




"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
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


SCREAMING_SNAKE_CASE__	:				Tuple  =       random.Random()

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=1.0			,_SCREAMING_SNAKE_CASE=None			,_SCREAMING_SNAKE_CASE=None   )     ->			Union[str, Any]:
       if rng is None:
              lowerCamelCase       :				Tuple      			= global_rng

       lowerCamelCase       :				Dict      			= []
       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 		UpperCamelCase__     (unittest.TestCase					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__=7					,  UpperCamelCase__=400					,  UpperCamelCase__=2000					,  UpperCamelCase__=1					,  UpperCamelCase__=0.0					,  UpperCamelCase__=2_4000					,  UpperCamelCase__=True					,  )					->				List[Any]:
           lowerCamelCase       :				List[Any]      			= parent
           lowerCamelCase       :				Dict      			= batch_size
           lowerCamelCase       :				List[str]      			= min_seq_length
           lowerCamelCase       :				Optional[Any]      			= max_seq_length
           lowerCamelCase       :				str      			= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           lowerCamelCase       :				int      			= feature_size
           lowerCamelCase       :				Optional[int]      			= padding_value
           lowerCamelCase       :				Dict      			= sampling_rate
           lowerCamelCase       :				List[str]      			= return_attention_mask







    def    _lowercase					(   self					)					->				Union[str, Any]:
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }







    def    _lowercase					(   self					,  UpperCamelCase__=False					,  UpperCamelCase__=False					)					->				str:
           def _flatten(UpperCamelCase__					):
                  return list(itertools.chain(*_snake_case					)					)

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

           return audio_inputs




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

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				EncodecFeatureExtractor







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Optional[Any]      			= EnCodecFeatureExtractionTester(self					)







    def    _lowercase					(   self					)					->				str:
           # Tests that all call wrap to encode_plus and batch_encode_plus
           lowerCamelCase       :				Tuple      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
           # create three inputs of length 800, 1000, and 1200
           lowerCamelCase       :				str      			= [floats_list((1, x)					)[0] for x in range(800					,  1400					,  200					)]
           lowerCamelCase       :				Tuple      			= [np.asarray(_snake_case					) for audio_input in audio_inputs]

           # Test not batched input
           lowerCamelCase       :				List[str]      			= feat_extract(audio_inputs[0]					,  return_tensors="np"					).input_values
           lowerCamelCase       :				Any      			= feat_extract(np_audio_inputs[0]					,  return_tensors="np"					).input_values
           self.assertTrue(np.allclose(_snake_case					,  _snake_case					,  atol=1e-3					)					)

           # Test batched
           lowerCamelCase       :				Any      			= feat_extract(_snake_case					,  padding=_snake_case					,  return_tensors="np"					).input_values
           lowerCamelCase       :				int      			= 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					)					)







    def    _lowercase					(   self					)					->				Dict:
           lowerCamelCase       :				Optional[Any]      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
           lowerCamelCase       :				int      			= np.random.rand(100					).astype(np.floataa					)
           lowerCamelCase       :				List[str]      			= np_audio_inputs.tolist()

           for inputs in [py_audio_inputs, np_audio_inputs]:
                  lowerCamelCase       :				Tuple      			= feature_extractor.pad([{"input_values": inputs}]					,  return_tensors="np"					)
                  self.assertTrue(np_processed.input_values.dtype == np.floataa					)
                  lowerCamelCase       :				Union[str, Any]      			= feature_extractor.pad([{"input_values": inputs}]					,  return_tensors="pt"					)
                  self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Optional[int]:
           from datasets import load_dataset

           lowerCamelCase       :				List[Any]      			= load_dataset("hf-internal-testing/librispeech_asr_dummy"					,  "clean"					,  split="validation"					)
           # automatic decoding with librispeech
           lowerCamelCase       :				Optional[int]      			= ds.sort("id"					).select(range(_snake_case					)					)[:num_samples]['audio']

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







    def    _lowercase					(   self					)					->				List[str]:
           # fmt: off
           lowerCamelCase       :				Union[str, 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
           lowerCamelCase       :				str      			= self._load_datasamples(1					)
           lowerCamelCase       :				str      			= EncodecFeatureExtractor()
           lowerCamelCase       :				List[str]      			= feature_extractor(_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(input_values.shape					,  (1, 1, 9_3680)					)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]					,  _snake_case					,  atol=1e-6					)					)







    def    _lowercase					(   self					)					->				Any:
           # fmt: off
           lowerCamelCase       :				int      			= 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
           lowerCamelCase       :				Tuple      			= self._load_datasamples(1					)
           lowerCamelCase       :				List[Any]      			= [np.tile(input_audio[0][None]					,  reps=(2, 1)					)]
           input_audio[0][1] *= 0.5
           lowerCamelCase       :				List[Any]      			= EncodecFeatureExtractor(feature_size=2					)
           lowerCamelCase       :				Union[str, Any]      			= feature_extractor(_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(input_values.shape					,  (1, 2, 9_3680)					)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]					,  _snake_case					,  atol=1e-6					)					)
           self.assertTrue(torch.allclose(input_values[0, 1, :30]					,  EXPECTED_INPUT_VALUES * 0.5					,  atol=1e-6					)					)







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				str      			= self._load_datasamples(2					)
           # would be easier if the stride was like
           lowerCamelCase       :				str      			= EncodecFeatureExtractor(feature_size=1					,  chunk_length_s=1					,  overlap=0.01					)

           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               _snake_case					,  "^Both padding and truncation were set. Make sure you only set one.$"					,  ):
                  lowerCamelCase       :				str      			= feature_extractor(
                      _snake_case					,  padding="max_length"					,  truncation=_snake_case					,  return_tensors="pt"					).input_values

           # truncate to chunk
           lowerCamelCase       :				Optional[Any]      			= feature_extractor(_snake_case					,  truncation=_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(truncated_outputs.shape					,  (2, 1, 7_1520)					)  # 2 chunks

           # force truncate to max_length
           lowerCamelCase       :				Dict      			= feature_extractor(
               _snake_case					,  truncation=_snake_case					,  max_length=4_8000					,  return_tensors="pt"					).input_values
           self.assertEquals(truncated_outputs.shape					,  (2, 1, 4_8000)					)

           # pad to chunk
           lowerCamelCase       :				Union[str, Any]      			= feature_extractor(_snake_case					,  padding=_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(padded_outputs.shape					,  (2, 1, 9_5280)					)

           # pad to chunk
           lowerCamelCase       :				List[str]      			= feature_extractor(_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(truncated_outputs.shape					,  (2, 1, 9_5280)					)

           # force pad to max length
           lowerCamelCase       :				Dict      			= feature_extractor(
               _snake_case					,  padding="max_length"					,  max_length=10_0000					,  return_tensors="pt"					).input_values
           self.assertEquals(truncated_outputs.shape					,  (2, 1, 10_0000)					)

           # force no pad
           with self.assertRaisesRegex(
               _snake_case					,  "^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$"					,  ):
                  lowerCamelCase       :				str      			= feature_extractor(_snake_case					,  padding=_snake_case					,  return_tensors="pt"					).input_values

           lowerCamelCase       :				List[Any]      			= feature_extractor(input_audio[0]					,  padding=_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(truncated_outputs.shape					,  (1, 1, 9_3680)					)

           # no pad if no chunk_length_s
           lowerCamelCase       :				str      			= None
           with self.assertRaisesRegex(
               _snake_case					,  "^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$"					,  ):
                  lowerCamelCase       :				int      			= feature_extractor(_snake_case					,  padding=_snake_case					,  return_tensors="pt"					).input_values

           lowerCamelCase       :				Any      			= feature_extractor(input_audio[0]					,  padding=_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(truncated_outputs.shape					,  (1, 1, 9_3680)					)

           # no pad if no overlap
           lowerCamelCase       :				Optional[Any]      			= 2
           lowerCamelCase       :				List[Any]      			= None
           with self.assertRaisesRegex(
               _snake_case					,  "^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$"					,  ):
                  lowerCamelCase       :				Any      			= feature_extractor(_snake_case					,  padding=_snake_case					,  return_tensors="pt"					).input_values

           lowerCamelCase       :				List[Any]      			= feature_extractor(input_audio[0]					,  padding=_snake_case					,  return_tensors="pt"					).input_values
           self.assertEquals(truncated_outputs.shape					,  (1, 1, 9_3680)					)












import numpy as np
import qiskit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Optional[int]      			= qiskit.QuantumCircuit(num_qubits + 1   )

       # First, let's deal with the case in which oracle is balanced
       if case == "balanced":
              # First generate a random number that tells us which CNOTs to
              # wrap in X-gates:
              lowerCamelCase       :				Optional[int]      			= np.random.randint(1			,2**num_qubits   )
              # Next, format 'b' as a binary string of length 'n', padded with zeros:
              lowerCamelCase       :				str      			= format(_SCREAMING_SNAKE_CASE			,f'''0{num_qubits}b'''   )
              # Next, we place the first X-gates. Each digit in our binary string
              # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
              # we apply an X-gate to that qubit:
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
              for index in range(_SCREAMING_SNAKE_CASE   ):
                     oracle_qc.cx(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
              # Next, place the final X-gates
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )

    # Case in which oracle is constant
       if case == "constant":
              # First decide what the fixed output of the oracle will be
              # (either always 0 or always 1)
              lowerCamelCase       :				str      			= np.random.randint(2   )
              if output == 1:
                     oracle_qc.x(_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				int      			= oracle_qc.to_gate()
       lowerCamelCase       :				Tuple      			= "Oracle"  # To show when we display the circuit
       return oracle_gate

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Union[str, Any]      			= qiskit.QuantumCircuit(num_qubits + 1			,_SCREAMING_SNAKE_CASE   )
       # Set up the output qubit:
       dj_circuit.x(_SCREAMING_SNAKE_CASE   )
       dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # And set up the input register:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # Let's append the oracle gate to our circuit:
       dj_circuit.append(_SCREAMING_SNAKE_CASE			,range(num_qubits + 1   )   )
       # Finally, perform the H-gates again and measure:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )

       for i in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.measure(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       return dj_circuit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.result.counts.Counts:
       lowerCamelCase       :				Tuple      			= qiskit.Aer.get_backend("aer_simulator"   )

       lowerCamelCase       :				Tuple      			= dj_oracle(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= dj_algorithm(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       # Execute the circuit on the simulator
       lowerCamelCase       :				List[Any]      			= qiskit.execute(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,shots=1000   )

       # Return the histogram data of the results of the experiment.
       return job.result().get_counts(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       print(f'''Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}''')
       print(f'''Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}''')






