





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor

UpperCAmelCase     :     int							=	random.Random()
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       List[str]=1.0	,	SCREAMING_SNAKE_CASE					:       Dict=None	,	SCREAMING_SNAKE_CASE					:       Any=None						):





  """simple docstring"""



  if rng is None:
    a__     : Union[str, Any]  =global_rng

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

  return values







@require_torch
@require_torchaudio
class     __lowerCAmelCase	(   unittest.TestCase):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=7		,				lowerCAmelCase__=4_0_0		,				lowerCAmelCase__=2_0_0_0		,				lowerCAmelCase__=2_4		,				lowerCAmelCase__=2_4		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=1_6_0_0_0		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				)       ->						Dict:



        '''simple docstring'''




        a__     : Tuple  =parent
        a__     : Any  =batch_size
        a__     : List[str]  =min_seq_length
        a__     : Dict  =max_seq_length
        a__     : List[str]  =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__     : Tuple  =feature_size
        a__     : List[str]  =num_mel_bins
        a__     : Optional[int]  =padding_value
        a__     : Any  =sampling_rate
        a__     : Optional[Any]  =return_attention_mask
        a__     : List[Any]  =do_normalize







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        return {
            "feature_size": self.feature_size,
            "num_mel_bins": self.num_mel_bins,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }







      def        _lowercase  (   self		,				lowerCAmelCase__=False		,				lowerCAmelCase__=False							)       ->						Any:



        '''simple docstring'''




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

        if equal_length:
          a__     : str  =[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
        else:
          # make sure that inputs increase in size
          a__     : Optional[Any]  =[
              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__     : Dict  =[np.asarray(lowercase_							) for x in speech_inputs]
        return speech_inputs







@require_torch
@require_torchaudio
class     __lowerCAmelCase	(   lowercase__						,    unittest.TestCase):
      _lowercase    : List[Any]            =      SpeechaTextFeatureExtractor if is_speech_available() else None







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : str  =SpeechaTextFeatureExtractionTester(self							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Tuple:



        '''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        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        # create three inputs of length 800, 1000, and 1200
        a__     : int  =[floats_list((1, x)							)[0] for x in range(8_0_0		,				1_4_0_0		,				2_0_0							)]
        a__     : List[str]  =[np.asarray(lowercase_							) for speech_input in speech_inputs]

        # Test feature size
        a__     : int  =feature_extractor(lowercase_		,				padding=lowercase_		,				return_tensors="np"							).input_features
        self.assertTrue(input_features.ndim == 3							)
        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size							)

        # Test not batched input
        a__     : int  =feature_extractor(speech_inputs[0]		,				return_tensors="np"							).input_features
        a__     : Optional[int]  =feature_extractor(np_speech_inputs[0]		,				return_tensors="np"							).input_features
        self.assertTrue(np.allclose(lowercase_		,				lowercase_		,				atol=1E-3							)							)

        # Test batched
        a__     : Dict  =feature_extractor(lowercase_		,				return_tensors="np"							).input_features
        a__     : Tuple  =feature_extractor(lowercase_		,				return_tensors="np"							).input_features
        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__     : List[Any]  =[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
        a__     : Tuple  =np.asarray(lowercase_							)
        a__     : int  =feature_extractor(lowercase_		,				return_tensors="np"							).input_features
        a__     : str  =feature_extractor(lowercase_		,				return_tensors="np"							).input_features
        for enc_seq_a, enc_seq_a in zip(lowercase_		,				lowercase_							):
          self.assertTrue(np.allclose(lowercase_		,				lowercase_		,				atol=1E-3							)							)







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



        '''simple docstring'''




        a__     : Union[str, Any]  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        a__     : Dict  =[floats_list((1, x)							)[0] for x in range(8_0_0		,				1_4_0_0		,				2_0_0							)]

        a__     : int  =["longest", "max_length", "do_not_pad"]
        a__     : Optional[Any]  =[None, 1_6, None]
        for max_length, padding in zip(lowercase_		,				lowercase_							):
          a__     : Dict  =feature_extractor(
              lowercase_		,				padding=lowercase_		,				max_length=lowercase_		,				return_attention_mask=lowercase_							)
          a__     : Any  =inputs.input_features
          a__     : List[Any]  =inputs.attention_mask
          a__     : Dict  =[np.sum(lowercase_							) for x in attention_mask]

          self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
          self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
          self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)







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



        '''simple docstring'''




        a__     : Tuple  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        a__     : Union[str, Any]  =[floats_list((1, x)							)[0] for x in range(8_0_0		,				1_4_0_0		,				2_0_0							)]

        a__     : Union[str, Any]  =["longest", "max_length", "do_not_pad"]
        a__     : Optional[Any]  =[None, 1_6, None]
        for max_length, padding in zip(lowercase_		,				lowercase_							):
          a__     : Optional[Any]  =feature_extractor(
              lowercase_		,				max_length=lowercase_		,				padding=lowercase_		,				return_tensors="np"		,				return_attention_mask=lowercase_							)
          a__     : Optional[int]  =inputs.input_features
          a__     : Union[str, Any]  =inputs.attention_mask
          a__     : Union[str, Any]  =[np.sum(lowercase_							) for x in attention_mask]

          self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
          self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6							)
          self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
          self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6							)
          self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[Any]  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        a__     : Any  =[floats_list((1, x)							)[0] for x in range(8_0_0		,				1_4_0_0		,				2_0_0							)]
        a__     : List[Any]  =feature_extractor(
            lowercase_		,				padding="max_length"		,				max_length=4		,				truncation=lowercase_		,				return_tensors="np"		,				return_attention_mask=lowercase_		,				)
        a__     : Dict  =inputs.input_features
        a__     : Optional[int]  =inputs.attention_mask
        a__     : int  =np.sum(attention_mask == 1		,				axis=1							)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
        self._check_zero_mean_unit_variance(input_features[1]							)
        self._check_zero_mean_unit_variance(input_features[2]							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        a__     : Optional[int]  =[floats_list((1, x)							)[0] for x in range(8_0_0		,				1_4_0_0		,				2_0_0							)]
        a__     : Dict  =feature_extractor(
            lowercase_		,				padding="longest"		,				max_length=4		,				truncation=lowercase_		,				return_tensors="np"		,				return_attention_mask=lowercase_		,				)
        a__     : Tuple  =inputs.input_features
        a__     : int  =inputs.attention_mask
        a__     : List[Any]  =np.sum(attention_mask == 1		,				axis=1							)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
        self._check_zero_mean_unit_variance(input_features[2]							)

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape		,				(3, 4, 2_4)							)

        a__     : Tuple  =[floats_list((1, x)							)[0] for x in range(8_0_0		,				1_4_0_0		,				2_0_0							)]
        a__     : Dict  =feature_extractor(
            lowercase_		,				padding="longest"		,				max_length=1_6		,				truncation=lowercase_		,				return_tensors="np"		,				return_attention_mask=lowercase_		,				)
        a__     : Dict  =inputs.input_features
        a__     : int  =inputs.attention_mask
        a__     : Any  =np.sum(attention_mask == 1		,				axis=1							)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
        self._check_zero_mean_unit_variance(input_features[2]							)

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape		,				(3, 6, 2_4)							)







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



        '''simple docstring'''




        import torch

        a__     : Any  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        a__     : Union[str, Any]  =np.random.rand(1_0_0		,				3_2							).astype(np.floataa							)
        a__     : Tuple  =np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
          a__     : Optional[int]  =feature_extractor.pad([{"input_features": inputs}]		,				return_tensors="np"							)
          self.assertTrue(np_processed.input_features.dtype == np.floataa							)
          a__     : Dict  =feature_extractor.pad([{"input_features": inputs}]		,				return_tensors="pt"							)
          self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)







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



        '''simple docstring'''




        from datasets import load_dataset

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

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







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



        '''simple docstring'''




        a__     : List[Any]  =np.array([
            -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
            -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
            -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
        ]							)
        # fmt: on

        a__     : List[Any]  =self._load_datasamples(1							)
        a__     : List[Any]  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        a__     : Optional[int]  =feature_extractor(lowercase_		,				return_tensors="pt"							).input_features
        self.assertEquals(input_features.shape		,				(1, 5_8_4, 2_4)							)
        self.assertTrue(np.allclose(input_features[0, 0, :3_0]		,				lowercase_		,				atol=1E-4							)							)







import random
from typing import Any
def 					_A	(		SCREAMING_SNAKE_CASE					:       list						):





  """simple docstring"""



  for _ in range(len(SCREAMING_SNAKE_CASE						)						):
    a__     : Tuple  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__     : Any  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__ ,							a__     : Tuple  =data[b], data[a]
  return data


if __name__ == "__main__":
     UpperCAmelCase     :     Dict							=	[0, 1, 2, 3, 4, 5, 6, 7]
     UpperCAmelCase     :     Optional[Any]							=	["""python""", """says""", """hello""", """!"""]
     print("""Fisher-Yates Shuffle:""")
     print("""List""", integers, strings)
     print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






