





import numpy as np
import qiskit


def 							SCREAMING_SNAKE_CASE__  (      __a = 8				,	__a = None				):
	snake_case_						:     Optional[int]     =		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_						:     Union[str, Any]     =		6 * key_len
	# Measurement basis for Alice's qubits.
	snake_case_						:     int     =		rng.integers(2				,	size=__a				)
	# The set of states Alice will prepare.
	snake_case_						:     Union[str, Any]     =		rng.integers(2				,	size=__a				)
	# Measurement basis for Bob's qubits.
	snake_case_						:     Optional[int]     =		rng.integers(2				,	size=__a				)

	# Quantum Circuit to simulate BB84
	snake_case_						:     List[str]     =		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_						:     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.
	snake_case_						:     Union[str, Any]     =		qiskit.execute(__a				,	__a				,	shots=1				,	seed_simulator=__a				)
	# Returns the result of measurement.
	snake_case_						:     Any     =		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_						:     List[Any]     =		''.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_						:     Union[str, Any]     =		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()








from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     int	 =    MBartConfig
						__magic_name__:     str	 =    {}
						__magic_name__:     Union[str, Any]	 =    "gelu"






						def __init__( self : List[str]						,				_A : Optional[int]						,				_A : List[Any]=13						,				_A : List[Any]=7						,				_A : Dict=True						,				_A : Tuple=False						,				_A : Optional[Any]=99						,				_A : Dict=32						,				_A : str=2						,				_A : str=4						,				_A : Tuple=37						,				_A : Tuple=0.1						,				_A : Union[str, Any]=0.1						,				_A : Optional[int]=20						,				_A : Dict=2						,				_A : List[str]=1						,				_A : Union[str, Any]=0						,				)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     str     =		parent
							snake_case_						:     List[str]     =		batch_size
							snake_case_						:     List[str]     =		seq_length
							snake_case_						:     Union[str, Any]     =		is_training
							snake_case_						:     Optional[int]     =		use_labels
							snake_case_						:     Dict     =		vocab_size
							snake_case_						:     Union[str, Any]     =		hidden_size
							snake_case_						:     str     =		num_hidden_layers
							snake_case_						:     Optional[Any]     =		num_attention_heads
							snake_case_						:     Union[str, Any]     =		intermediate_size
							snake_case_						:     Any     =		hidden_dropout_prob
							snake_case_						:     Any     =		attention_probs_dropout_prob
							snake_case_						:     Optional[Any]     =		max_position_embeddings
							snake_case_						:     Optional[Any]     =		eos_token_id
							snake_case_						:     Tuple     =		pad_token_id
							snake_case_						:     int     =		bos_token_id






						def UpperCAmelCase_			( self : List[str]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		ids_tensor([self.batch_size, self.seq_length - 1]						,				self.vocab_size		)
							snake_case_						:     int     =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)						,				1		)
							snake_case_						:     Dict     =		tf.concat([input_ids, eos_tensor]						,				axis=1		)

							snake_case_						:     Any     =		ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size		)

							snake_case_						:     Optional[Any]     =		self.config_cls(
							    vocab_size=self.vocab_size						,				d_model=self.hidden_size						,				encoder_layers=self.num_hidden_layers						,				decoder_layers=self.num_hidden_layers						,				encoder_attention_heads=self.num_attention_heads						,				decoder_attention_heads=self.num_attention_heads						,				encoder_ffn_dim=self.intermediate_size						,				decoder_ffn_dim=self.intermediate_size						,				dropout=self.hidden_dropout_prob						,				attention_dropout=self.attention_probs_dropout_prob						,				max_position_embeddings=self.max_position_embeddings						,				eos_token_ids=[2]						,				bos_token_id=self.bos_token_id						,				pad_token_id=self.pad_token_id						,				decoder_start_token_id=self.pad_token_id						,				**self.config_updates						,				)
							snake_case_						:     Union[str, Any]     =		prepare_mbart_inputs_dict(_A						,				_A						,				_A		)
							return config, inputs_dict






						def UpperCAmelCase_			( self : Optional[Any]						,				_A : Optional[Any]						,				_A : int		)							->      str:







							"""simple docstring"""


							snake_case_						:     Dict     =		TFMBartModel(config=_A		).get_decoder()
							snake_case_						:     Any     =		inputs_dict['input_ids']

							snake_case_						:     List[Any]     =		input_ids[:1, :]
							snake_case_						:     Dict     =		inputs_dict['attention_mask'][:1, :]
							snake_case_						:     Tuple     =		inputs_dict['head_mask']
							snake_case_						:     List[Any]     =		1

							# first forward pass
							snake_case_						:     Any     =		model(_A						,				attention_mask=_A						,				head_mask=_A						,				use_cache=_A		)

							snake_case_  ,snake_case_						:     str     =		outputs.to_tuple()
							snake_case_						:     int     =		past_key_values[1]


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a=None				,	__a=None				,	__a=None				,	__a=None				,	__a=None				,	):
	if attention_mask is None:
		snake_case_						:     Optional[int]     =		tf.cast(tf.math.not_equal(__a				,	config.pad_token_id				)				,	tf.inta				)
	if decoder_attention_mask is None:
		snake_case_						:     str     =		tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape				,	dtype=tf.inta				),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,	config.pad_token_id				)				,	tf.inta				),
		    ]				,	axis=-1				,	)
	if head_mask is None:
		snake_case_						:     str     =		tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
	if decoder_head_mask is None:
		snake_case_						:     int     =		tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
	if cross_attn_head_mask is None:
		snake_case_						:     Dict     =		tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}
@require_tf
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Tuple	 =    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						__magic_name__:     int	 =    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
						__magic_name__:     Union[str, Any]	 =    (
						    {
						        "conversational": TFMBartForConditionalGeneration,
						        "feature-extraction": TFMBartModel,
						        "summarization": TFMBartForConditionalGeneration,
						        "text2text-generation": TFMBartForConditionalGeneration,
						        "translation": TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						__magic_name__:     Tuple	 =    True
						__magic_name__:     Tuple	 =    False
						__magic_name__:     Any	 =    False






						def UpperCAmelCase_			( self : Any						,				_A : Union[str, Any]						,				_A : List[Any]						,				_A : str						,				_A : int						,				_A : Dict		)							->      Union[str, Any]:







							"""simple docstring"""


							if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
								# Exception encountered when calling layer '...'
								return True

							return False






						def UpperCAmelCase_			( self : Dict		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		TFMBartModelTester(self		)
							snake_case_						:     List[Any]     =		ConfigTester(self						,				config_class=_A		)






						def UpperCAmelCase_			( self : Optional[Any]		)							->      str:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Dict     =		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_A		)
@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):
						__magic_name__:     Optional[int]	 =    [
						    " UN Chief Says There Is No Military Solution in Syria",
						]
						__magic_name__:     Union[str, Any]	 =    [
						    "Şeful ONU declară că nu există o soluţie militară în Siria",
						]
						__magic_name__:     List[Any]	 =    "facebook/mbart-large-en-ro"






						@cached_property
						def UpperCAmelCase_			( self : str		)							->      List[Any]:







							"""simple docstring"""


							return AutoTokenizer.from_pretrained(self.model_name		)






						@cached_property
						def UpperCAmelCase_			( self : List[Any]		)							->      Any:







							"""simple docstring"""


							snake_case_						:     List[str]     =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
							return model






						def UpperCAmelCase_			( self : Optional[int]						,				**_A : str		)							->      int:







							"""simple docstring"""


							snake_case_						:     List[str]     =		self.translate_src_text(**_A		)
							self.assertListEqual(self.expected_text						,				_A		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				**_A : Dict		)							->      int:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.tokenizer(self.src_text						,				**_A						,				return_tensors='tf'		)
							snake_case_						:     int     =		self.model.generate(
							    model_inputs.input_ids						,				attention_mask=model_inputs.attention_mask						,				num_beams=2		)
							snake_case_						:     Any     =		self.tokenizer.batch_decode(_A						,				skip_special_tokens=_A		)
							return generated_words






						@slow
						def UpperCAmelCase_			( self : str		)							->      List[str]:







							"""simple docstring"""


							self._assert_generated_batch_equal_expected()

