

"""simple docstring"""




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 =				random.Random()
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=1.0							,    __UpperCamelCase=None							,    __UpperCamelCase=None      ):
 '''simple docstring'''





 if rng is None:
  UpperCAmelCase__					:  Tuple					   =					global_rng

 UpperCAmelCase__					:  int					   =					[]
 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      __lowercase			(    unittest.TestCase					):

  def __init__(  self		:			Optional[int]					,A		:			List[Any]					,A		:			int=7					,A		:			List[str]=400					,A		:			int=2_000					,A		:			Optional[int]=24					,A		:			Optional[Any]=24					,A		:			List[str]=0.0					,A		:			List[str]=16_000					,A		:			int=True					,A		:			str=True					,):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					parent
   UpperCAmelCase__					:  int					   =					batch_size
   UpperCAmelCase__					:  int					   =					min_seq_length
   UpperCAmelCase__					:  Dict					   =					max_seq_length
   UpperCAmelCase__					:  Tuple					   =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   UpperCAmelCase__					:  Dict					   =					feature_size
   UpperCAmelCase__					:  Any					   =					num_mel_bins
   UpperCAmelCase__					:  Any					   =					padding_value
   UpperCAmelCase__					:  Any					   =					sampling_rate
   UpperCAmelCase__					:  List[str]					   =					return_attention_mask
   UpperCAmelCase__					:  List[str]					   =					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		:			Dict					,A		:			Optional[Any]=False					,A		:			Tuple=False    ):






   '''simple docstring'''
   def _flatten(A		:			Any    ):
    return list(itertools.chain(*A    )    )

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







@require_torch
@require_torchaudio
class      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       SpeechaTextFeatureExtractor if is_speech_available() else None

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					SpeechaTextFeatureExtractionTester(self    )

  def    __lowercase       (  self		:			List[str]					,A		:			int    ):






   '''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    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   # Tests that all call wrap to encode_plus and batch_encode_plus
   UpperCAmelCase__					:  Dict					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   # create three inputs of length 800, 1000, and 1200
   UpperCAmelCase__					:  Union[str, Any]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
   UpperCAmelCase__					:  Optional[int]					   =					[np.asarray(A    ) for speech_input in speech_inputs]

   # Test feature size
   UpperCAmelCase__					:  Optional[int]					   =					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__					:  List[Any]					   =					feature_extractor(speech_inputs[0]					,return_tensors="""np"""    ).input_features
   UpperCAmelCase__					:  int					   =					feature_extractor(np_speech_inputs[0]					,return_tensors="""np"""    ).input_features
   self.assertTrue(np.allclose(A					,A					,atol=1e-3    )    )

   # Test batched
   UpperCAmelCase__					:  int					   =					feature_extractor(A					,return_tensors="""np"""    ).input_features
   UpperCAmelCase__					:  Optional[int]					   =					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__					:  Optional[Any]					   =					[floats_list((1, x)    )[0] for x in (800, 800, 800)]
   UpperCAmelCase__					:  Optional[Any]					   =					np.asarray(A    )
   UpperCAmelCase__					:  List[str]					   =					feature_extractor(A					,return_tensors="""np"""    ).input_features
   UpperCAmelCase__					:  List[str]					   =					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    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   UpperCAmelCase__					:  Dict					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]

   UpperCAmelCase__					:  Dict					   =					["""longest""", """max_length""", """do_not_pad"""]
   UpperCAmelCase__					:  List[Any]					   =					[None, 16, None]
   for max_length, padding in zip(A					,A    ):
    UpperCAmelCase__					:  int					   =					feature_extractor(
        A					,padding=A					,max_length=A					,return_attention_mask=A    )
    UpperCAmelCase__					:  List[Any]					   =					inputs.input_features
    UpperCAmelCase__					:  Any					   =					inputs.attention_mask
    UpperCAmelCase__					:  int					   =					[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    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   UpperCAmelCase__					:  int					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]

   UpperCAmelCase__					:  str					   =					["""longest""", """max_length""", """do_not_pad"""]
   UpperCAmelCase__					:  List[str]					   =					[None, 16, None]
   for max_length, padding in zip(A					,A    ):
    UpperCAmelCase__					:  int					   =					feature_extractor(
        A					,max_length=A					,padding=A					,return_tensors="""np"""					,return_attention_mask=A    )
    UpperCAmelCase__					:  int					   =					inputs.input_features
    UpperCAmelCase__					:  Optional[int]					   =					inputs.attention_mask
    UpperCAmelCase__					:  Union[str, Any]					   =					[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    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   UpperCAmelCase__					:  List[Any]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
   UpperCAmelCase__					:  Union[str, Any]					   =					feature_extractor(
       A					,padding="""max_length"""					,max_length=4					,truncation=A					,return_tensors="""np"""					,return_attention_mask=A					,)
   UpperCAmelCase__					:  Optional[Any]					   =					inputs.input_features
   UpperCAmelCase__					:  Optional[Any]					   =					inputs.attention_mask
   UpperCAmelCase__					:  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]    )
   self._check_zero_mean_unit_variance(input_features[2]    )

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   UpperCAmelCase__					:  Optional[int]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
   UpperCAmelCase__					:  Any					   =					feature_extractor(
       A					,padding="""longest"""					,max_length=4					,truncation=A					,return_tensors="""np"""					,return_attention_mask=A					,)
   UpperCAmelCase__					:  str					   =					inputs.input_features
   UpperCAmelCase__					:  Union[str, Any]					   =					inputs.attention_mask
   UpperCAmelCase__					:  Tuple					   =					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__					:  Optional[Any]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
   UpperCAmelCase__					:  Tuple					   =					feature_extractor(
       A					,padding="""longest"""					,max_length=16					,truncation=A					,return_tensors="""np"""					,return_attention_mask=A					,)
   UpperCAmelCase__					:  List[str]					   =					inputs.input_features
   UpperCAmelCase__					:  Optional[Any]					   =					inputs.attention_mask
   UpperCAmelCase__					:  Optional[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, : 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    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   import torch

   UpperCAmelCase__					:  Tuple					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   UpperCAmelCase__					:  Optional[int]					   =					np.random.rand(100					,32    ).astype(np.floataa    )
   UpperCAmelCase__					:  int					   =					np_speech_inputs.tolist()

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

  def    __lowercase       (  self		:			Dict					,A		:			str    ):






   '''simple docstring'''
   from datasets import load_dataset

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

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




  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   # fmt: off
   UpperCAmelCase__					:  List[Any]					   =					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__					:  Tuple					   =					self._load_datasamples(1    )
   UpperCAmelCase__					:  str					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   UpperCAmelCase__					:  int					   =					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   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					list(range(len(__UpperCamelCase      )      )      )
 UpperCAmelCase__					:  Union[str, Any]					   =					[v / w for v, w in zip(__UpperCamelCase							,    __UpperCamelCase      )]
 index.sort(key=lambda __UpperCamelCase      : ratio[i]							,    reverse=__UpperCamelCase      )

 UpperCAmelCase__					:  float					   =					0
 UpperCAmelCase__					:  list[float]					   =					[0] * len(__UpperCamelCase      )
 for i in index:
  if weight[i] <= capacity:
   UpperCAmelCase__					:  Optional[Any]					   =					1
   max_value += value[i]
   capacity -= weight[i]
  else:
   UpperCAmelCase__					:  Union[str, Any]					   =					capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
      import doctest

      doctest.testmod()





