




from math import sqrt







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (
          number >= 0
      ), "'number' must been an int and positive"

      _A							:     Optional[Any]			=						True

      # 0 and 1 are none primes.
      if number <= 1:
            _A							:     Tuple			=						False

      for divisor in range(2,int(round(sqrt(snake_case_     )     )     ) + 1     ):
            # if 'number' divisible by 'divisor' then sets 'status'
            # of false and break up the loop.
            if number % divisor == 0:
                  _A							:     Optional[Any]			=						False
                  break

    # precondition
      assert isinstance(snake_case_,snake_case_     ), "'status' must been from type bool"

      return status







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (n > 2), "'N' must been an int and > 2"

      # beginList: contains all natural numbers from 2 up to N
      _A							:     Optional[Any]			=						list(range(2,n + 1     )     )

      _A							:     List[Any]			=						[]  # this list will be returns.

      # actual sieve of erathostenes
      for i in range(len(snake_case_     )     ):
            for j in range(i + 1,len(snake_case_     )     ):
                  if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                        _A							:     int			=						0

    # filters actual prime numbers.
      _A							:     Union[str, Any]			=						[x for x in begin_list if x != 0]

      # precondition
      assert isinstance(snake_case_,snake_case_     ), "'ans' must been from type list"

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (n > 2), "'N' must been an int and > 2"

      _A							:     int			=						[]

      # iterates over all numbers between 2 up to N+1
      # if a number is prime then appends to list 'ans'
      for number in range(2,n + 1     ):
            if is_prime(snake_case_     ):
                  ans.append(snake_case_     )

    # precondition
      assert isinstance(snake_case_,snake_case_     ), "'ans' must been from type list"

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and number >= 0, "'number' must been an int and >= 0"

      _A							:     Optional[int]			=						[]  # this list will be returns of the function.

      # potential prime number factors.

      _A							:     List[Any]			=						2

      _A							:     Dict			=						number

      if number == 0 or number == 1:
            ans.append(snake_case_     )

      # if 'number' not prime then builds the prime factorization of 'number'
      elif not is_prime(snake_case_     ):
            while quotient != 1:
                  if is_prime(snake_case_     ) and (quotient % factor == 0):
                        ans.append(snake_case_     )
                        quotient /= factor
                  else:
                        factor += 1

      else:
            ans.append(snake_case_     )

      # precondition
      assert isinstance(snake_case_,snake_case_     ), "'ans' must been from type list"

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (
          number >= 0
      ), "'number' bust been an int and >= 0"

      _A							:     Union[str, Any]			=						0

      # prime factorization of 'number'
      _A							:     Tuple			=						prime_factorization(snake_case_     )

      _A							:     Tuple			=						max(snake_case_     )

      # precondition
      assert isinstance(snake_case_,snake_case_     ), "'ans' must been from type int"

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (
          number >= 0
      ), "'number' bust been an int and >= 0"

      _A							:     Tuple			=						0

      # prime factorization of 'number'
      _A							:     Union[str, Any]			=						prime_factorization(snake_case_     )

      _A							:     Union[str, Any]			=						min(snake_case_     )

      # precondition
      assert isinstance(snake_case_,snake_case_     ), "'ans' must been from type int"

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ), "'number' must been an int"
      assert isinstance(number % 2 == 0,snake_case_     ), "compare bust been from type bool"

      return number % 2 == 0







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ), "'number' must been an int"
      assert isinstance(number % 2 != 0,snake_case_     ), "compare bust been from type bool"

      return number % 2 != 0







def 				lowerCAmelCase_	(     snake_case_     ):
      assert (
          isinstance(snake_case_,snake_case_     ) and (number > 2) and is_even(snake_case_     )
      ), "'number' must been an int, even and > 2"

      _A							:     Dict			=						[]  # this list will returned

      # creates a list of prime numbers between 2 up to 'number'
      _A							:     Union[str, Any]			=						get_prime_numbers(snake_case_     )
      _A							:     Union[str, Any]			=						len(snake_case_     )

      # run variable for while-loops.
      _A							:     Dict			=						0
      _A							:     Union[str, Any]			=						None

      # exit variable. for break up the loops
      _A							:     str			=						True

      while i < len_pn and loop:
            _A							:     Union[str, Any]			=						i + 1

            while j < len_pn and loop:
                  if prime_numbers[i] + prime_numbers[j] == number:
                        _A							:     List[Any]			=						False
                        ans.append(prime_numbers[i]     )
                        ans.append(prime_numbers[j]     )

                  j += 1

            i += 1

      # precondition
      assert (
          isinstance(snake_case_,snake_case_     )
          and (len(snake_case_     ) == 2)
          and (ans[0] + ans[1] == number)
          and is_prime(ans[0]     )
          and is_prime(ans[1]     )
      ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

      return ans







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      assert (
          isinstance(snake_case_,snake_case_     )
          and isinstance(snake_case_,snake_case_     )
          and (numbera >= 0)
          and (numbera >= 0)
      ), "'number1' and 'number2' must been positive integer."

      _A							:     Optional[Any]			=						0

      while numbera != 0:
            _A							:     List[Any]			=						numbera % numbera
            _A							:     List[Any]			=						numbera
            _A							:     Dict			=						rest

      # precondition
      assert isinstance(snake_case_,snake_case_     ) and (
          numbera >= 0
      ), "'number' must been from type int and positive"

      return numbera







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      assert (
          isinstance(snake_case_,snake_case_     )
          and isinstance(snake_case_,snake_case_     )
          and (numbera >= 1)
          and (numbera >= 1)
      ), "'number1' and 'number2' must been positive integer."

      _A							:     Optional[int]			=						1  # actual answer that will be return.

      # for kgV (x,1)
      if numbera > 1 and numbera > 1:
            # builds the prime factorization of 'number1' and 'number2'
            _A							:     Tuple			=						prime_factorization(snake_case_     )
            _A							:     Dict			=						prime_factorization(snake_case_     )

      elif numbera == 1 or numbera == 1:
            _A							:     Optional[Any]			=						[]
            _A							:     List[str]			=						[]
            _A							:     Union[str, Any]			=						max(snake_case_,snake_case_     )

      _A							:     Tuple			=						0
      _A							:     Tuple			=						0

      _A							:     Optional[int]			=						[]  # captured numbers int both 'primeFac1' and 'primeFac2'

      # iterates through primeFac1
      for n in prime_fac_a:
            if n not in done:
                  if n in prime_fac_a:
                        _A							:     int			=						prime_fac_a.count(snake_case_     )
                        _A							:     Union[str, Any]			=						prime_fac_a.count(snake_case_     )

                        for _ in range(max(snake_case_,snake_case_     )     ):
                              ans *= n

                  else:
                        _A							:     Union[str, Any]			=						prime_fac_a.count(snake_case_     )

                        for _ in range(snake_case_     ):
                              ans *= n

                  done.append(snake_case_     )

    # iterates through primeFac2
      for n in prime_fac_a:
            if n not in done:
                  _A							:     int			=						prime_fac_a.count(snake_case_     )

                  for _ in range(snake_case_     ):
                        ans *= n

                  done.append(snake_case_     )

    # precondition
      assert isinstance(snake_case_,snake_case_     ) and (
          ans >= 0
      ), "'ans' must been from type int and positive"

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (n >= 0), "'number' must been a positive int"

      _A							:     Union[str, Any]			=						0
      _A							:     Optional[int]			=						2  # this variable holds the answer

      while index < n:
            index += 1

            ans += 1  # counts to the next number

            # if ans not prime then
            # runs to the next prime number.
            while not is_prime(snake_case_     ):
                  ans += 1

    # precondition
      assert isinstance(snake_case_,snake_case_     ) and is_prime(
          snake_case_     ), "'ans' must been a prime number and from type int"

      return ans







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      assert (
          is_prime(snake_case_     ) and is_prime(snake_case_     ) and (p_number_a < p_number_a)
      ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

      _A							:     str			=						p_number_a + 1  # jump to the next number

      _A							:     Tuple			=						[]  # this list will be returns.

      # if number is not prime then
      # fetch the next prime number.
      while not is_prime(snake_case_     ):
            number += 1

      while number < p_number_a:
            ans.append(snake_case_     )

            number += 1

            # fetch the next prime number.
            while not is_prime(snake_case_     ):
                  number += 1

    # precondition
      assert (
          isinstance(snake_case_,snake_case_     )
          and ans[0] != p_number_a
          and ans[len(snake_case_     ) - 1] != p_number_a
      ), "'ans' must been a list without the arguments"

      # 'ans' contains not 'pNumber1' and 'pNumber2' !
      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (n >= 1), "'n' must been int and >= 1"

      _A							:     Optional[int]			=						[]  # will be returned.

      for divisor in range(1,n + 1     ):
            if n % divisor == 0:
                  ans.append(snake_case_     )

    # precondition
      assert ans[0] == 1 and ans[len(snake_case_     ) - 1] == n, "Error in function getDivisiors(...)"

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (
          number > 1
      ), "'number' must been an int and >= 1"

      _A							:     Optional[Any]			=						get_divisors(snake_case_     )

      # precondition
      assert (
          isinstance(snake_case_,snake_case_     )
          and (divisors[0] == 1)
          and (divisors[len(snake_case_     ) - 1] == number)
      ), "Error in help-function getDivisiors(...)"

      # summed all divisors up to 'number' (exclusive), hence [:-1]
      return sum(divisors[:-1]     ) == number







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      assert (
          isinstance(snake_case_,snake_case_     )
          and isinstance(snake_case_,snake_case_     )
          and (denominator != 0)
      ), "The arguments must been from type int and 'denominator' != 0"

      # build the greatest common divisor of numerator and denominator.
      _A							:     Union[str, Any]			=						gcd(abs(snake_case_     ),abs(snake_case_     )     )

      # precondition
      assert (
          isinstance(snake_case_,snake_case_     )
          and (numerator % gcd_of_fraction == 0)
          and (denominator % gcd_of_fraction == 0)
      ), "Error in function gcd(...,...)"

      return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (n >= 0), "'n' must been a int and >= 0"

      _A							:     List[Any]			=						1  # this will be return.

      for factor in range(1,n + 1     ):
            ans *= factor

      return ans







def 				lowerCAmelCase_	(     snake_case_     ):
      assert isinstance(snake_case_,snake_case_     ) and (n >= 0), "'n' must been an int and >= 0"

      _A							:     Any			=						0
      _A							:     Tuple			=						1
      _A							:     Dict			=						1  # this will be return

      for _ in range(n - 1     ):
            _A							:     Optional[int]			=						ans
            ans += fiba
            _A							:     Optional[int]			=						tmp

      return ans






import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


a_   :Any        =      random.Random()
def        lowercase_							(A : int		,					A : Union[str, Any]=1.0		,					A : List[str]=None		,					A : Any=None      ):
       if rng is None:
              snake_case__							:	List[str]   	=	global_rng

       snake_case__							:	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
class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], _snake_case  :  List[str], _snake_case  :  Tuple=7, _snake_case  :  Union[str, Any]=4_0_0, _snake_case  :  Any=2_0_0_0, _snake_case  :  Dict=1, _snake_case  :  Optional[Any]=0.0, _snake_case  :  List[Any]=1_6_0_0_0, _snake_case  :  List[Any]=True, _snake_case  :  List[Any]=8_0, _snake_case  :  Dict=1_6, _snake_case  :  str=6_4, _snake_case  :  Tuple="hann_window", _snake_case  :  Union[str, Any]=8_0, _snake_case  :  Optional[Any]=7_6_0_0, _snake_case  :  str=1e-10, _snake_case  :  Any=True, )   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	parent
            snake_case__							:	Optional[Any]   	=	batch_size
            snake_case__							:	List[Any]   	=	min_seq_length
            snake_case__							:	List[Any]   	=	max_seq_length
            snake_case__							:	Any   	=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            snake_case__							:	Tuple   	=	feature_size
            snake_case__							:	List[Any]   	=	padding_value
            snake_case__							:	Any   	=	sampling_rate
            snake_case__							:	Dict   	=	do_normalize
            snake_case__							:	Union[str, Any]   	=	num_mel_bins
            snake_case__							:	Any   	=	hop_length
            snake_case__							:	Any   	=	win_length
            snake_case__							:	Any   	=	win_function
            snake_case__							:	Optional[int]   	=	fmin
            snake_case__							:	int   	=	fmax
            snake_case__							:	Union[str, Any]   	=	mel_floor
            snake_case__							:	Union[str, Any]   	=	return_attention_mask






     def    lowercase_   (       self  :  Optional[int]					)   ->List[str]:
            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "do_normalize": self.do_normalize,
                "num_mel_bins": self.num_mel_bins,
                "hop_length": self.hop_length,
                "win_length": self.win_length,
                "win_function": self.win_function,
                "fmin": self.fmin,
                "fmax": self.fmax,
                "mel_floor": self.mel_floor,
                "return_attention_mask": self.return_attention_mask,
            }






     def    lowercase_   (       self  :  Any, _snake_case  :  Optional[Any]=False, _snake_case  :  List[str]=False					)   ->Union[str, Any]:
            def _flatten(_snake_case  :  List[str]					):
                   return list(itertools.chain(*_snake_case					)					)

            if equal_length:
                   snake_case__							:	Any   	=	floats_list((self.batch_size, self.max_seq_length)					)
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	int   	=	[
                       _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:
                   snake_case__							:	Any   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str=False, _snake_case  :  Dict=False					)   ->List[str]:
            if equal_length:
                   snake_case__							:	Optional[Any]   	=	[floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	List[str]   	=	[
                       floats_list((x, self.num_mel_bins)					)
                       for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff					)
                   ]

            if numpify:
                   snake_case__							:	int   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs




@require_torch
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	SpeechTaFeatureExtractor






     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	List[str]   	=	SpeechTaFeatureExtractionTester(self					)






     def    lowercase_   (       self  :  Any, _snake_case  :  Dict					)   ->Any:
            self.assertTrue(np.all(np.mean(_snake_case, axis=0					) < 1e-3					)					)
            self.assertTrue(np.all(np.abs(np.var(_snake_case, axis=0					) - 1					) < 1e-3					)					)






     def    lowercase_   (       self  :  List[Any]					)   ->Union[str, Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	int   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Tuple   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test not batched input
            snake_case__							:	str   	=	feat_extract(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	List[str]   	=	feat_extract(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Any   	=	feat_extract(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	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					)					)






     def    lowercase_   (       self  :  int					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]

            snake_case__							:	int   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	List[str]   	=	[None, 1_6_0_0, None]
            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	Optional[int]   	=	feat_extract(_snake_case, padding=_snake_case, max_length=_snake_case, return_tensors='np'					)
                   snake_case__							:	Optional[int]   	=	processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
                   self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6					)
                   self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
                   self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6					)
                   self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[Any]:
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	range(8_0_0, 1_4_0_0, 2_0_0					)
            snake_case__							:	Optional[Any]   	=	[floats_list((1, x)					)[0] for x in lengths]

            snake_case__							:	Union[str, Any]   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	str   	=	[None, 1_6_0_0, None]

            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	List[str]   	=	feat_extract(_snake_case, max_length=_snake_case, padding=_snake_case					)
                   snake_case__							:	Tuple   	=	processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
                   self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
                   self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)






     def    lowercase_   (       self  :  List[Any]					)   ->Optional[Any]:
            snake_case__							:	Any   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	str   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Optional[Any]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='max_length', return_tensors='np'					)
            snake_case__							:	int   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1]					)
            self._check_zero_mean_unit_variance(input_values[2]					)






     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Dict   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	str   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	Dict   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
            self._check_zero_mean_unit_variance(input_values[2]					)

            # make sure that if max_length < longest -> then pad to max_length
            self.assertTrue(input_values.shape == (3, 1_0_0_0)					)

            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	List[str]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=2_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	Optional[Any]   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
            self._check_zero_mean_unit_variance(input_values[2]					)

            # make sure that if max_length > longest -> then pad to longest
            self.assertTrue(input_values.shape == (3, 1_2_0_0)					)






     def    lowercase_   (       self  :  List[str]					)   ->Dict:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	List[Any]   	=	np.random.rand(1_0_0					).astype(np.floataa					)
            snake_case__							:	int   	=	np_speech_inputs.tolist()

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






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	List[Any]   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Dict   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test feature size
            snake_case__							:	Optional[int]   	=	feature_extractor(audio_target=_snake_case, padding=_snake_case, return_tensors='np'					).input_values
            self.assertTrue(input_values.ndim == 3					)
            self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins					)

            # Test not batched input
            snake_case__							:	Dict   	=	feature_extractor(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	Any   	=	feature_extractor(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Dict   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Dict   	=	feature_extractor(_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.
            snake_case__							:	Optional[Any]   	=	[floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
            snake_case__							:	int   	=	np.asarray(_snake_case					)
            snake_case__							:	Union[str, Any]   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Union[str, Any]   	=	feature_extractor(_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					)					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[str]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Tuple   	=	BatchFeature({input_name: speech_inputs}					)

            self.assertTrue(all(len(_snake_case					) == len(_snake_case					) for x, y in zip(_snake_case, processed_features[input_name]					)					)					)

            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	BatchFeature({input_name: speech_inputs}, tensor_type='np'					)

            snake_case__							:	Dict   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	List[str]   	=	batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)






     @require_torch
     def    lowercase_   (       self  :  List[str]					)   ->Any:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Tuple   	=	feat_extract.model_input_names[0]

            snake_case__							:	List[Any]   	=	BatchFeature({input_name: speech_inputs}, tensor_type='pt'					)

            snake_case__							:	Tuple   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	Any   	=	batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)






     @require_torch
     def    lowercase_   (       self  :  Optional[int]					)   ->Tuple:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Union[str, Any]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	List[str]   	=	BatchFeature({input_name: speech_inputs}					)

            snake_case__							:	int   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='np'					)[input_name]
            snake_case__							:	Union[str, Any]   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='pt'					)[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_pt.numpy().astype(np.floataa					).sum()					) < 1e-2					)






     def    lowercase_   (       self  :  int					)   ->Any:
            snake_case__							:	Any   	=	self.feat_extract_dict
            snake_case__							:	List[Any]   	=	True
            snake_case__							:	Union[str, Any]   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	Any   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[Any]   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Union[str, Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Optional[int]   	=	BatchFeature({input_name: speech_inputs}					)

            snake_case__							:	List[str]   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	str   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='np'					)
            self.assertIn('attention_mask', _snake_case					)
            self.assertListEqual(list(processed.attention_mask.shape					), list(processed[input_name].shape[:2]					)					)
            self.assertListEqual(processed.attention_mask.sum(-1					).tolist(), _snake_case					)






     def    lowercase_   (       self  :  Optional[int]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_dict
            snake_case__							:	List[str]   	=	True
            snake_case__							:	Tuple   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	List[str]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	str   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Optional[int]   	=	BatchFeature({input_name: speech_inputs}					)
            snake_case__							:	Optional[Any]   	=	min(_snake_case					)

            snake_case__							:	Union[str, Any]   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(
                _snake_case, padding='max_length', max_length=_snake_case, truncation=_snake_case, return_tensors='np'					)
            self.assertIn('attention_mask', _snake_case					)
            self.assertListEqual(
                list(processed_pad.attention_mask.shape					), [processed_pad[input_name].shape[0], max_length]					)
            self.assertListEqual(
                processed_pad.attention_mask[:, :max_length].sum(-1					).tolist(), [max_length for x in speech_inputs]					)






     def    lowercase_   (       self  :  List[Any], _snake_case  :  Optional[int]					)   ->Optional[Any]:
            from datasets import load_dataset

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

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






     def    lowercase_   (       self  :  str					)   ->str:
            # fmt: off
            snake_case__							:	List[Any]   	=	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

            snake_case__							:	Union[str, Any]   	=	self._load_datasamples(1					)
            snake_case__							:	Optional[int]   	=	SpeechTaFeatureExtractor()
            snake_case__							:	List[Any]   	=	feature_extractor(_snake_case, return_tensors='pt'					).input_values
            self.assertEquals(input_values.shape, (1, 9_3_6_8_0)					)
            self.assertTrue(torch.allclose(input_values[0, :3_0], _snake_case, atol=1e-6					)					)






     def    lowercase_   (       self  :  Any					)   ->str:
            # fmt: off
            snake_case__							:	Optional[Any]   	=	torch.tensor(
                [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
                 -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
                 -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
                 -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]					)
            # fmt: on

            snake_case__							:	List[str]   	=	self._load_datasamples(1					)
            snake_case__							:	str   	=	SpeechTaFeatureExtractor()
            snake_case__							:	Optional[Any]   	=	feature_extractor(audio_target=_snake_case, return_tensors='pt'					).input_values
            self.assertEquals(input_values.shape, (1, 3_6_6, 8_0)					)
            self.assertTrue(torch.allclose(input_values[0, 0, :3_0], _snake_case, atol=1e-4					)					)






