



'''simple docstring'''





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


lowercase_	 =						random.Random()




def lowerCAmelCase						(__A						,			__A=1.0						,			__A=None						,			__A=None):



     """simple docstring"""



     if rng is None:
          _a											=						global_rng

     _a											=						[]
     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		):



 '''simple docstring'''







 def __init__(self				,     A				,     A=7				,     A=400				,     A=2_000				,     A=1				,     A=0.0				,     A=16_000				,     A=True				,     A=True				,     )					->  Optional[Any]:


      """simple docstring"""



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



 def       a__						(self )					->  Dict:


      """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       a__						(self				,     A=False				,     A=False )					->  int:


      """simple docstring"""



      def _flatten(A ):
           return list(itertools.chain(*A ) )

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

      return speech_inputs






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: List[Any]    = WavaVecaFeatureExtractor



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						WavaVecaFeatureExtractionTester(self )



 def       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



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



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      # create three inputs of length 800, 1000, and 1200
      _a											=						[floats_list((1, x) )[0] for x in range(800				,     1_400				,     200 )]
      _a											=						[np.asarray(A ) for speech_input in speech_inputs]

      # Test not batched input
      _a											=						feat_extract(speech_inputs[0]				,     return_tensors='''np''' ).input_values
      _a											=						feat_extract(np_speech_inputs[0]				,     return_tensors='''np''' ).input_values
      self.assertTrue(np.allclose(A				,     A				,     atol=1E-3 ) )

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

      # Test 2-D numpy arrays are batched.
      _a											=						[floats_list((1, x) )[0] for x in (800, 800, 800)]
      _a											=						np.asarray(A )
      _a											=						feat_extract(A				,     return_tensors='''np''' ).input_values
      _a											=						feat_extract(A				,     return_tensors='''np''' ).input_values
      for enc_seq_a, enc_seq_a in zip(A				,     A ):
           self.assertTrue(np.allclose(A				,     A				,     atol=1E-3 ) )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      _a											=						[floats_list((1, x) )[0] for x in range(800				,     1_400				,     200 )]

      _a											=						['''longest''', '''max_length''', '''do_not_pad''']
      _a											=						[None, 1_600, None]
      for max_length, padding in zip(A				,     A ):
           _a											=						feat_extract(A				,     padding=A				,     max_length=A				,     return_tensors='''np''' )
           _a											=						processed.input_values

           self._check_zero_mean_unit_variance(input_values[0][:800] )
           self.assertTrue(input_values[0][800:].sum() < 1E-6 )
           self._check_zero_mean_unit_variance(input_values[1][:1_000] )
           self.assertTrue(input_values[0][1_000:].sum() < 1E-6 )
           self._check_zero_mean_unit_variance(input_values[2][:1_200] )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      _a											=						range(800				,     1_400				,     200 )
      _a											=						[floats_list((1, x) )[0] for x in lengths]

      _a											=						['''longest''', '''max_length''', '''do_not_pad''']
      _a											=						[None, 1_600, None]

      for max_length, padding in zip(A				,     A ):
           _a											=						feat_extract(A				,     max_length=A				,     padding=A )
           _a											=						processed.input_values

           self._check_zero_mean_unit_variance(input_values[0][:800] )
           self._check_zero_mean_unit_variance(input_values[1][:1_000] )
           self._check_zero_mean_unit_variance(input_values[2][:1_200] )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      _a											=						[floats_list((1, x) )[0] for x in range(800				,     1_400				,     200 )]
      _a											=						feat_extract(
          A				,     truncation=A				,     max_length=1_000				,     padding='''max_length'''				,     return_tensors='''np''' )
      _a											=						processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :800] )
      self._check_zero_mean_unit_variance(input_values[1] )
      self._check_zero_mean_unit_variance(input_values[2] )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      _a											=						[floats_list((1, x) )[0] for x in range(800				,     1_400				,     200 )]
      _a											=						feat_extract(
          A				,     truncation=A				,     max_length=1_000				,     padding='''longest'''				,     return_tensors='''np''' )
      _a											=						processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :800] )
      self._check_zero_mean_unit_variance(input_values[1, :1_000] )
      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_000) )

      _a											=						[floats_list((1, x) )[0] for x in range(800				,     1_400				,     200 )]
      _a											=						feat_extract(
          A				,     truncation=A				,     max_length=2_000				,     padding='''longest'''				,     return_tensors='''np''' )
      _a											=						processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :800] )
      self._check_zero_mean_unit_variance(input_values[1, :1_000] )
      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_200) )



 @require_torch
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      import torch

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

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



 @slow
 @require_torch
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
           _a											=						WavaVecaConfig.from_pretrained(A )
           _a											=						WavaVecaFeatureExtractor.from_pretrained(A )

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








'''simple docstring'''





import numpy as np
import qiskit




def lowerCAmelCase						(__A = 8						,			__A = None):



     """simple docstring"""



     _a											=						np.random.default_rng(seed=__A)

     # Roughly 25% of the qubits will contribute to the key.
     # So we take more than we need.
     _a											=						6 * key_len
     # Measurement basis for Alice's qubits.
     _a											=						rng.integers(2						,			size=__A)
     # The set of states Alice will prepare.
     _a											=						rng.integers(2						,			size=__A)
     # Measurement basis for Bob's qubits.
     _a											=						rng.integers(2						,			size=__A)

     # Quantum Circuit to simulate BB84
     _a											=						qiskit.QuantumCircuit(__A						,			name='''BB84''')

     # Alice prepares her qubits according to rules above.
     for index, _ in enumerate(__A):
          if alice_state[index] == 1:
               bbaa_circ.x(__A)
          if alice_basis[index] == 1:
               bbaa_circ.h(__A)
     bbaa_circ.barrier()

     # Bob measures the received qubits according to rules above.
     for index, _ in enumerate(__A):
          if bob_basis[index] == 1:
               bbaa_circ.h(__A)

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

     # Simulate the quantum circuit.
     _a											=						qiskit.Aer.get_backend('''aer_simulator''')
     # We only need to run one shot because the key is unique.
     # Multiple shots will produce the same key.
     _a											=						qiskit.execute(__A						,			__A						,			shots=1						,			seed_simulator=__A)
     # Returns the result of measurement.
     _a											=						job.result().get_counts(__A).most_frequent()

     # Extracting the generated key from the simulation results.
     # Only keep measurement results where Alice and Bob chose the same basis.
     _a											=						''''''.join(
         [
             result_bit
             for alice_basis_bit, bob_basis_bit, result_bit in zip(
                 __A						,			__A						,			__A)
             if alice_basis_bit == bob_basis_bit
         ])

     # Get final key. Pad with 0 if too short, otherwise truncate.
     _a											=						gen_key[:key_len] if len(__A) >= key_len else gen_key.ljust(__A						,			'''0''')
     return key


if __name__ == "__main__":
  print(F"""The generated key is : {bbaa(8, seed=0)}""")
  from doctest import testmod

  testmod()



