
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class   UpperCamelCase_      (      UpperCAmelCase__		):







						'''simple docstring'''



						UpperCamelCase      :  Union[str, Any]				   =					'''Wav2Vec2FeatureExtractor'''
						UpperCamelCase      :  Tuple				   =					'''AutoTokenizer'''





						def __init__(			self							:int		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										super().__init__(__lowercase		,    __lowercase     )
										lowercase         =      self.feature_extractor
										lowercase         =      False





						@classmethod
						def        SCREAMING_SNAKE_CASE(			cls							:Any		,    lowerCAmelCase__							:List[str]		,    **lowerCAmelCase__							:Dict     )  ->Tuple:






										try:
														return super().from_pretrained(__lowercase		,    **__lowercase     )
										except OSError:
														warnings.warn(
														    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
														    " include a `tokenizer_class` attribute is deprecated and will be "
														    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
														    " attribute to either your `config.json` or `tokenizer_config.json` "
														    "file to suppress this warning: "		,    __lowercase		,    )

														lowercase         =      WavaVecaFeatureExtractor.from_pretrained(__lowercase		,    **__lowercase     )
														lowercase         =      WavaVecaCTCTokenizer.from_pretrained(__lowercase		,    **__lowercase     )

														return cls(feature_extractor=__lowercase		,    tokenizer=__lowercase     )





						def __call__(			self							:List[Any]		,    *lowerCAmelCase__							:List[Any]		,    **lowerCAmelCase__							:List[str]     )  ->Any:






										if self._in_target_context_manager:
														return self.current_processor(*__lowercase		,    **__lowercase     )

										if "raw_speech" in kwargs:
														warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."     )
														lowercase         =      kwargs.pop("raw_speech"     )
										else:
														lowercase         =      kwargs.pop("audio"		,    __lowercase     )
										lowercase         =      kwargs.pop("sampling_rate"		,    __lowercase     )
										lowercase         =      kwargs.pop("text"		,    __lowercase     )
										if len(__lowercase     ) > 0:
														lowercase         =      args[0]
														lowercase         =      args[1:]

										if audio is None and text is None:
														raise ValueError("You need to specify either an `audio` or `text` input to process."     )

										if audio is not None:
														lowercase         =      self.feature_extractor(__lowercase		,    *__lowercase		,    sampling_rate=__lowercase		,    **__lowercase     )
										if text is not None:
														lowercase         =      self.tokenizer(__lowercase		,    **__lowercase     )

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														lowercase         =      encodings["input_ids"]
														return inputs





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    *lowerCAmelCase__							:str		,    **lowerCAmelCase__							:Dict     )  ->Optional[int]:






										if self._in_target_context_manager:
														return self.current_processor.pad(*__lowercase		,    **__lowercase     )

										lowercase         =      kwargs.pop("input_features"		,    __lowercase     )
										lowercase         =      kwargs.pop("labels"		,    __lowercase     )
										if len(__lowercase     ) > 0:
														lowercase         =      args[0]
														lowercase         =      args[1:]

										if input_features is not None:
														lowercase         =      self.feature_extractor.pad(__lowercase		,    *__lowercase		,    **__lowercase     )
										if labels is not None:
														lowercase         =      self.tokenizer.pad(__lowercase		,    **__lowercase     )

										if labels is None:
														return input_features
										elif input_features is None:
														return labels
										else:
														lowercase         =      labels["input_ids"]
														return input_features





						def        SCREAMING_SNAKE_CASE(			self							:str		,    *lowerCAmelCase__							:Dict		,    **lowerCAmelCase__							:int     )  ->str:
										return self.tokenizer.batch_decode(*__lowercase		,    **__lowercase     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    *lowerCAmelCase__							:Union[str, Any]		,    **lowerCAmelCase__							:Union[str, Any]     )  ->Optional[int]:
										return self.tokenizer.decode(*__lowercase		,    **__lowercase     )





						@contextmanager
						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->int:
										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your audio inputs, or in a separate call."     )
										lowercase         =      True
										lowercase         =      self.tokenizer
										yield
										lowercase         =      self.feature_extractor
										lowercase         =      False






import numpy as np
import qiskit


def 		lowerCamelCase__       (						_A = 8	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	np.random.default_rng(seed=_A				)

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

	# Quantum Circuit to simulate BB84
	snake_case_						       =	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.
	snake_case_						       =	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.
	snake_case_						       =	qiskit.execute(_A	,      _A	,      shots=1	,      seed_simulator=_A				)
	# Returns the result of measurement.
	snake_case_						       =	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.
	snake_case_						       =	"".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.
	snake_case_						       =	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()
