





import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_snake_case           =			random.Random()


def _A       (   __magic_name__  , __magic_name__=1.0  , __magic_name__=None  , __magic_name__=None	):
   if rng is None:
      lowercase__			       =  global_rng

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

   return values





class  lowerCAmelCase							(       unittest.TestCase ):







  def __init__(      self  :Union[str, Any]   ,							_lowercase  :str   ,							_lowercase  :Union[str, Any]=7   ,							_lowercase  :int=4_00   ,							_lowercase  :List[Any]=20_00   ,							_lowercase  :Union[str, Any]=1   ,							_lowercase  :Any=0.0   ,							_lowercase  :List[Any]=1_60_00   ,							_lowercase  :List[str]=True   ,							_lowercase  :Any=True   ,							):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :int ):






     '''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,
         "do_normalize": self.do_normalize,
     }







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :List[str]=False   ,							_lowercase  :List[Any]=False ):






     '''simple docstring'''





     def _flatten(_lowercase  :Any ):
        return list(itertools.chain(*_lowercase ) )

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

     return speech_inputs





class  lowerCAmelCase							(       lowercase_							,							unittest.TestCase ):
  __lowerCamelCase		=      WavaVecaFeatureExtractor







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  WavaVecaFeatureExtractionTester(self )







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :str ):






     '''simple docstring'''





     self.assertTrue(np.all(np.mean(_lowercase   ,							axis=0 ) < 1e-3 ) )
     self.assertTrue(np.all(np.abs(np.var(_lowercase   ,							axis=0 ) - 1 ) < 1e-3 ) )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     # create three inputs of length 800, 1000, and 1200
     lowercase__			       =  [floats_list((1, x) )[0] for x in range(8_00   ,							14_00   ,							2_00 )]
     lowercase__			       =  [np.asarray(_lowercase ) for speech_input in speech_inputs]

     # Test not batched input
     lowercase__			       =  feat_extract(speech_inputs[0]   ,							return_tensors="np" ).input_values
     lowercase__			       =  feat_extract(np_speech_inputs[0]   ,							return_tensors="np" ).input_values
     self.assertTrue(np.allclose(_lowercase   ,							_lowercase   ,							atol=1e-3 ) )

     # Test batched
     lowercase__			       =  feat_extract(_lowercase   ,							return_tensors="np" ).input_values
     lowercase__			       =  feat_extract(_lowercase   ,							return_tensors="np" ).input_values
     for enc_seq_a, enc_seq_a in zip(_lowercase   ,							_lowercase ):
        self.assertTrue(np.allclose(_lowercase   ,							_lowercase   ,							atol=1e-3 ) )

     # Test 2-D numpy arrays are batched.
     lowercase__			       =  [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
     lowercase__			       =  np.asarray(_lowercase )
     lowercase__			       =  feat_extract(_lowercase   ,							return_tensors="np" ).input_values
     lowercase__			       =  feat_extract(_lowercase   ,							return_tensors="np" ).input_values
     for enc_seq_a, enc_seq_a in zip(_lowercase   ,							_lowercase ):
        self.assertTrue(np.allclose(_lowercase   ,							_lowercase   ,							atol=1e-3 ) )







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     lowercase__			       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     lowercase__			       =  [floats_list((1, x) )[0] for x in range(8_00   ,							14_00   ,							2_00 )]

     lowercase__			       =  ["longest", "max_length", "do_not_pad"]
     lowercase__			       =  [None, 16_00, None]
     for max_length, padding in zip(_lowercase   ,							_lowercase ):
        lowercase__			       =  feat_extract(_lowercase   ,							padding=_lowercase   ,							max_length=_lowercase   ,							return_tensors="np" )
        lowercase__			       =  processed.input_values

        self._check_zero_mean_unit_variance(input_values[0][:8_00] )
        self.assertTrue(input_values[0][8_00:].sum() < 1e-6 )
        self._check_zero_mean_unit_variance(input_values[1][:10_00] )
        self.assertTrue(input_values[0][10_00:].sum() < 1e-6 )
        self._check_zero_mean_unit_variance(input_values[2][:12_00] )







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     lowercase__			       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     lowercase__			       =  range(8_00   ,							14_00   ,							2_00 )
     lowercase__			       =  [floats_list((1, x) )[0] for x in lengths]

     lowercase__			       =  ["longest", "max_length", "do_not_pad"]
     lowercase__			       =  [None, 16_00, None]

     for max_length, padding in zip(_lowercase   ,							_lowercase ):
        lowercase__			       =  feat_extract(_lowercase   ,							max_length=_lowercase   ,							padding=_lowercase )
        lowercase__			       =  processed.input_values

        self._check_zero_mean_unit_variance(input_values[0][:8_00] )
        self._check_zero_mean_unit_variance(input_values[1][:10_00] )
        self._check_zero_mean_unit_variance(input_values[2][:12_00] )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     lowercase__			       =  [floats_list((1, x) )[0] for x in range(8_00   ,							14_00   ,							2_00 )]
     lowercase__			       =  feat_extract(
         _lowercase   ,							truncation=_lowercase   ,							max_length=10_00   ,							padding="max_length"   ,							return_tensors="np" )
     lowercase__			       =  processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_00] )
     self._check_zero_mean_unit_variance(input_values[1] )
     self._check_zero_mean_unit_variance(input_values[2] )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     lowercase__			       =  [floats_list((1, x) )[0] for x in range(8_00   ,							14_00   ,							2_00 )]
     lowercase__			       =  feat_extract(
         _lowercase   ,							truncation=_lowercase   ,							max_length=10_00   ,							padding="longest"   ,							return_tensors="np" )
     lowercase__			       =  processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_00] )
     self._check_zero_mean_unit_variance(input_values[1, :10_00] )
     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, 10_00) )

     lowercase__			       =  [floats_list((1, x) )[0] for x in range(8_00   ,							14_00   ,							2_00 )]
     lowercase__			       =  feat_extract(
         _lowercase   ,							truncation=_lowercase   ,							max_length=20_00   ,							padding="longest"   ,							return_tensors="np" )
     lowercase__			       =  processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_00] )
     self._check_zero_mean_unit_variance(input_values[1, :10_00] )
     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, 12_00) )







  @require_torch
  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     import torch

     lowercase__			       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     lowercase__			       =  np.random.rand(1_00 ).astype(np.floataa )
     lowercase__			       =  np_speech_inputs.tolist()

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







  @slow
  @require_torch
  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
        lowercase__			       =  WavaVecaConfig.from_pretrained(_lowercase )
        lowercase__			       =  WavaVecaFeatureExtractor.from_pretrained(_lowercase )

        # only "layer" feature extraction norm should make use of
        # attention_mask
        self.assertEqual(feat_extract.return_attention_mask   ,							config.feat_extract_norm == "layer" )













def _A       (   __magic_name__	):
   lowercase__			       =  ""
   for ch in key:
      if ch == " " or ch not in key_no_dups and ch.isalpha():
         key_no_dups += ch
   return key_no_dups


def _A       (   __magic_name__	):
   lowercase__			       =  [chr(i + 65	) for i in range(26	)]
   # Remove duplicate characters from key
   lowercase__			       =  remove_duplicates(key.upper()	)
   lowercase__			       =  len(__magic_name__	)
   # First fill cipher with key characters
   lowercase__			       =  {alphabet[i]: char for i, char in enumerate(__magic_name__	)}
   # Then map remaining characters in alphabet to
   # the alphabet from the beginning
   for i in range(len(__magic_name__	)  , 26	):
      lowercase__			       =  alphabet[i - offset]
      # Ensure we are not mapping letters to letters previously mapped
      while char in key:
         offset -= 1
         lowercase__			       =  alphabet[i - offset]
      lowercase__			       =  char
   return cipher_alphabet


def _A       (   __magic_name__  , __magic_name__	):
   return "".join(cipher_map.get(__magic_name__  , __magic_name__	) for ch in message.upper()	)


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  {v: k for k, v in cipher_map.items()}
   return "".join(rev_cipher_map.get(__magic_name__  , __magic_name__	) for ch in message.upper()	)


def _A       (   ):
   lowercase__			       =  input("Enter message to encode or decode: "	).strip()
   lowercase__			       =  input("Enter keyword: "	).strip()
   lowercase__			       =  input("Encipher or decipher? E/D:"	).strip()[0].lower()
   try:
      lowercase__			       =  {"e": encipher, "d": decipher}[option]
   except KeyError:
      raise KeyError("invalid input option"	)
   lowercase__			       =  create_cipher_map(__magic_name__	)
   print(func(__magic_name__  , __magic_name__	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






