
"""simple docstring"""

import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch


UpperCamelCase      :							str          =					random.Random()






def 				A		(							snake_case :List[Any]							,   snake_case :Union[str, Any]=1.0							,   snake_case :List[Any]=None							,   snake_case :str=None						) ->      Dict:






 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
class __lowerCAmelCase						( unittest.TestCase     ):





     def __init__(      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=7      ,	__UpperCAmelCase=400      ,	__UpperCAmelCase=2000      ,	__UpperCAmelCase=1      ,	__UpperCAmelCase=0.0      ,	__UpperCAmelCase=2_4000      ,	__UpperCAmelCase=True      ,	):

      '''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							    =			padding_value
      __UpperCamelCase							    =			sampling_rate
      __UpperCamelCase							    =			return_attention_mask





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase=False      ,	__UpperCAmelCase=False ):

      '''simple docstring'''




      def _flatten(__UpperCAmelCase ):
       return list(itertools.chain(*_lowerCamelCase ) )

      if equal_length:
       __UpperCamelCase							    =			floats_list((self.batch_size, self.max_seq_length) )
      else:
       # make sure that inputs increase in size
       __UpperCamelCase							    =			[
           _flatten(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(_lowerCamelCase ) for x in audio_inputs]

      return audio_inputs


@require_torch
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		EncodecFeatureExtractor





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			EnCodecFeatureExtractionTester(self )





     def 		UpperCAmelCase   (      self ):

      '''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(_lowerCamelCase ) for audio_input in audio_inputs]

      # Test not batched input
      __UpperCamelCase							    =			feat_extract(audio_inputs[0]      ,	return_tensors='np' ).input_values
      __UpperCamelCase							    =			feat_extract(np_audio_inputs[0]      ,	return_tensors='np' ).input_values
      self.assertTrue(np.allclose(_lowerCamelCase      ,	_lowerCamelCase      ,	atol=1E-3 ) )

      # Test batched
      __UpperCamelCase							    =			feat_extract(_lowerCamelCase      ,	padding=_lowerCamelCase      ,	return_tensors='np' ).input_values
      __UpperCamelCase							    =			feat_extract(_lowerCamelCase      ,	padding=_lowerCamelCase      ,	return_tensors='np' ).input_values
      for enc_seq_a, enc_seq_a in zip(_lowerCamelCase      ,	_lowerCamelCase ):
       self.assertTrue(np.allclose(_lowerCamelCase      ,	_lowerCamelCase      ,	atol=1E-3 ) )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      __UpperCamelCase							    =			np.random.rand(100 ).astype(np.floataa )
      __UpperCamelCase							    =			np_audio_inputs.tolist()

      for inputs in [py_audio_inputs, np_audio_inputs]:
       __UpperCamelCase							    =			feature_extractor.pad([{'input_values': inputs}]      ,	return_tensors='np' )
       self.assertTrue(np_processed.input_values.dtype == np.floataa )
       __UpperCamelCase							    =			feature_extractor.pad([{'input_values': inputs}]      ,	return_tensors='pt' )
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''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(_lowerCamelCase ) )[:num_samples]["audio"]

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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			torch.tensor(
          [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
           3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
           2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
           4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
           7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
           4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
      # fmt: on
      __UpperCamelCase							    =			self._load_datasamples(1 )
      __UpperCamelCase							    =			EncodecFeatureExtractor()
      __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(input_values.shape      ,	(1, 1, 9_3680) )
      self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,	_lowerCamelCase      ,	atol=1E-6 ) )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			torch.tensor(
          [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
           3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
           2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
           4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
           7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
           4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
      # fmt: on
      __UpperCamelCase							    =			self._load_datasamples(1 )
      __UpperCamelCase							    =			[np.tile(input_audio[0][None]      ,	reps=(2, 1) )]
      input_audio[0][1] *= 0.5
      __UpperCamelCase							    =			EncodecFeatureExtractor(feature_size=2 )
      __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(input_values.shape      ,	(1, 2, 9_3680) )
      self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,	_lowerCamelCase      ,	atol=1E-6 ) )
      self.assertTrue(torch.allclose(input_values[0, 1, :30]      ,	EXPECTED_INPUT_VALUES * 0.5      ,	atol=1E-6 ) )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self._load_datasamples(2 )
      # would be easier if the stride was like
      __UpperCamelCase							    =			EncodecFeatureExtractor(feature_size=1      ,	chunk_length_s=1      ,	overlap=0.0_1 )

      # pad and trunc raise an error ?
      with self.assertRaisesRegex(
          _lowerCamelCase      ,	'^Both padding and truncation were set. Make sure you only set one.$'      ,	):
       __UpperCamelCase							    =			feature_extractor(
           _lowerCamelCase      ,	padding='max_length'      ,	truncation=_lowerCamelCase      ,	return_tensors='pt' ).input_values

      # truncate to chunk
      __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	truncation=_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(truncated_outputs.shape      ,	(2, 1, 7_1520) )  # 2 chunks

      # force truncate to max_length
      __UpperCamelCase							    =			feature_extractor(
          _lowerCamelCase      ,	truncation=_lowerCamelCase      ,	max_length=4_8000      ,	return_tensors='pt' ).input_values
      self.assertEquals(truncated_outputs.shape      ,	(2, 1, 4_8000) )

      # pad to chunk
      __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	padding=_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(padded_outputs.shape      ,	(2, 1, 9_5280) )

      # pad to chunk
      __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(truncated_outputs.shape      ,	(2, 1, 9_5280) )

      # force pad to max length
      __UpperCamelCase							    =			feature_extractor(
          _lowerCamelCase      ,	padding='max_length'      ,	max_length=10_0000      ,	return_tensors='pt' ).input_values
      self.assertEquals(truncated_outputs.shape      ,	(2, 1, 10_0000) )

      # force no pad
      with self.assertRaisesRegex(
          _lowerCamelCase      ,	'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,	):
       __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	padding=_lowerCamelCase      ,	return_tensors='pt' ).input_values

      __UpperCamelCase							    =			feature_extractor(input_audio[0]      ,	padding=_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(truncated_outputs.shape      ,	(1, 1, 9_3680) )

      # no pad if no chunk_length_s
      __UpperCamelCase							    =			None
      with self.assertRaisesRegex(
          _lowerCamelCase      ,	'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,	):
       __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	padding=_lowerCamelCase      ,	return_tensors='pt' ).input_values

      __UpperCamelCase							    =			feature_extractor(input_audio[0]      ,	padding=_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(truncated_outputs.shape      ,	(1, 1, 9_3680) )

      # no pad if no overlap
      __UpperCamelCase							    =			2
      __UpperCamelCase							    =			None
      with self.assertRaisesRegex(
          _lowerCamelCase      ,	'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,	):
       __UpperCamelCase							    =			feature_extractor(_lowerCamelCase      ,	padding=_lowerCamelCase      ,	return_tensors='pt' ).input_values

      __UpperCamelCase							    =			feature_extractor(input_audio[0]      ,	padding=_lowerCamelCase      ,	return_tensors='pt' ).input_values
      self.assertEquals(truncated_outputs.shape      ,	(1, 1, 9_3680) )





import math




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      list[int]:

							"""simple docstring"""






							if not isinstance(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			Optional[Any]	       =		F"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE  )
							if number < 1:
														_UpperCAmelCase  :			Dict	       =		F"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			List[Any]	       =		[number]
							while number != 1:
														if number % 2 == 0:
																					_UpperCAmelCase  :			List[str]	       =		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														else:
																					_UpperCAmelCase  :			Dict	       =		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														sequence.append(_SCREAMING_SNAKE_CASE  )
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()




