



import argparse
import struct
import unittest



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Optional[Any]			,				lowerCAmelCase  :			bytes)	->				None:




         """simple docstring"""

         lowercase__     =  data

         # Initialize hash values
         lowercase__     =  [
             0X6_A_0_9_E_6_6_7,
             0XB_B_6_7_A_E_8_5,
             0X3_C_6_E_F_3_7_2,
             0XA_5_4_F_F_5_3_A,
             0X5_1_0_E_5_2_7_F,
             0X9_B_0_5_6_8_8_C,
             0X1_F_8_3_D_9_A_B,
             0X5_B_E_0_C_D_1_9,
         ]

         # Initialize round constants
         lowercase__     =  [
             0X4_2_8_A_2_F_9_8,
             0X7_1_3_7_4_4_9_1,
             0XB_5_C_0_F_B_C_F,
             0XE_9_B_5_D_B_A_5,
             0X3_9_5_6_C_2_5_B,
             0X5_9_F_1_1_1_F_1,
             0X9_2_3_F_8_2_A_4,
             0XA_B_1_C_5_E_D_5,
             0XD_8_0_7_A_A_9_8,
             0X1_2_8_3_5_B_0_1,
             0X2_4_3_1_8_5_B_E,
             0X5_5_0_C_7_D_C_3,
             0X7_2_B_E_5_D_7_4,
             0X8_0_D_E_B_1_F_E,
             0X9_B_D_C_0_6_A_7,
             0XC_1_9_B_F_1_7_4,
             0XE_4_9_B_6_9_C_1,
             0XE_F_B_E_4_7_8_6,
             0X0_F_C_1_9_D_C_6,
             0X2_4_0_C_A_1_C_C,
             0X2_D_E_9_2_C_6_F,
             0X4_A_7_4_8_4_A_A,
             0X5_C_B_0_A_9_D_C,
             0X7_6_F_9_8_8_D_A,
             0X9_8_3_E_5_1_5_2,
             0XA_8_3_1_C_6_6_D,
             0XB_0_0_3_2_7_C_8,
             0XB_F_5_9_7_F_C_7,
             0XC_6_E_0_0_B_F_3,
             0XD_5_A_7_9_1_4_7,
             0X0_6_C_A_6_3_5_1,
             0X1_4_2_9_2_9_6_7,
             0X2_7_B_7_0_A_8_5,
             0X2_E_1_B_2_1_3_8,
             0X4_D_2_C_6_D_F_C,
             0X5_3_3_8_0_D_1_3,
             0X6_5_0_A_7_3_5_4,
             0X7_6_6_A_0_A_B_B,
             0X8_1_C_2_C_9_2_E,
             0X9_2_7_2_2_C_8_5,
             0XA_2_B_F_E_8_A_1,
             0XA_8_1_A_6_6_4_B,
             0XC_2_4_B_8_B_7_0,
             0XC_7_6_C_5_1_A_3,
             0XD_1_9_2_E_8_1_9,
             0XD_6_9_9_0_6_2_4,
             0XF_4_0_E_3_5_8_5,
             0X1_0_6_A_A_0_7_0,
             0X1_9_A_4_C_1_1_6,
             0X1_E_3_7_6_C_0_8,
             0X2_7_4_8_7_7_4_C,
             0X3_4_B_0_B_C_B_5,
             0X3_9_1_C_0_C_B_3,
             0X4_E_D_8_A_A_4_A,
             0X5_B_9_C_C_A_4_F,
             0X6_8_2_E_6_F_F_3,
             0X7_4_8_F_8_2_E_E,
             0X7_8_A_5_6_3_6_F,
             0X8_4_C_8_7_8_1_4,
             0X8_C_C_7_0_2_0_8,
             0X9_0_B_E_F_F_F_A,
             0XA_4_5_0_6_C_E_B,
             0XB_E_F_9_A_3_F_7,
             0XC_6_7_1_7_8_F_2,
         ]

         lowercase__     =  self.preprocessing(self.data)
         self.final_hash()


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			bytes)	->				bytes:




         """simple docstring"""

         lowercase__     =  B'\x80' + (B'\x00' * (63 - (len(lowerCAmelCase) + 8) % 64))
         lowercase__     =  struct.pack('>Q'			,				(len(lowerCAmelCase) * 8))
         return data + padding + big_endian_integer


   def 			UpperCAmelCase		(						self  :			Any)	->				None:




         """simple docstring"""

         lowercase__     =  [
             self.preprocessed_data[x : x + 64]
             for x in range(0			,				len(self.preprocessed_data)			,				64)
         ]

         for block in self.blocks:
               # Convert the given block into a list of 4 byte integers
               lowercase__     =  list(struct.unpack('>16L'			,				lowerCAmelCase))
               # add 48 0-ed integers
               words += [0] * 48

               lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  self.hashes

               for index in range(0			,				64):
                     if index > 15:
                           # modify the zero-ed indexes at the end of the array
                           lowercase__     =  (
                               self.ror(words[index - 15]			,				7)
                               ^ self.ror(words[index - 15]			,				18)
                               ^ (words[index - 15] >> 3)
                           )
                           lowercase__     =  (
                               self.ror(words[index - 2]			,				17)
                               ^ self.ror(words[index - 2]			,				19)
                               ^ (words[index - 2] >> 10)
                           )

                           lowercase__     =  (
                               words[index - 16] + sa + words[index - 7] + sa
                           ) % 0X1_0_0_0_0_0_0_0_0

                     # Compression
                     lowercase__     =  self.ror(lowerCAmelCase			,				6) ^ self.ror(lowerCAmelCase			,				11) ^ self.ror(lowerCAmelCase			,				25)
                     lowercase__     =  (e & f) ^ ((~e & 0XF_F_F_F_F_F_F_F) & g)
                     lowercase__     =  (
                         h + sa + ch + self.round_constants[index] + words[index]
                     ) % 0X1_0_0_0_0_0_0_0_0
                     lowercase__     =  self.ror(lowerCAmelCase			,				2) ^ self.ror(lowerCAmelCase			,				13) ^ self.ror(lowerCAmelCase			,				22)
                     lowercase__     =  (a & b) ^ (a & c) ^ (b & c)
                     lowercase__     =  (sa + maj) % 0X1_0_0_0_0_0_0_0_0

                     lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  (
                         g,
                         f,
                         e,
                         ((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
                         c,
                         b,
                         a,
                         ((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
                     )

               lowercase__     =  [a, b, c, d, e, f, g, h]

               # Modify final values
               lowercase__     =  [
                   ((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
                   for index, element in enumerate(self.hashes)
               ]

         lowercase__     =  ''.join([hex(lowerCAmelCase)[2:].zfill(8) for value in self.hashes])



   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

         return 0XF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)



class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Tuple)	->				None:




         """simple docstring"""

         import hashlib

         lowercase__     =  bytes('Test String'			,				'utf-8')
         self.assertEqual(SHAaaa(lowerCAmelCase).hash			,				hashlib.shaaaa(lowerCAmelCase).hexdigest())

def   _lowerCAmelCase   (						):
      import doctest

      doctest.testmod()

      lowercase__     =  argparse.ArgumentParser()
      parser.add_argument(
          '-s'						,  '--string'						,  dest='input_string'						,  default='Hello World!! Welcome to Cryptography'						,  help='Hash the string'						,  )
      parser.add_argument(
          '-f'						,  '--file'						,  dest='input_file'						,  help='Hash contents of a file'	)

      lowercase__     =  parser.parse_args()

      lowercase__     =  args.input_string

      # hash input should be a bytestring
      if args.input_file:
            with open(args.input_file						,  'rb'	) as f:
                  lowercase__     =  f.read()
      else:
            lowercase__     =  bytes(A__						,  'utf-8'	)

      print(SHAaaa(A__	).hash	)


if __name__ == "__main__":
       main()





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[Any]       				=    None
   A    :  Optional[int]       				=    None


   @property
   def 			UpperCAmelCase		(						self  :			str)	->				Union[str, Any]:




         """simple docstring"""

         return self.feat_extract_tester.prepare_feat_extract_dict()


   def 			UpperCAmelCase		(						self  :			int)	->				Any:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         self.assertTrue(hasattr(lowerCAmelCase			,				'feature_size'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'sampling_rate'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'padding_value'))


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

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

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='np')

         lowercase__     =  processed_features[input_name]

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

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


   @require_torch
   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='pt')

         lowercase__     =  processed_features[input_name]

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

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


   @require_tf
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='tf')

         lowercase__     =  processed_features[input_name]

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

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


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str=False)	->				Union[str, Any]:




         """simple docstring"""

         def _inputs_have_equal_length(lowerCAmelCase  :			int):
               lowercase__     =  len(input[0])
               for input_slice in input[1:]:
                     if len(lowerCAmelCase) != length:
                           return False
               return True

         def _inputs_are_equal(lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Tuple):
               if len(lowerCAmelCase) != len(lowerCAmelCase):
                     return False

               for input_slice_a, input_slice_a in zip(lowerCAmelCase			,				lowerCAmelCase):
                     if not np.allclose(np.asarray(lowerCAmelCase)			,				np.asarray(lowerCAmelCase)			,				atol=1E-3):
                           return False
               return True

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  self.feat_extract_tester.seq_length_diff
         lowercase__     =  self.feat_extract_tester.max_seq_length + pad_diff
         lowercase__     =  self.feat_extract_tester.min_seq_length
         lowercase__     =  self.feat_extract_tester.batch_size
         lowercase__     =  self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest')
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[-1]))
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='max_length')[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=lowerCAmelCase			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))
         self.assertTrue(len(input_a[0]) == pad_min_length)
         self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

         self.assertTrue(all(len(lowerCAmelCase) % 10 == 0 for x in input_a))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))

         lowercase__     =  pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(lowerCAmelCase) == expected_mult_pad_length for x in input_a))
         self.assertEqual(input_a.shape[:2]			,				(batch_size, expected_mult_pad_length))

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == feature_size)

         # Check padding value is correct
         lowercase__     =  (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
             < 1E-3)
         self.assertTrue(
             abs(
                 np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
             < 1E-3)
         self.assertTrue(
             abs(
                 np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
             < 1E-3)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1E-3)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
             < 1E-3)


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			Dict=False)	->				str:




         """simple docstring"""

         def _inputs_have_equal_length(lowerCAmelCase  :			int):
               lowercase__     =  len(input[0])
               for input_slice in input[1:]:
                     if len(lowerCAmelCase) != length:
                           return False
               return True

         def _inputs_are_equal(lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[Any]):
               if len(lowerCAmelCase) != len(lowerCAmelCase):
                     return False

               for input_slice_a, input_slice_a in zip(lowerCAmelCase			,				lowerCAmelCase):
                     if not np.allclose(np.asarray(lowerCAmelCase)			,				np.asarray(lowerCAmelCase)			,				atol=1E-3):
                           return False
               return True

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         # truncate to smallest
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0]))
         lowercase__     =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))

         # truncate to smallest with np
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				return_tensors='np'			,				truncation=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))

         # truncate to middle
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				truncation=lowerCAmelCase)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				truncation=lowerCAmelCase)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				truncation=lowerCAmelCase)[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				truncation=lowerCAmelCase)[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         lowercase__     =  12
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				truncation=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowercase__     =  len(speech_inputs[0])
         if expected_length % pad_to_multiple_of != 0:
               lowercase__     =  ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]) == expected_length)
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))


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




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Optional[Any]:




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				int:




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


   @require_torch
   def 			UpperCAmelCase		(						self  :			Dict)	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				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)


   @require_tf
   def 			UpperCAmelCase		(						self  :			str)	->				str:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='tf')[input_name]

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


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




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

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



   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})
         lowercase__     =  min(lowerCAmelCase)

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=lowerCAmelCase			,				truncation=lowerCAmelCase			,				return_tensors='np')
         self.assertIn('attention_mask'			,				lowerCAmelCase)
         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])
