



'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


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

if is_torch_available():
     import torch
def    lowerCamelCase__ (		_A				,    _A=1.0				,    _A=None				,    _A=None       ):
  if rng is None:
    a		:      str       =  global_rng

  a		:      Any       =  []
  for batch_idx in range(shape[0]       ):
    values.append([]       )
    for _ in range(shape[1]       ):
      values[-1].append(rng.random() * scale       )

  return values

class 							a__(      unittest.TestCase			):







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						List[str]		,    __snake_case				:						Dict=7		,    __snake_case				:						str=4_00		,    __snake_case				:						Union[str, Any]=20_00		,    __snake_case				:						Optional[int]=1		,    __snake_case				:						Dict=0.0		,    __snake_case				:						Union[str, Any]=1_60_00		,    __snake_case				:						Any=True		,    __snake_case				:						Any=True		,    ):
      a		:      Optional[Any]       =  parent
      a		:      List[str]       =  batch_size
      a		:      Optional[int]       =  min_seq_length
      a		:      Tuple       =  max_seq_length
      a		:      Union[str, Any]       =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      a		:      List[str]       =  feature_size
      a		:      List[str]       =  padding_value
      a		:      List[Any]       =  sampling_rate
      a		:      int       =  return_attention_mask
      a		:      Optional[Any]       =  do_normalize







    def     lowercase_ (   self				:						List[str]			):
      return {
          "feature_size": self.feature_size,
          "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				:						Optional[int]		,    __snake_case				:						Tuple=False		,    __snake_case				:						int=False			):
      def _flatten(__snake_case				:						List[Any]			):
        return list(itertools.chain(*__snake_case			)			)

      if equal_length:
        a		:      List[str]       =  floats_list((self.batch_size, self.max_seq_length)			)
      else:
        # make sure that inputs increase in size
        a		:      str       =  [
            _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:
        a		:      Optional[Any]       =  [np.asarray(__snake_case			) for x in speech_inputs]

      return speech_inputs

@require_torch
@require_torchaudio
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    _a				=					ASTFeatureExtractor







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      str       =  ASTFeatureExtractionTester(self			)







    def     lowercase_ (   self				:						int			):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      a		:      List[str]       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      a		:      List[str]       =  [floats_list((1, x)			)[0] for x in range(8_00		,    14_00		,    2_00			)]
      a		:      int       =  [np.asarray(__snake_case			) for speech_input in speech_inputs]

      # Test not batched input
      a		:      int       =  feat_extract(speech_inputs[0]		,    return_tensors='np'			).input_values
      a		:      Tuple       =  feat_extract(np_speech_inputs[0]		,    return_tensors='np'			).input_values
      self.assertTrue(np.allclose(__snake_case		,    __snake_case		,    atol=1e-3			)			)

      # Test batched
      a		:      Dict       =  feat_extract(__snake_case		,    padding=__snake_case		,    return_tensors='np'			).input_values
      a		:      Union[str, Any]       =  feat_extract(__snake_case		,    padding=__snake_case		,    return_tensors='np'			).input_values
      for enc_seq_a, enc_seq_a in zip(__snake_case		,    __snake_case			):
        self.assertTrue(np.allclose(__snake_case		,    __snake_case		,    atol=1e-3			)			)

      # Test 2-D numpy arrays are batched.
      a		:      Any       =  [floats_list((1, x)			)[0] for x in (8_00, 8_00, 8_00)]
      a		:      List[Any]       =  np.asarray(__snake_case			)
      a		:      Dict       =  feat_extract(__snake_case		,    return_tensors='np'			).input_values
      a		:      Union[str, Any]       =  feat_extract(__snake_case		,    return_tensors='np'			).input_values
      for enc_seq_a, enc_seq_a in zip(__snake_case		,    __snake_case			):
        self.assertTrue(np.allclose(__snake_case		,    __snake_case		,    atol=1e-3			)			)







    @require_torch
    def     lowercase_ (   self				:						Optional[Any]			):
      import torch

      a		:      Optional[int]       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      a		:      Union[str, Any]       =  np.random.rand(1_00			).astype(np.floataa			)
      a		:      Union[str, Any]       =  np_speech_inputs.tolist()

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







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Optional[Any]			):
      from datasets import load_dataset

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

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







    @require_torch
    def     lowercase_ (   self				:						Optional[int]			):
      # fmt: off
      a		:      int       =  torch.tensor(
          [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
           -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
           -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
           -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]			)
      # fmt: on

      a		:      Union[str, Any]       =  self._load_datasamples(1			)
      a		:      Union[str, Any]       =  ASTFeatureExtractor()
      a		:      Any       =  feature_extractor(__snake_case		,    return_tensors='pt'			).input_values
      self.assertEquals(input_values.shape		,    (1, 10_24, 1_28)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :30]		,    __snake_case		,    atol=1e-4			)			)




'''simple docstring'''






def    lowerCamelCase__ (		_A = 6008_5147_5143       ):

  try:
    a		:      Optional[int]       =  int(_A       )
  except (TypeError, ValueError):
    raise TypeError('Parameter n must be int or castable to int.'       )
  if n <= 0:
    raise ValueError('Parameter n must be greater than or equal to one.'       )
  a		:      Any       =  1
  a		:      Union[str, Any]       =  2
  while i * i <= n:
    while n % i == 0:
      a		:      str       =  i
      n //= i
    i += 1
  if n > 1:
    a		:      Any       =  n
  return int(_A       )


if __name__ == "__main__":
     print(F"{solution() = }")