





from typing import TYPE_CHECKING

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


__lowerCamelCase			:	int					    =	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	Any					    =	["""RobertaTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	Any					    =	[
        """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:
    __lowerCamelCase			:	Optional[int]					    =	[
        """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:
    __lowerCamelCase			:	List[str]					    =	[
        """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

    __lowerCamelCase			:	Any					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import numpy as np
import qiskit



def 						A_							(	_lowerCAmelCase = 8				,  _lowerCAmelCase = None	)		->					str:
 UpperCamelCase      :   Tuple			      =  np.random.default_rng(seed=_lowerCAmelCase	)

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

 # Quantum Circuit to simulate BB84
 UpperCamelCase      :   List[Any]			      =  qiskit.QuantumCircuit(_lowerCAmelCase				,  name="BB84"	)

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

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

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 UpperCamelCase      :   Union[str, Any]			      =  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.
 UpperCamelCase      :   Tuple			      =  qiskit.execute(_lowerCAmelCase				,  _lowerCAmelCase				,  shots=1				,  seed_simulator=_lowerCAmelCase	)
 # Returns the result of measurement.
 UpperCamelCase      :   Optional[Any]			      =  job.result().get_counts(_lowerCAmelCase	).most_frequent()

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

 # Get final key. Pad with 0 if too short, otherwise truncate.
 UpperCamelCase      :   Tuple			      =  gen_key[:key_len] if len(_lowerCAmelCase	) >= key_len else gen_key.ljust(_lowerCAmelCase				,  "0"	)
 return key


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

    testmod()
