'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_      	=	{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''RobertaTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RobertaForCausalLM''',
		    '''RobertaForMaskedLM''',
		    '''RobertaForMultipleChoice''',
		    '''RobertaForQuestionAnswering''',
		    '''RobertaForSequenceClassification''',
		    '''RobertaForTokenClassification''',
		    '''RobertaModel''',
		    '''RobertaPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRobertaForCausalLM''',
		    '''TFRobertaForMaskedLM''',
		    '''TFRobertaForMultipleChoice''',
		    '''TFRobertaForQuestionAnswering''',
		    '''TFRobertaForSequenceClassification''',
		    '''TFRobertaForTokenClassification''',
		    '''TFRobertaMainLayer''',
		    '''TFRobertaModel''',
		    '''TFRobertaPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''FlaxRobertaForCausalLM''',
		    '''FlaxRobertaForMaskedLM''',
		    '''FlaxRobertaForMultipleChoice''',
		    '''FlaxRobertaForQuestionAnswering''',
		    '''FlaxRobertaForSequenceClassification''',
		    '''FlaxRobertaForTokenClassification''',
		    '''FlaxRobertaModel''',
		    '''FlaxRobertaPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
		from .tokenization_roberta import RobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roberta_fast import RobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta import (
				    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaForCausalLM,
				    RobertaForMaskedLM,
				    RobertaForMultipleChoice,
				    RobertaForQuestionAnswering,
				    RobertaForSequenceClassification,
				    RobertaForTokenClassification,
				    RobertaModel,
				    RobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta import (
				    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaForCausalLM,
				    TFRobertaForMaskedLM,
				    TFRobertaForMultipleChoice,
				    TFRobertaForQuestionAnswering,
				    TFRobertaForSequenceClassification,
				    TFRobertaForTokenClassification,
				    TFRobertaMainLayer,
				    TFRobertaModel,
				    TFRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				    FlaxRobertaPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''







import numpy as np
import qiskit


def    lowerCamelCase       (			UpperCAmelCase__				:  int = 8				,						UpperCAmelCase__				:  int | None = None		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.random.default_rng(seed=UpperCAmelCase__		)

							# Roughly 25% of the qubits will contribute to the key.
							# So we take more than we need.
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 6 * key_len
							# Measurement basis for Alice's qubits.
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rng.integers(2				,						size=UpperCAmelCase__		)
							# The set of states Alice will prepare.
							SCREAMING_SNAKE_CASE__     :List[Any]						    = rng.integers(2				,						size=UpperCAmelCase__		)
							# Measurement basis for Bob's qubits.
							SCREAMING_SNAKE_CASE__     :str						    = rng.integers(2				,						size=UpperCAmelCase__		)

							# Quantum Circuit to simulate BB84
							SCREAMING_SNAKE_CASE__     :int						    = qiskit.QuantumCircuit(UpperCAmelCase__				,						name='BB84'		)

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

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

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							SCREAMING_SNAKE_CASE__     :str						    = 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.
							SCREAMING_SNAKE_CASE__     :int						    = qiskit.execute(UpperCAmelCase__				,						UpperCAmelCase__				,						shots=1				,						seed_simulator=UpperCAmelCase__		)
							# Returns the result of measurement.
							SCREAMING_SNAKE_CASE__     :List[Any]						    = job.result().get_counts(UpperCAmelCase__		).most_frequent()

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

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


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

		testmod()






