






'''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

__A							:							List[str]  							=		random.Random()






def    lowerCamelCase_     (    lowercase__				,     lowercase__=1.0				,     lowercase__=None				,     lowercase__=None):






	if rng is None:
		lowerCamelCase__  =     global_rng

	lowerCamelCase__  =     []
	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      ):


				'''simple docstring'''

				def __init__(					self     :     Dict				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     str=7				,     __lowerCamelCase     :     Optional[int]=400				,     __lowerCamelCase     :     int=2000				,     __lowerCamelCase     :     Any=24				,     __lowerCamelCase     :     Dict=24				,     __lowerCamelCase     :     Tuple=0.0				,     __lowerCamelCase     :     Union[str, Any]=16000				,     __lowerCamelCase     :     List[Any]=True				,     __lowerCamelCase     :     str=True				,     )							->							int:

					'''simple docstring'''




					lowerCamelCase__  =     parent
					lowerCamelCase__  =     batch_size
					lowerCamelCase__  =     min_seq_length
					lowerCamelCase__  =     max_seq_length
					lowerCamelCase__  =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					lowerCamelCase__  =     feature_size
					lowerCamelCase__  =     num_mel_bins
					lowerCamelCase__  =     padding_value
					lowerCamelCase__  =     sampling_rate
					lowerCamelCase__  =     return_attention_mask
					lowerCamelCase__  =     do_normalize

				def    a__				(					self     :     List[Any]     )							->							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     :     str				,     __lowerCamelCase     :     Union[str, Any]=False				,     __lowerCamelCase     :     int=False     )							->							Tuple:

					'''simple docstring'''




					def _flatten(__lowerCamelCase     :     Tuple     ):
						return list(itertools.chain(*__lowerCamelCase     )     )

					if equal_length:
						lowerCamelCase__  =     [floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
					else:
						# make sure that inputs increase in size
						lowerCamelCase__  =     [
						    floats_list((x, self.feature_size)     )
						    for x in range(self.min_seq_length				,     self.max_seq_length				,     self.seq_length_diff     )
						]
					if numpify:
						lowerCamelCase__  =     [np.asarray(__lowerCamelCase     ) for x in speech_inputs]
					return speech_inputs
@require_torch
@require_torchaudio
class 				lowercase       (							lowercase_ ,       unittest.TestCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							SpeechaTextFeatureExtractor if is_speech_available() else None

				def    a__				(					self     :     Optional[Any]     )							->							Tuple:

					'''simple docstring'''




					lowerCamelCase__  =     SpeechaTextFeatureExtractionTester(self     )

				def    a__				(					self     :     str				,     __lowerCamelCase     :     Dict     )							->							List[Any]:

					'''simple docstring'''




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

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

					'''simple docstring'''




					lowerCamelCase__  =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
					# create three inputs of length 800, 1000, and 1200
					lowerCamelCase__  =     [floats_list((1, x)     )[0] for x in range(800				,     1400				,     200     )]
					lowerCamelCase__  =     [np.asarray(__lowerCamelCase     ) for speech_input in speech_inputs]

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

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

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

				def    a__				(					self     :     Optional[Any]     )							->							Dict:

					'''simple docstring'''




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

					lowerCamelCase__  =     ["longest", "max_length", "do_not_pad"]
					lowerCamelCase__  =     [None, 16, None]
					for max_length, padding in zip(__lowerCamelCase				,     __lowerCamelCase     ):
						lowerCamelCase__  =     feature_extractor(
						    __lowerCamelCase				,     padding=__lowerCamelCase				,     max_length=__lowerCamelCase				,     return_attention_mask=__lowerCamelCase     )
						lowerCamelCase__  =     inputs.input_features
						lowerCamelCase__  =     inputs.attention_mask
						lowerCamelCase__  =     [np.sum(__lowerCamelCase     ) 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     :     Optional[Any]     )							->							Dict:

					'''simple docstring'''




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

					lowerCamelCase__  =     ["longest", "max_length", "do_not_pad"]
					lowerCamelCase__  =     [None, 16, None]
					for max_length, padding in zip(__lowerCamelCase				,     __lowerCamelCase     ):
						lowerCamelCase__  =     feature_extractor(
						    __lowerCamelCase				,     max_length=__lowerCamelCase				,     padding=__lowerCamelCase				,     return_tensors="np"				,     return_attention_mask=__lowerCamelCase     )
						lowerCamelCase__  =     inputs.input_features
						lowerCamelCase__  =     inputs.attention_mask
						lowerCamelCase__  =     [np.sum(__lowerCamelCase     ) 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     :     Any     )							->							Tuple:

					'''simple docstring'''




					lowerCamelCase__  =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
					lowerCamelCase__  =     [floats_list((1, x)     )[0] for x in range(800				,     1400				,     200     )]
					lowerCamelCase__  =     feature_extractor(
					    __lowerCamelCase				,     padding="max_length"				,     max_length=4				,     truncation=__lowerCamelCase				,     return_tensors="np"				,     return_attention_mask=__lowerCamelCase				,     )
					lowerCamelCase__  =     inputs.input_features
					lowerCamelCase__  =     inputs.attention_mask
					lowerCamelCase__  =     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     :     List[Any]     )							->							str:

					'''simple docstring'''




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

					lowerCamelCase__  =     [floats_list((1, x)     )[0] for x in range(800				,     1400				,     200     )]
					lowerCamelCase__  =     feature_extractor(
					    __lowerCamelCase				,     padding="longest"				,     max_length=16				,     truncation=__lowerCamelCase				,     return_tensors="np"				,     return_attention_mask=__lowerCamelCase				,     )
					lowerCamelCase__  =     inputs.input_features
					lowerCamelCase__  =     inputs.attention_mask
					lowerCamelCase__  =     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     :     str     )							->							Dict:

					'''simple docstring'''




					import torch

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

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

				def    a__				(					self     :     str				,     __lowerCamelCase     :     Any     )							->							List[str]:

					'''simple docstring'''




					from datasets import load_dataset

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

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




				def    a__				(					self     :     Optional[int]     )							->							Dict:

					'''simple docstring'''




					lowerCamelCase__  =     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

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









'''simple docstring'''





from __future__ import annotations






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     list(range(len(lowercase__)))
 lowerCamelCase__  =     [v / w for v, w in zip(lowercase__				,     lowercase__)]
 index.sort(key=lambda lowercase__: ratio[i]				,     reverse=lowercase__)

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

 return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()

