self.return_attention_mask,
  }


 def  snake_case__   (      self      ,      __lowercase=False      ,      __lowercase=False						):

  """simple docstring"""







  def _flatten(__lowercase						):
   return list(itertools.chain(*__lowercase						)						)

  if equal_length:
   __A  :		Union[str, Any]       =			floats_list((self.batch_size, self.max_seq_length)						)
  else:
   # make sure that inputs increase in size
   __A  :		List[str]       =			[
       _flatten(floats_list((x, self.feature_size)						)						)
       for x in range(self.min_seq_length      ,      self.max_seq_length      ,      self.seq_length_diff						)
   ]

  if numpify:
   __A  :		str       =			[np.asarray(__lowercase						) for x in speech_inputs]

  return speech_inputs







 def  snake_case__   (      self      ,      __lowercase=False      ,      __lowercase=False						):

  """simple docstring"""







  if equal_length:
   __A  :		Optional[Any]       =			[floats_list((self.max_seq_length, self.num_mel_bins)						) for _ in range(self.batch_size						)]
  else:
   # make sure that inputs increase in size
   __A  :		List[str]       =			[
       floats_list((x, self.num_mel_bins)						)
       for x in range(self.min_seq_length      ,      self.max_seq_length      ,      self.seq_length_diff						)
   ]

  if numpify:
   __A  :		Optional[Any]       =			[np.asarray(__lowercase						) for x in speech_inputs]

  return speech_inputs


@require_torch
class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						SpeechTaFeatureExtractor


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			SpeechTaFeatureExtractionTester(self						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  self.assertTrue(np.all(np.mean(__lowercase      ,      axis=0						) < 1E-3						)						)
  self.assertTrue(np.all(np.abs(np.var(__lowercase      ,      axis=0						) - 1						) < 1E-3						)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
  # create three inputs of length 800, 1000, and 1200
  __A  :		Dict       =			[floats_list((1, x)						)[0] for x in range(800      ,      1_400      ,      200						)]
  __A  :		Any       =			[np.asarray(__lowercase						) for speech_input in speech_inputs]

  # Test not batched input
  __A  :		List[Any]       =			feat_extract(speech_inputs[0]      ,      return_tensors='np'						).input_values
  __A  :		Any       =			feat_extract(np_speech_inputs[0]      ,      return_tensors='np'						).input_values
  self.assertTrue(np.allclose(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)

  # Test batched
  __A  :		Optional[Any]       =			feat_extract(__lowercase      ,      return_tensors='np'						).input_values
  __A  :		List[Any]       =			feat_extract(__lowercase      ,      return_tensors='np'						).input_values
  for enc_seq_a, enc_seq_a in zip(__lowercase      ,      __lowercase						):
   self.assertTrue(np.allclose(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
  __A  :		List[str]       =			[floats_list((1, x)						)[0] for x in range(800      ,      1_400      ,      200						)]

  __A  :		List[Any]       =			['longest', 'max_length', 'do_not_pad']
  __A  :		Dict       =			[None, 1_600, None]
  for max_length, padding in zip(__lowercase      ,      __lowercase						):
   __A  :		Optional[int]       =			feat_extract(__lowercase      ,      padding=__lowercase      ,      max_length=__lowercase      ,      return_tensors='np'						)
   __A  :		List[Any]       =			processed.input_values

   self._check_zero_mean_unit_variance(input_values[0][:800]						)
   self.assertTrue(input_values[0][800:].sum() < 1E-6						)
   self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
   self.assertTrue(input_values[0][1_000:].sum() < 1E-6						)
   self._check_zero_mean_unit_variance(input_values[2][:1_200]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
  __A  :		Optional[int]       =			range(800      ,      1_400      ,      200						)
  __A  :		Union[str, Any]       =			[floats_list((1, x)						)[0] for x in lengths]

  __A  :		Optional[Any]       =			['longest', 'max_length', 'do_not_pad']
  __A  :		List[Any]       =			[None, 1_600, None]

  for max_length, padding in zip(__lowercase      ,      __lowercase						):
   __A  :		Optional[int]       =			feat_extract(__lowercase      ,      max_length=__lowercase      ,      padding=__lowercase						)
   __A  :		str       =			processed.input_values

   self._check_zero_mean_unit_variance(input_values[0][:800]						)
   self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
   self._check_zero_mean_unit_variance(input_values[2][:1_200]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
  __A  :		Dict       =			[floats_list((1, x)						)[0] for x in range(800      ,      1_400      ,      200						)]
  __A  :		List[Any]       =			feat_extract(
      __lowercase      ,      truncation=__lowercase      ,      max_length=1_000      ,      padding='max_length'      ,      return_tensors='np'						)
  __A  :		Optional[int]       =			processed.input_values

  self._check_zero_mean_unit_variance(input_values[0, :800]						)
  self._check_zero_mean_unit_variance(input_values[1]						)
  self._check_zero_mean_unit_variance(input_values[2]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
  __A  :		int       =			[floats_list((1, x)						)[0] for x in range(800      ,      1_400      ,      200						)]
  __A  :		Any       =			feat_extract(
      __lowercase      ,      truncation=__lowercase      ,      max_length=1_000      ,      padding='longest'      ,      return_tensors='np'						)
  __A  :		Union[str, Any]       =			processed.input_values

  self._check_zero_mean_unit_variance(input_values[0, :800]						)
  self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
  self._check_zero_mean_unit_variance(input_values[2]						)

  # make sure that if max_length < longest -> then pad to max_length
  self.assertTrue(input_values.shape == (3, 1_000)						)

  __A  :		Optional[int]       =			[floats_list((1, x)						)[0] for x in range(800      ,      1_400      ,      200						)]
  __A  :		Any       =			feat_extract(
      __lowercase      ,      truncation=__lowercase      ,      max_length=2_000      ,      padding='longest'      ,      return_tensors='np'						)
  __A  :		Optional[int]       =			processed.input_values

  self._check_zero_mean_unit_variance(input_values[0, :800]						)
  self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
  self._check_zero_mean_unit_variance(input_values[2]						)

  # make sure that if max_length > longest -> then pad to longest
  self.assertTrue(input_values.shape == (3, 1_200)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
  __A  :		Optional[int]       =			np.random.rand(100						).astype(np.floataa						)
  __A  :		Dict       =			np_speech_inputs.tolist()

  for inputs in [py_speech_inputs, np_speech_inputs]:
   __A  :		Tuple       =			feature_extractor.pad([{'input_values': inputs}]      ,      return_tensors='np'						)
   self.assertTrue(np_processed.input_values.dtype == np.floataa						)
   __A  :		Tuple       =			feature_extractor.pad([{'input_values': inputs}]      ,      return_tensors='pt'						)
   self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
  # create three inputs of length 800, 1000, and 1200
  __A  :		List[str]       =			[floats_list((1, x)						)[0] for x in range(800      ,      1_400      ,      200						)]
  __A  :		List[str]       =			[np.asarray(__lowercase						) for speech_input in speech_inputs]

  # Test feature size
  __A  :		Tuple       =			feature_extractor(audio_target=__lowercase      ,      padding=__lowercase      ,      return_tensors='np'						).input_values
  self.assertTrue(input_values.ndim == 3						)
  self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins						)

  # Test not batched input
  __A  :		Tuple       =			feature_extractor(speech_inputs[0]      ,      return_tensors='np'						).input_values
  __A  :		Dict       =			feature_extractor(np_speech_inputs[0]      ,      return_tensors='np'						).input_values
  self.assertTrue(np.allclose(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)

  # Test batched
  __A  :		Union[str, Any]       =			feature_extractor(__lowercase      ,      return_tensors='np'						).input_values
  __A  :		str       =			feature_extractor(__lowercase      ,      return_tensors='np'						).input_values
  for enc_seq_a, enc_seq_a in zip(__lowercase      ,      __lowercase						):
   self.assertTrue(np.allclose(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)

  # Test 2-D numpy arrays are batched.
  __A  :		Dict       =			[floats_list((1, x)						)[0] for x in (800, 800, 800)]
  __A  :		Optional[int]       =			np.asarray(__lowercase						)
  __A  :		Any       =			feature_extractor(__lowercase      ,      return_tensors='np'						).input_values
  __A  :		Optional[Any]       =			feature_extractor(__lowercase      ,      return_tensors='np'						).input_values
  for enc_seq_a, enc_seq_a in zip(__lowercase      ,      __lowercase						):
   self.assertTrue(np.allclose(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.feat_extract_tester.prepare_inputs_for_target()
  __A  :		Tuple       =			self.feature_extraction_class(**self.feat_extract_dict						)
  __A  :		int       =			feat_extract.model_input_names[0]

  __A  :		List[str]       =			BatchFeature({input_name: speech_inputs}						)

  self.assertTrue(all(len(__lowercase						) == len(__lowercase						) for x, y in zip(__lowercase      ,      processed_features[input_name]						)						)						)

  __A  :		Tuple       =			self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowercase						)
  __A  :		Union[str, Any]       =			BatchFeature({input_name: speech_inputs}      ,      tensor_type='np'						)

  __A  :		Tuple       =			processed_features[input_name]

  if len(batch_features_input.shape						) < 3:
   __A  :		Union[str, Any]       =			batch_features_input[:, :, None]

  self.assertTrue(
      batch_features_input.shape
      == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.num_mel_bins)						)


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowercase						)
  __A  :		str       =			self.feature_extraction_class(**self.feat_extract_dict						)
  __A  :		Any       =			feat_extract.model_input_names[0]

  __A  :		str       =			BatchFeature({input_name: speech_inputs}      ,      tensor_type='pt'						)

  __A  :		int       =			processed_features[input_name]

  if len(batch_features_input.shape						) < 3:
   __A  :		str       =			batch_features_input[:, :, None]

  self.assertTrue(
      batch_features_input.shape
      == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.num_mel_bins)						)


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.feature_extraction_class(**self.feat_extract_dict						)
  __A  :		str       =			self.feat_extract_tester.prepare_inputs_for_target()
  __A  :		Optional[Any]       =			feat_extract.model_input_names[0]

  __A  :		List[Any]       =			BatchFeature({input_name: speech_inputs}						)

  __A  :		Optional[Any]       =			feat_extract.num_mel_bins  # hack!

  __A  :		int       =			feat_extract.pad(__lowercase      ,      padding='longest'      ,      return_tensors='np'						)[input_name]
  __A  :		Optional[int]       =			feat_extract.pad(__lowercase      ,      padding='longest'      ,      return_tensors='pt'						)[input_name]

  self.assertTrue(abs(input_np.astype(np.floataa						).sum() - input_pt.numpy().astype(np.floataa						).sum()						) < 1E-2						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.feat_extract_dict
  __A  :		Tuple       =			True
  __A  :		Optional[Any]       =			self.feature_extraction_class(**__lowercase						)
  __A  :		int       =			self.feat_extract_tester.prepare_inputs_for_target()
  __A  :		Tuple       =			[len(__lowercase						) for x in speech_inputs]
  __A  :		int       =			feat_extract.model_input_names[0]

  __A  :		str       =			BatchFeature({input_name: speech_inputs}						)

  __A  :		Optional[Any]       =			feat_extract.num_mel_bins  # hack!

  __A  :		Optional[int]       =			feat_extract.pad(__lowercase      ,      padding='longest'      ,      return_tensors='np'						)
  self.assertIn('attention_mask'      ,      __lowercase						)
  self.assertListEqual(list(processed.attention_mask.shape						)      ,      list(processed[input_name].shape[:2]						)						)
  self.assertListEqual(processed.attention_mask.sum(-1						).tolist()      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			self.feat_extract_dict
  __A  :		Dict       =			True
  __A  :		Optional[int]       =			self.feature_extraction_class(**__lowercase						)
  __A  :		Any       =			self.feat_extract_tester.prepare_inputs_for_target()
  __A  :		Optional[int]       =			[len(__lowercase						) for x in speech_inputs]
  __A  :		str       =			feat_extract.model_input_names[0]

  __A  :		List[str]       =			BatchFeature({input_name: speech_inputs}						)
  __A  :		Optional[int]       =			min(__lowercase						)

  __A  :		int       =			feat_extract.num_mel_bins  # hack!

  __A  :		int       =			feat_extract.pad(
      __lowercase      ,      padding='max_length'      ,      max_length=__lowercase      ,      truncation=__lowercase      ,      return_tensors='np'						)
  self.assertIn('attention_mask'      ,      __lowercase						)
  self.assertListEqual(
      list(processed_pad.attention_mask.shape						)      ,      [processed_pad[input_name].shape[0], max_length]						)
  self.assertListEqual(
      processed_pad.attention_mask[:, :max_length].sum(-1						).tolist()      ,      [max_length for x in speech_inputs]						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  from datasets import load_dataset

  __A  :		List[Any]       =			load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,      'clean'      ,      split='validation'						)
  # automatic decoding with librispeech
  __A  :		List[Any]       =			ds.sort('id'						).select(range(__lowercase						)						)[:num_samples]['audio']

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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			torch.tensor(
      [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
       3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
       2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
       4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
       7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
       4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]						)
  # fmt: on

  __A  :		Dict       =			self._load_datasamples(1						)
  __A  :		Any       =			SpeechTaFeatureExtractor()
  __A  :		Optional[Any]       =			feature_extractor(__lowercase      ,      return_tensors='pt'						).input_values
  self.assertEquals(input_values.shape      ,      (1, 93_680)						)
  self.assertTrue(torch.allclose(input_values[0, :30]      ,      __lowercase      ,      atol=1E-6						)						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			torch.tensor(
      [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
       -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
       -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
       -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]						)
  # fmt: on

  __A  :		Dict       =			self._load_datasamples(1						)
  __A  :		List[Any]       =			SpeechTaFeatureExtractor()
  __A  :		str       =			feature_extractor(audio_target=__lowercase      ,      return_tensors='pt'						).input_values
  self.assertEquals(input_values.shape      ,      (1, 366, 80)						)
  self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,      __lowercase      ,      atol=1E-4						)						)










'''simple docstring'''
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow

@is_pipeline_test
@require_torch
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			pipeline(
      task='zero-shot-audio-classification'      ,      model='hf-internal-testing/tiny-clap-htsat-unfused'						)
  __A  :		str       =			load_dataset('ashraq/esc50'						)
  __A  :		Union[str, Any]       =			dataset['train']['audio'][-1]['array']
  __A  :		List[str]       =			audio_classifier(__lowercase      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']						)
  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [{'score': 0.5_0_1, 'label': 'Sound of a dog'}, {'score': 0.4_9_9, 'label': 'Sound of vaccum cleaner'}]      ,      )


 @unittest.skip('No models are available in TF'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @slow
 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			pipeline(
      task='zero-shot-audio-classification'      ,      model='laion/clap-htsat-unfused'      ,      )
  # This is an audio of a dog
  __A  :		Optional[Any]       =			load_dataset('ashraq/esc50'						)
  __A  :		List[str]       =			dataset['train']['audio'][-1]['array']
  __A  :		Union[str, Any]       =			audio_classifier(__lowercase      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']						)

  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [
          {'score': 0.9_9_9, 'label': 'Sound of a dog'},
          {'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
      ]      ,      )

  __A  :		Any       =			audio_classifier([audio] * 5      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']						)
  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [
          [
              {'score': 0.9_9_9, 'label': 'Sound of a dog'},
              {'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
          ],
      ]
      * 5      ,      )
  __A  :		str       =			audio_classifier(
      [audio] * 5      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']      ,      batch_size=5						)
  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [
          [
              {'score': 0.9_9_9, 'label': 'Sound of a dog'},
              {'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
          ],
      ]
      * 5      ,      )







 @unittest.skip('No models are available in TF'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass






