





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


lowerCamelCase_       =		random.Random()



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Any=1.0			,       a_					:		Dict=None			,       a_					:		List[str]=None							)      ->				Union[str, Any]:
      if rng is None:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  global_rng

      __SCREAMING_SNAKE_CASE       :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  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=7		,SCREAMING_SNAKE_CASE__=4_00		,SCREAMING_SNAKE_CASE__=20_00		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=1_60_00		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=80		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=64		,SCREAMING_SNAKE_CASE__="hann_window"		,SCREAMING_SNAKE_CASE__=80		,SCREAMING_SNAKE_CASE__=76_00		,SCREAMING_SNAKE_CASE__=1E-10		,SCREAMING_SNAKE_CASE__=True		,)      ->				Optional[int]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				int:





             """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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False							)      ->				List[str]:





             """simple docstring"""






             def _flatten(SCREAMING_SNAKE_CASE__							):
                   return list(itertools.chain(*lowercase_							)							)

             if equal_length:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  floats_list((self.batch_size, self.max_seq_length)							)
             else:
                   # make sure that inputs increase in size
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  [
                       _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:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [np.asarray(lowercase_							) for x in speech_inputs]

             return speech_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False							)      ->				Dict:





             """simple docstring"""






             if equal_length:
                   __SCREAMING_SNAKE_CASE       :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
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [
                       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:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  [np.asarray(lowercase_							) for x in speech_inputs]

             return speech_inputs






@require_torch
class  _SCREAMING_SNAKE_CASE(       _UpperCAmelCase   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  SpeechTaFeatureExtractor




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  SpeechTaFeatureExtractionTester(self							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """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    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             # create three inputs of length 800, 1000, and 1200
             __SCREAMING_SNAKE_CASE       :int		    =  [floats_list((1, x)							)[0] for x in range(8_00		,14_00		,2_00							)]
             __SCREAMING_SNAKE_CASE       :Any		    =  [np.asarray(lowercase_							) for speech_input in speech_inputs]

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

             # Test batched
             __SCREAMING_SNAKE_CASE       :Dict		    =  feat_extract(lowercase_		,return_tensors='''np'''							).input_values
             __SCREAMING_SNAKE_CASE       :Optional[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    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __SCREAMING_SNAKE_CASE       :int		    =  [floats_list((1, x)							)[0] for x in range(8_00		,14_00		,2_00							)]

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  ["""longest""", """max_length""", """do_not_pad"""]
             __SCREAMING_SNAKE_CASE       :int		    =  [None, 16_00, None]
             for max_length, padding in zip(lowercase_		,lowercase_							):
                   __SCREAMING_SNAKE_CASE       :Any		    =  feat_extract(lowercase_		,padding=lowercase_		,max_length=lowercase_		,return_tensors='''np'''							)
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_00]							)
                   self.assertTrue(input_values[0][8_00:].sum() < 1E-6							)
                   self._check_zero_mean_unit_variance(input_values[1][:10_00]							)
                   self.assertTrue(input_values[0][10_00:].sum() < 1E-6							)
                   self._check_zero_mean_unit_variance(input_values[2][:12_00]							)




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __SCREAMING_SNAKE_CASE       :Any		    =  range(8_00		,14_00		,2_00							)
             __SCREAMING_SNAKE_CASE       :int		    =  [floats_list((1, x)							)[0] for x in lengths]

             __SCREAMING_SNAKE_CASE       :int		    =  ["""longest""", """max_length""", """do_not_pad"""]
             __SCREAMING_SNAKE_CASE       :str		    =  [None, 16_00, None]

             for max_length, padding in zip(lowercase_		,lowercase_							):
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  feat_extract(lowercase_		,max_length=lowercase_		,padding=lowercase_							)
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_00]							)
                   self._check_zero_mean_unit_variance(input_values[1][:10_00]							)
                   self._check_zero_mean_unit_variance(input_values[2][:12_00]							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  [floats_list((1, x)							)[0] for x in range(8_00		,14_00		,2_00							)]
             __SCREAMING_SNAKE_CASE       :int		    =  feat_extract(
                 lowercase_		,truncation=lowercase_		,max_length=10_00		,padding='''max_length'''		,return_tensors='''np'''							)
             __SCREAMING_SNAKE_CASE       :int		    =  processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :8_00]							)
             self._check_zero_mean_unit_variance(input_values[1]							)
             self._check_zero_mean_unit_variance(input_values[2]							)




       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [floats_list((1, x)							)[0] for x in range(8_00		,14_00		,2_00							)]
             __SCREAMING_SNAKE_CASE       :str		    =  feat_extract(
                 lowercase_		,truncation=lowercase_		,max_length=10_00		,padding='''longest'''		,return_tensors='''np'''							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :8_00]							)
             self._check_zero_mean_unit_variance(input_values[1, :10_00]							)
             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, 10_00)							)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  [floats_list((1, x)							)[0] for x in range(8_00		,14_00		,2_00							)]
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  feat_extract(
                 lowercase_		,truncation=lowercase_		,max_length=20_00		,padding='''longest'''		,return_tensors='''np'''							)
             __SCREAMING_SNAKE_CASE       :int		    =  processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :8_00]							)
             self._check_zero_mean_unit_variance(input_values[1, :10_00]							)
             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, 12_00)							)




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  np.random.rand(1_00							).astype(np.floataa							)
             __SCREAMING_SNAKE_CASE       :Any		    =  np_speech_inputs.tolist()

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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             # create three inputs of length 800, 1000, and 1200
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  [floats_list((1, x)							)[0] for x in range(8_00		,14_00		,2_00							)]
             __SCREAMING_SNAKE_CASE       :Tuple		    =  [np.asarray(lowercase_							) for speech_input in speech_inputs]

             # Test feature size
             __SCREAMING_SNAKE_CASE       :Dict		    =  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
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  feature_extractor(speech_inputs[0]		,return_tensors='''np'''							).input_values
             __SCREAMING_SNAKE_CASE       :Any		    =  feature_extractor(np_speech_inputs[0]		,return_tensors='''np'''							).input_values
             self.assertTrue(np.allclose(lowercase_		,lowercase_		,atol=1E-3							)							)

             # Test batched
             __SCREAMING_SNAKE_CASE       :Dict		    =  feature_extractor(lowercase_		,return_tensors='''np'''							).input_values
             __SCREAMING_SNAKE_CASE       :int		    =  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.
             __SCREAMING_SNAKE_CASE       :str		    =  [floats_list((1, x)							)[0] for x in (8_00, 8_00, 8_00)]
             __SCREAMING_SNAKE_CASE       :str		    =  np.asarray(lowercase_							)
             __SCREAMING_SNAKE_CASE       :Any		    =  feature_extractor(lowercase_		,return_tensors='''np'''							).input_values
             __SCREAMING_SNAKE_CASE       :Dict		    =  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    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.feat_extract_tester.prepare_inputs_for_target()
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.feature_extraction_class(**self.feat_extract_dict							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  feat_extract.model_input_names[0]

             __SCREAMING_SNAKE_CASE       :Dict		    =  BatchFeature({input_name: speech_inputs}							)

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

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  BatchFeature({input_name: speech_inputs}		,tensor_type='''np'''							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  processed_features[input_name]

             if len(batch_features_input.shape							) < 3:
                   __SCREAMING_SNAKE_CASE       :List[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    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.feature_extraction_class(**self.feat_extract_dict							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  feat_extract.model_input_names[0]

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  BatchFeature({input_name: speech_inputs}		,tensor_type='''pt'''							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  processed_features[input_name]

             if len(batch_features_input.shape							) < 3:
                   __SCREAMING_SNAKE_CASE       :List[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    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.feature_extraction_class(**self.feat_extract_dict							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.feat_extract_tester.prepare_inputs_for_target()
             __SCREAMING_SNAKE_CASE       :List[str]		    =  feat_extract.model_input_names[0]

             __SCREAMING_SNAKE_CASE       :Tuple		    =  BatchFeature({input_name: speech_inputs}							)

             __SCREAMING_SNAKE_CASE       :str		    =  feat_extract.num_mel_bins  # hack!

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  feat_extract.pad(lowercase_		,padding='''longest'''		,return_tensors='''np'''							)[input_name]
             __SCREAMING_SNAKE_CASE       :Dict		    =  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    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.feat_extract_dict
             __SCREAMING_SNAKE_CASE       :str		    =  True
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.feature_extraction_class(**lowercase_							)
             __SCREAMING_SNAKE_CASE       :str		    =  self.feat_extract_tester.prepare_inputs_for_target()
             __SCREAMING_SNAKE_CASE       :Any		    =  [len(lowercase_							) for x in speech_inputs]
             __SCREAMING_SNAKE_CASE       :List[str]		    =  feat_extract.model_input_names[0]

             __SCREAMING_SNAKE_CASE       :Any		    =  BatchFeature({input_name: speech_inputs}							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  feat_extract.num_mel_bins  # hack!

             __SCREAMING_SNAKE_CASE       :Dict		    =  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    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.feat_extract_dict
             __SCREAMING_SNAKE_CASE       :Tuple		    =  True
             __SCREAMING_SNAKE_CASE       :int		    =  self.feature_extraction_class(**lowercase_							)
             __SCREAMING_SNAKE_CASE       :int		    =  self.feat_extract_tester.prepare_inputs_for_target()
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  [len(lowercase_							) for x in speech_inputs]
             __SCREAMING_SNAKE_CASE       :Dict		    =  feat_extract.model_input_names[0]

             __SCREAMING_SNAKE_CASE       :List[str]		    =  BatchFeature({input_name: speech_inputs}							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  min(lowercase_							)

             __SCREAMING_SNAKE_CASE       :str		    =  feat_extract.num_mel_bins  # hack!

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             from datasets import load_dataset

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

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




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  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

             __SCREAMING_SNAKE_CASE       :List[str]		    =  self._load_datasamples(1							)
             __SCREAMING_SNAKE_CASE       :str		    =  SpeechTaFeatureExtractor()
             __SCREAMING_SNAKE_CASE       :int		    =  feature_extractor(lowercase_		,return_tensors='''pt'''							).input_values
             self.assertEquals(input_values.shape		,(1, 9_36_80)							)
             self.assertTrue(torch.allclose(input_values[0, :30]		,lowercase_		,atol=1E-6							)							)




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  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

             __SCREAMING_SNAKE_CASE       :str		    =  self._load_datasamples(1							)
             __SCREAMING_SNAKE_CASE       :str		    =  SpeechTaFeatureExtractor()
             __SCREAMING_SNAKE_CASE       :int		    =  feature_extractor(audio_target=lowercase_		,return_tensors='''pt'''							).input_values
             self.assertEquals(input_values.shape		,(1, 3_66, 80)							)
             self.assertTrue(torch.allclose(input_values[0, 0, :30]		,lowercase_		,atol=1E-4							)							)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Optional[Any]							)      ->				Union[str, Any]:
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Any=0							)      ->				Optional[Any]:
      return sorted(a_			,       key=lambda a_							: x[column]							)



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		Optional[int]			,       a_					:		str=float('''inf'''							)							)      ->				str:

      for i in range(points_counts - 1							):
            for j in range(i + 1			,       a_							):
                  __SCREAMING_SNAKE_CASE       :Dict		    =  euclidean_distance_sqr(points[i]			,       points[j]							)
                  if current_dis < min_dis:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  current_dis
      return min_dis



def 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Any			,       a_					:		Optional[int]=float('''inf'''							)							)      ->				Optional[Any]:

      for i in range(min(6			,       points_counts - 1							)			,       a_							):
            for j in range(max(0			,       i - 6							)			,       a_							):
                  __SCREAMING_SNAKE_CASE       :Dict		    =  euclidean_distance_sqr(points[i]			,       points[j]							)
                  if current_dis < min_dis:
                        __SCREAMING_SNAKE_CASE       :int		    =  current_dis
      return min_dis



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		List[Any]			,       a_					:		int							)      ->				Optional[int]:

      # base case
      if points_counts <= 3:
            return dis_between_closest_pair(a_			,       a_							)

      # recursion
      __SCREAMING_SNAKE_CASE       :int		    =  points_counts // 2
      __SCREAMING_SNAKE_CASE       :Dict		    =  closest_pair_of_points_sqr(
          a_			,       points_sorted_on_y[:mid]			,       a_							)
      __SCREAMING_SNAKE_CASE       :Any		    =  closest_pair_of_points_sqr(
          a_			,       points_sorted_on_y[mid:]			,       points_counts - mid							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  min(a_			,       a_							)
      __SCREAMING_SNAKE_CASE       :str		    =  []
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]							) < closest_pair_dis:
                  cross_strip.append(a_							)

      __SCREAMING_SNAKE_CASE       :Dict		    =  dis_between_closest_in_strip(
          a_			,       len(a_							)			,       a_							)
      return min(a_			,       a_							)



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Tuple							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  column_based_sort(a_			,       column=0							)
      __SCREAMING_SNAKE_CASE       :int		    =  column_based_sort(a_			,       column=1							)
      return (
          closest_pair_of_points_sqr(
              a_			,       a_			,       a_							)
      ) ** 0.5


if __name__ == "__main__":
  lowerCamelCase_       =		[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print("Distance:", closest_pair_of_points(points, len(points)))