




from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowercase__							:	Dict											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							if isinstance(__lowercase			,      __lowercase		):
								snake_case_						       =	[label.strip() for label in labels.split(","		) if label.strip()]
							return labels






						def __call__(		self				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Any			,      __lowercase				:	str		):






							"""simple docstring"""






							if len(__lowercase		) == 0 or len(__lowercase		) == 0:
								raise ValueError("You must include at least one label and at least one sequence."		)
							if hypothesis_template.format(labels[0]		) == hypothesis_template:
								raise ValueError(
								    (
								        "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
								        "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
								    ).format(__lowercase		)		)

							if isinstance(__lowercase			,      __lowercase		):
								snake_case_						       =	[sequences]

							snake_case_						       =	[]
							for sequence in sequences:
								sequence_pairs.extend([[sequence, hypothesis_template.format(__lowercase		)] for label in labels]		)

							return sequence_pairs, sequences







@add_end_docstrings(UpperCAmelCase__     )
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	int			,      __lowercase				:	Any=ZeroShotClassificationArgumentHandler()			,      *__lowercase				:	Tuple			,      **__lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	args_parser
							super().__init__(*__lowercase			,      **__lowercase		)
							if self.entailment_id == -1:
								logger.warning(
								    "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
								    "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."		)






						@property
						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							for label, ind in self.model.config.labelaid.items():
								if label.lower().startswith("entail"		):
									return ind
							return -1






						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[Any]=True			,      __lowercase				:	Dict=True			,      __lowercase				:	Optional[int]=TruncationStrategy.ONLY_FIRST			,      **__lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	self.framework
							if self.tokenizer.pad_token is None:
								# Override for tokenizers not supporting padding
								logger.error(
								    "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
								    " `pad_token=eos_token`"		)
								snake_case_						       =	self.tokenizer.eos_token
							try:
								snake_case_						       =	self.tokenizer(
								    __lowercase			,      add_special_tokens=__lowercase			,      return_tensors=__lowercase			,      padding=__lowercase			,      truncation=__lowercase			,      )
							except Exception as e:
								if "too short" in str(__lowercase		):
									# tokenizers might yell that we want to truncate
									# to a value that is not even reached by the input.
									# In that case we don't want to truncate.
									# It seems there's not a really better way to catch that
									# exception.

									snake_case_						       =	self.tokenizer(
									    __lowercase			,      add_special_tokens=__lowercase			,      return_tensors=__lowercase			,      padding=__lowercase			,      truncation=TruncationStrategy.DO_NOT_TRUNCATE			,      )
								else:
									raise e

							return inputs






						def 			snake_case__		(		self				:	Union[str, Any]			,      **__lowercase				:	Tuple		):






							"""simple docstring"""






							if kwargs.get("multi_class"			,      __lowercase		) is not None:
								snake_case_						       =	kwargs["multi_class"]
								logger.warning(
								    "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
								    "`multi_class` will be removed in a future version of Transformers."		)
							snake_case_						       =	{}
							if "candidate_labels" in kwargs:
								snake_case_						       =	self._args_parser._parse_labels(kwargs["candidate_labels"]		)
							if "hypothesis_template" in kwargs:
								snake_case_						       =	kwargs["hypothesis_template"]

							snake_case_						       =	{}
							if "multi_label" in kwargs:
								snake_case_						       =	kwargs["multi_label"]
							return preprocess_params, {}, postprocess_params






						def __call__(		self				:	Tuple			,      __lowercase				:	Union[str, List[str]]			,      *__lowercase				:	int			,      **__lowercase				:	List[Any]			,      ):






							"""simple docstring"""






							if len(__lowercase		) == 0:
								pass
							elif len(__lowercase		) == 1 and "candidate_labels" not in kwargs:
								snake_case_						       =	args[0]
							else:
								raise ValueError(f"Unable to understand extra arguments {args}"		)

							return super().__call__(__lowercase			,      **__lowercase		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	Optional[Any]="This example is {}."		):






							"""simple docstring"""






							snake_case_       , snake_case_						       =	self._args_parser(__lowercase			,      __lowercase			,      __lowercase		)

							for i, (candidate_label, sequence_pair) in enumerate(zip(__lowercase			,      __lowercase		)		):
								snake_case_						       =	self._parse_and_tokenize([sequence_pair]		)

								yield {
								    "candidate_label": candidate_label,
								    "sequence": sequences[0],
								    "is_last": i == len(__lowercase		) - 1,
								    **model_input,
								}






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	inputs["candidate_label"]
							snake_case_						       =	inputs["sequence"]
							snake_case_						       =	{k: inputs[k] for k in self.tokenizer.model_input_names}
							snake_case_						       =	self.model(**__lowercase		)

							snake_case_						       =	{
							    "candidate_label": candidate_label,
							    "sequence": sequence,
							    "is_last": inputs["is_last"],
							    **outputs,
							}
							return model_outputs






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[Any]			,      __lowercase				:	List[Any]=False		):






							"""simple docstring"""






							snake_case_						       =	[outputs["candidate_label"] for outputs in model_outputs]
							snake_case_						       =	[outputs["sequence"] for outputs in model_outputs]
							snake_case_						       =	np.concatenate([output["logits"].numpy() for output in model_outputs]		)
							snake_case_						       =	logits.shape[0]
							snake_case_						       =	len(__lowercase		)
							snake_case_						       =	N // n
							snake_case_						       =	logits.reshape((num_sequences, n, -1)		)

							if multi_label or len(__lowercase		) == 1:
								# softmax over the entailment vs. contradiction dim for each label independently
								snake_case_						       =	self.entailment_id
								snake_case_						       =	-1 if entailment_id == 0 else 0
								snake_case_						       =	reshaped_outputs[..., [contradiction_id, entailment_id]]
								snake_case_						       =	np.exp(__lowercase		) / np.exp(__lowercase		).sum(-1			,      keepdims=__lowercase		)
								snake_case_						       =	scores[..., 1]
							else:
								# softmax the "entailment" logits over all candidate labels
								snake_case_						       =	reshaped_outputs[..., self.entailment_id]
								snake_case_						       =	np.exp(__lowercase		) / np.exp(__lowercase		).sum(-1			,      keepdims=__lowercase		)

							snake_case_						       =	list(reversed(scores[0].argsort()		)		)
							return {
							    "sequence": sequences[0],
							    "labels": [candidate_labels[i] for i in top_inds],
							    "scores": scores[0, top_inds].tolist(),
							}






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()
