






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

SCREAMING_SNAKE_CASE__											=     random.Random()







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1.0	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				List[Any]:

      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






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

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=7    ,	_SCREAMING_SNAKE_CASE=400    ,	_SCREAMING_SNAKE_CASE=2000    ,	_SCREAMING_SNAKE_CASE=24    ,	_SCREAMING_SNAKE_CASE=24    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=16000    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	)						->   int:



        """simple docstring"""






        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       						=      feature_size
        UpperCamelCase       						=      num_mel_bins
        UpperCamelCase       						=      padding_value
        UpperCamelCase       						=      sampling_rate
        UpperCamelCase       						=      return_attention_mask
        UpperCamelCase       						=      do_normalize

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



        """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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=False   )						->   Optional[int]:



        """simple docstring"""






        def _flatten(_SCREAMING_SNAKE_CASE   ):
              return list(itertools.chain(*A_   )   )

        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(A_   ) for x in speech_inputs]
        return speech_inputs






@require_torch
@require_torchaudio
class        a_		(    _SCREAMING_SNAKE_CASE			, unittest.TestCase       ):
  lowercase  	=   SpeechaTextFeatureExtractor if is_speech_available() else None

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      SpeechaTextFeatureExtractionTester(self   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






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

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_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(A_   ) for speech_input in speech_inputs]

        # Test feature size
        UpperCamelCase       						=      feature_extractor(A_    ,	padding=A_    ,	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
        UpperCamelCase       						=      feature_extractor(speech_inputs[0]    ,	return_tensors="""np"""   ).input_features
        UpperCamelCase       						=      feature_extractor(np_speech_inputs[0]    ,	return_tensors="""np"""   ).input_features
        self.assertTrue(np.allclose(A_    ,	A_    ,	atol=1e-3   )   )

        # Test batched
        UpperCamelCase       						=      feature_extractor(A_    ,	return_tensors="""np"""   ).input_features
        UpperCamelCase       						=      feature_extractor(A_    ,	return_tensors="""np"""   ).input_features
        for enc_seq_a, enc_seq_a in zip(A_    ,	A_   ):
              self.assertTrue(np.allclose(A_    ,	A_    ,	atol=1e-3   )   )

        # Test 2-D numpy arrays are batched.
        UpperCamelCase       						=      [floats_list((1, x)   )[0] for x in (800, 800, 800)]
        UpperCamelCase       						=      np.asarray(A_   )
        UpperCamelCase       						=      feature_extractor(A_    ,	return_tensors="""np"""   ).input_features
        UpperCamelCase       						=      feature_extractor(A_    ,	return_tensors="""np"""   ).input_features
        for enc_seq_a, enc_seq_a in zip(A_    ,	A_   ):
              self.assertTrue(np.allclose(A_    ,	A_    ,	atol=1e-3   )   )

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        UpperCamelCase       						=      [floats_list((1, x)   )[0] for x in range(800    ,	1400    ,	200   )]

        UpperCamelCase       						=      ["""longest""", """max_length""", """do_not_pad"""]
        UpperCamelCase       						=      [None, 16, None]
        for max_length, padding in zip(A_    ,	A_   ):
              UpperCamelCase       						=      feature_extractor(
                  A_    ,	padding=A_    ,	max_length=A_    ,	return_attention_mask=A_   )
              UpperCamelCase       						=      inputs.input_features
              UpperCamelCase       						=      inputs.attention_mask
              UpperCamelCase       						=      [np.sum(A_   ) 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 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        UpperCamelCase       						=      [floats_list((1, x)   )[0] for x in range(800    ,	1400    ,	200   )]

        UpperCamelCase       						=      ["""longest""", """max_length""", """do_not_pad"""]
        UpperCamelCase       						=      [None, 16, None]
        for max_length, padding in zip(A_    ,	A_   ):
              UpperCamelCase       						=      feature_extractor(
                  A_    ,	max_length=A_    ,	padding=A_    ,	return_tensors="""np"""    ,	return_attention_mask=A_   )
              UpperCamelCase       						=      inputs.input_features
              UpperCamelCase       						=      inputs.attention_mask
              UpperCamelCase       						=      [np.sum(A_   ) 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 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        UpperCamelCase       						=      [floats_list((1, x)   )[0] for x in range(800    ,	1400    ,	200   )]
        UpperCamelCase       						=      feature_extractor(
            A_    ,	padding="""max_length"""    ,	max_length=4    ,	truncation=A_    ,	return_tensors="""np"""    ,	return_attention_mask=A_    ,	)
        UpperCamelCase       						=      inputs.input_features
        UpperCamelCase       						=      inputs.attention_mask
        UpperCamelCase       						=      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 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        UpperCamelCase       						=      [floats_list((1, x)   )[0] for x in range(800    ,	1400    ,	200   )]
        UpperCamelCase       						=      feature_extractor(
            A_    ,	padding="""longest"""    ,	max_length=4    ,	truncation=A_    ,	return_tensors="""np"""    ,	return_attention_mask=A_    ,	)
        UpperCamelCase       						=      inputs.input_features
        UpperCamelCase       						=      inputs.attention_mask
        UpperCamelCase       						=      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, 24)   )

        UpperCamelCase       						=      [floats_list((1, x)   )[0] for x in range(800    ,	1400    ,	200   )]
        UpperCamelCase       						=      feature_extractor(
            A_    ,	padding="""longest"""    ,	max_length=16    ,	truncation=A_    ,	return_tensors="""np"""    ,	return_attention_mask=A_    ,	)
        UpperCamelCase       						=      inputs.input_features
        UpperCamelCase       						=      inputs.attention_mask
        UpperCamelCase       						=      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, 24)   )

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        import torch

        UpperCamelCase       						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        UpperCamelCase       						=      np.random.rand(100    ,	32   ).astype(np.floataa   )
        UpperCamelCase       						=      np_speech_inputs.tolist()

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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        from datasets import load_dataset

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

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


  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      np.array([
            -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
            -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
            -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
        ]   )
        # fmt: on

        UpperCamelCase       						=      self._load_datasamples(1   )
        UpperCamelCase       						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
        UpperCamelCase       						=      feature_extractor(A_    ,	return_tensors="""pt"""   ).input_features
        self.assertEquals(input_features.shape    ,	(1, 584, 24)   )
        self.assertTrue(np.allclose(input_features[0, 0, :30]    ,	A_    ,	atol=1e-4   )   )








'''simple docstring'''

from __future__ import annotations







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				tuple[float, list[float]]:
      UpperCamelCase       						=      list(range(len(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [v / w for v, w in zip(__UpperCamelCase	,		__UpperCamelCase					)]
      index.sort(key=lambda __UpperCamelCase					: ratio[i]	,		reverse=__UpperCamelCase					)

      UpperCamelCase       						=      0
      UpperCamelCase       						=      [0] * len(__UpperCamelCase					)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase       						=      1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase       						=      capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()
