



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

__snake_case						:Optional[Any]      		=     random.Random()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase=1.0   , _UpperCAmelCase=None   , _UpperCAmelCase=None				):
    if rng is None:
        __a						       =				global_rng

    __a						       =				[]
    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							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=7     ,		__SCREAMING_SNAKE_CASE							:     int=400     ,		__SCREAMING_SNAKE_CASE							:     Any=2_000     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=24     ,		__SCREAMING_SNAKE_CASE							:     Dict=24     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     int=16_000     ,		__SCREAMING_SNAKE_CASE							:     Tuple=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=True     ,		):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''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 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int=False     ,		__SCREAMING_SNAKE_CASE							:     Dict=False):


           '''simple docstring'''

           def _flatten(__SCREAMING_SNAKE_CASE							:     Dict):
               return list(itertools.chain(*lowerCAmelCase_))

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


@require_torch
@require_torchaudio
class     _A       (	__lowerCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    List[str]											=						SpeechaTextFeatureExtractor if is_speech_available() else None



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				SpeechaTextFeatureExtractionTester(self)



       def 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     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 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           __a						       =				[floats_list((1, x))[0] for x in range(800     ,		1_400     ,		200)]
           __a						       =				[np.asarray(lowerCAmelCase_) for speech_input in speech_inputs]

           # Test feature size
           __a						       =				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
           __a						       =				feature_extractor(speech_inputs[0]     ,		return_tensors='''np''').input_features
           __a						       =				feature_extractor(np_speech_inputs[0]     ,		return_tensors='''np''').input_features
           self.assertTrue(np.allclose(lowerCAmelCase_     ,		lowerCAmelCase_     ,		atol=1E-3))

           # Test batched
           __a						       =				feature_extractor(lowerCAmelCase_     ,		return_tensors='''np''').input_features
           __a						       =				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.
           __a						       =				[floats_list((1, x))[0] for x in (800, 800, 800)]
           __a						       =				np.asarray(lowerCAmelCase_)
           __a						       =				feature_extractor(lowerCAmelCase_     ,		return_tensors='''np''').input_features
           __a						       =				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 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a						       =				[floats_list((1, x))[0] for x in range(800     ,		1_400     ,		200)]

           __a						       =				['''longest''', '''max_length''', '''do_not_pad''']
           __a						       =				[None, 16, None]
           for max_length, padding in zip(lowerCAmelCase_     ,		lowerCAmelCase_):
               __a						       =				feature_extractor(
                   lowerCAmelCase_     ,		padding=lowerCAmelCase_     ,		max_length=lowerCAmelCase_     ,		return_attention_mask=lowerCAmelCase_)
               __a						       =				inputs.input_features
               __a						       =				inputs.attention_mask
               __a						       =				[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 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a						       =				[floats_list((1, x))[0] for x in range(800     ,		1_400     ,		200)]

           __a						       =				['''longest''', '''max_length''', '''do_not_pad''']
           __a						       =				[None, 16, None]
           for max_length, padding in zip(lowerCAmelCase_     ,		lowerCAmelCase_):
               __a						       =				feature_extractor(
                   lowerCAmelCase_     ,		max_length=lowerCAmelCase_     ,		padding=lowerCAmelCase_     ,		return_tensors='''np'''     ,		return_attention_mask=lowerCAmelCase_)
               __a						       =				inputs.input_features
               __a						       =				inputs.attention_mask
               __a						       =				[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 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a						       =				[floats_list((1, x))[0] for x in range(800     ,		1_400     ,		200)]
           __a						       =				feature_extractor(
               lowerCAmelCase_     ,		padding='''max_length'''     ,		max_length=4     ,		truncation=lowerCAmelCase_     ,		return_tensors='''np'''     ,		return_attention_mask=lowerCAmelCase_     ,		)
           __a						       =				inputs.input_features
           __a						       =				inputs.attention_mask
           __a						       =				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 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a						       =				[floats_list((1, x))[0] for x in range(800     ,		1_400     ,		200)]
           __a						       =				feature_extractor(
               lowerCAmelCase_     ,		padding='''longest'''     ,		max_length=4     ,		truncation=lowerCAmelCase_     ,		return_tensors='''np'''     ,		return_attention_mask=lowerCAmelCase_     ,		)
           __a						       =				inputs.input_features
           __a						       =				inputs.attention_mask
           __a						       =				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))

           __a						       =				[floats_list((1, x))[0] for x in range(800     ,		1_400     ,		200)]
           __a						       =				feature_extractor(
               lowerCAmelCase_     ,		padding='''longest'''     ,		max_length=16     ,		truncation=lowerCAmelCase_     ,		return_tensors='''np'''     ,		return_attention_mask=lowerCAmelCase_     ,		)
           __a						       =				inputs.input_features
           __a						       =				inputs.attention_mask
           __a						       =				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 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           import torch

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

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



       def 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           from datasets import load_dataset

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

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





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				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						       =				self._load_datasamples(1)
           __a						       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a						       =				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))






import math
import sys






def  snake_case  (       snake_case__			:int)						->   int:
       if number != int(snake_case__):
              raise ValueError("""the value of input must be a natural number""")
       if number < 0:
              raise ValueError("""the value of input must not be a negative number""")
       if number == 0:
              return 1
       _A        =  [-1] * (number + 1)
       _A        =  0
       for i in range(1   ,	number + 1):
              _A        =  sys.maxsize
              _A        =  int(math.sqrt(snake_case__))
              for j in range(1   ,	root + 1):
                     _A        =  1 + answers[i - (j**2)]
                     _A        =  min(snake_case__   ,	snake_case__)
              _A        =  answer
       return answers[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()




