


'''simple docstring'''






import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__A	:	List[Any]     =     logging.getLogger(__name__)
@dataclass
class 						__UpperCamelCase	:
							lowercase			:		str
							lowercase			:		List[str]
							lowercase			:		Optional[List[str]]





@dataclass
class 						__UpperCamelCase	:
							lowercase			:		List[int]
							lowercase			:		List[int]
							lowercase			:		Optional[List[int]]   		=  None
							lowercase			:		Optional[List[int]]   		=  None





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		List[str]   		=  'train'
							lowercase			:		int   		=  'dev'
							lowercase			:		Optional[Any]   		=  'test'





class 						__UpperCamelCase	:





							@staticmethod
							def 		a__   (  _UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Union[Split, str]			):
									raise NotImplementedError





							@staticmethod
							def 		a__   (  _UpperCamelCase		:str			):
									raise NotImplementedError





							@staticmethod
							def 		a__   (  _UpperCamelCase		:List[InputExample]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:PreTrainedTokenizer  ,_UpperCamelCase		:Tuple=False  ,_UpperCamelCase		:Union[str, Any]="[CLS]"  ,_UpperCamelCase		:Any=1  ,_UpperCamelCase		:int="[SEP]"  ,_UpperCamelCase		:Tuple=False  ,_UpperCamelCase		:Dict=False  ,_UpperCamelCase		:Optional[Any]=0  ,_UpperCamelCase		:Optional[Any]=0  ,_UpperCamelCase		:Union[str, Any]=-1_0_0  ,_UpperCamelCase		:List[Any]=0  ,_UpperCamelCase		:Tuple=True  ,):
									snake_case_		:							str			       =						{label: i for i, label in enumerate(_UpperCamelCase			)}

									snake_case_		:							Dict			       =						[]
									for ex_index, example in enumerate(_UpperCamelCase			):
											if ex_index % 1_0_0_0_0 == 0:
													logger.info("""Writing example %d of %d"""  ,_UpperCamelCase  ,len(_UpperCamelCase			)			)

											snake_case_		:							List[Any]			       =						[]
											snake_case_		:							List[Any]			       =						[]
											for word, label in zip(example.words  ,example.labels			):
													snake_case_		:							str			       =						tokenizer.tokenize(_UpperCamelCase			)

													# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
													if len(_UpperCamelCase			) > 0:
															tokens.extend(_UpperCamelCase			)
															# Use the real label id for the first token of the word, and padding ids for the remaining tokens
															label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_UpperCamelCase			) - 1)			)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											snake_case_		:							Dict			       =						tokenizer.num_special_tokens_to_add()
											if len(_UpperCamelCase			) > max_seq_length - special_tokens_count:
													snake_case_		:							Union[str, Any]			       =						tokens[: (max_seq_length - special_tokens_count)]
													snake_case_		:							List[str]			       =						label_ids[: (max_seq_length - special_tokens_count)]

											# The convention in BERT is:
											# (a) For sequence pairs:
											#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
											#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
											# (b) For single sequences:
											#  tokens:   [CLS] the dog is hairy . [SEP]
											#  type_ids:   0   0   0   0  0     0   0
											#
											# Where "type_ids" are used to indicate whether this is the first
											# sequence or the second sequence. The embedding vectors for `type=0` and
											# `type=1` were learned during pre-training and are added to the wordpiece
											# embedding vector (and position vector). This is not *strictly* necessary
											# since the [SEP] token unambiguously separates the sequences, but it makes
											# it easier for the model to learn the concept of sequences.
											#
											# For classification tasks, the first vector (corresponding to [CLS]) is
											# used as the "sentence vector". Note that this only makes sense because
											# the entire model is fine-tuned.
											tokens += [sep_token]
											label_ids += [pad_token_label_id]
											if sep_token_extra:
													# roberta uses an extra separator b/w pairs of sentences
													tokens += [sep_token]
													label_ids += [pad_token_label_id]
											snake_case_		:							Tuple			       =						[sequence_a_segment_id] * len(_UpperCamelCase			)

											if cls_token_at_end:
													tokens += [cls_token]
													label_ids += [pad_token_label_id]
													segment_ids += [cls_token_segment_id]
											else:
													snake_case_		:							List[str]			       =						[cls_token] + tokens
													snake_case_		:							Tuple			       =						[pad_token_label_id] + label_ids
													snake_case_		:							Tuple			       =						[cls_token_segment_id] + segment_ids

											snake_case_		:							Union[str, Any]			       =						tokenizer.convert_tokens_to_ids(_UpperCamelCase			)

											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											snake_case_		:							str			       =						[1 if mask_padding_with_zero else 0] * len(_UpperCamelCase			)

											# Zero-pad up to the sequence length.
											snake_case_		:							List[Any]			       =						max_seq_length - len(_UpperCamelCase			)
											if pad_on_left:
													snake_case_		:							List[str]			       =						([pad_token] * padding_length) + input_ids
													snake_case_		:							List[Any]			       =						([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
													snake_case_		:							int			       =						([pad_token_segment_id] * padding_length) + segment_ids
													snake_case_		:							Dict			       =						([pad_token_label_id] * padding_length) + label_ids
											else:
													input_ids += [pad_token] * padding_length
													input_mask += [0 if mask_padding_with_zero else 1] * padding_length
													segment_ids += [pad_token_segment_id] * padding_length
													label_ids += [pad_token_label_id] * padding_length

											assert len(_UpperCamelCase			) == max_seq_length
											assert len(_UpperCamelCase			) == max_seq_length
											assert len(_UpperCamelCase			) == max_seq_length
											assert len(_UpperCamelCase			) == max_seq_length

											if ex_index < 5:
													logger.info("""*** Example ***"""			)
													logger.info("""guid: %s"""  ,example.guid			)
													logger.info("""tokens: %s"""  ,""" """.join([str(_UpperCamelCase			) for x in tokens]			)			)
													logger.info("""input_ids: %s"""  ,""" """.join([str(_UpperCamelCase			) for x in input_ids]			)			)
													logger.info("""input_mask: %s"""  ,""" """.join([str(_UpperCamelCase			) for x in input_mask]			)			)
													logger.info("""segment_ids: %s"""  ,""" """.join([str(_UpperCamelCase			) for x in segment_ids]			)			)
													logger.info("""label_ids: %s"""  ,""" """.join([str(_UpperCamelCase			) for x in label_ids]			)			)

											if "token_type_ids" not in tokenizer.model_input_names:
													snake_case_		:							Optional[Any]			       =						None

											features.append(
											    InputFeatures(
											        input_ids=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,label_ids=_UpperCamelCase			)			)
									return features


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset
				class 						__UpperCamelCase	(						lowercase__							):
											lowercase			:		List[InputFeatures]
											lowercase			:		int   		=  nn.CrossEntropyLoss().ignore_index





											def __init__(  self		:str  ,_UpperCamelCase		:TokenClassificationTask  ,_UpperCamelCase		:str  ,_UpperCamelCase		:PreTrainedTokenizer  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[Any]=False  ,_UpperCamelCase		:Split = Split.train  ,):
													# Load data features from cache or dataset file
													snake_case_		:							Optional[int]			       =						os.path.join(
													    _UpperCamelCase  ,"""cached_{}_{}_{}""".format(mode.value  ,tokenizer.__class__.__name__  ,str(_UpperCamelCase			)			)  ,)

													# Make sure only the first process in distributed training processes the dataset,
													# and the others will use the cache.
													snake_case_		:							Optional[Any]			       =						cached_features_file + """.lock"""
													with FileLock(_UpperCamelCase			):
															if os.path.exists(_UpperCamelCase			) and not overwrite_cache:
																	logger.info(F'''Loading features from cached file {cached_features_file}'''			)
																	snake_case_		:							Optional[int]			       =						torch.load(_UpperCamelCase			)
															else:
																	logger.info(F'''Creating features from dataset file at {data_dir}'''			)
																	snake_case_		:							int			       =						token_classification_task.read_examples_from_file(_UpperCamelCase  ,_UpperCamelCase			)
																	# TODO clean up all this to leverage built-in features of tokenizers
																	snake_case_		:							Any			       =						token_classification_task.convert_examples_to_features(
																	    _UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,cls_token_at_end=bool(model_type in ["""xlnet"""]			)  ,cls_token=tokenizer.cls_token  ,cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0  ,sep_token=tokenizer.sep_token  ,sep_token_extra=_UpperCamelCase  ,pad_on_left=bool(tokenizer.padding_side == """left"""			)  ,pad_token=tokenizer.pad_token_id  ,pad_token_segment_id=tokenizer.pad_token_type_id  ,pad_token_label_id=self.pad_token_label_id  ,)
																	logger.info(F'''Saving features into cached file {cached_features_file}'''			)
																	torch.save(self.features  ,_UpperCamelCase			)





											def __len__(  self		:Any			):
													return len(self.features			)





											def __getitem__(  self		:Optional[int]  ,_UpperCamelCase		:Tuple			):
													return self.features[i]


if is_tf_available():
				import tensorflow as tf
				class 						__UpperCamelCase	:
											lowercase			:		List[InputFeatures]
											lowercase			:		int   		=  -1_0_0





											def __init__(  self		:Optional[int]  ,_UpperCamelCase		:TokenClassificationTask  ,_UpperCamelCase		:str  ,_UpperCamelCase		:PreTrainedTokenizer  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Split = Split.train  ,):
													snake_case_		:							Tuple			       =						token_classification_task.read_examples_from_file(_UpperCamelCase  ,_UpperCamelCase			)
													# TODO clean up all this to leverage built-in features of tokenizers
													snake_case_		:							int			       =						token_classification_task.convert_examples_to_features(
													    _UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,cls_token_at_end=bool(model_type in ["""xlnet"""]			)  ,cls_token=tokenizer.cls_token  ,cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0  ,sep_token=tokenizer.sep_token  ,sep_token_extra=_UpperCamelCase  ,pad_on_left=bool(tokenizer.padding_side == """left"""			)  ,pad_token=tokenizer.pad_token_id  ,pad_token_segment_id=tokenizer.pad_token_type_id  ,pad_token_label_id=self.pad_token_label_id  ,)

													def gen():
															for ex in self.features:
																	if ex.token_type_ids is None:
																			yield (
																			    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																			    ex.label_ids,
																			)
																	else:
																			yield (
																			    {
																			        "input_ids": ex.input_ids,
																			        "attention_mask": ex.attention_mask,
																			        "token_type_ids": ex.token_type_ids,
																			    },
																			    ex.label_ids,
																			)

													if "token_type_ids" not in tokenizer.model_input_names:
															snake_case_		:							List[str]			       =						tf.data.Dataset.from_generator(
															    _UpperCamelCase  ,({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)  ,(
															        {"""input_ids""": tf.TensorShape([None]			), """attention_mask""": tf.TensorShape([None]			)},
															        tf.TensorShape([None]			),
															    )  ,)
													else:
															snake_case_		:							Union[str, Any]			       =						tf.data.Dataset.from_generator(
															    _UpperCamelCase  ,({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)  ,(
															        {
															            """input_ids""": tf.TensorShape([None]			),
															            """attention_mask""": tf.TensorShape([None]			),
															            """token_type_ids""": tf.TensorShape([None]			),
															        },
															        tf.TensorShape([None]			),
															    )  ,)





											def 		a__   (  self		:int			):
													snake_case_		:							List[Any]			       =						self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

													return self.dataset





											def __len__(  self		:Dict			):
													return len(self.features			)





											def __getitem__(  self		:Dict  ,_UpperCamelCase		:str			):
													return self.features[i]



'''simple docstring'''






import numpy as np
import qiskit





def      UpperCAmelCase      (		lowerCamelCase_  :int = 8				,      lowerCamelCase_  :int | None = None ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						np.random.default_rng(seed=lowerCamelCase_ )

		# 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_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# The set of states Alice will prepare.
		snake_case_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# Measurement basis for Bob's qubits.
		snake_case_		:							List[Any]			       =						rng.integers(2				,      size=lowerCamelCase_ )

		# Quantum Circuit to simulate BB84
		snake_case_		:							Optional[int]			       =						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.
		snake_case_		:							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.
		snake_case_		:							Tuple			       =						qiskit.execute(lowerCamelCase_				,      lowerCamelCase_				,      shots=1				,      seed_simulator=lowerCamelCase_ )
		# Returns the result of measurement.
		snake_case_		:							Tuple			       =						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.
		snake_case_		:							Optional[Any]			       =						"""""".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.
		snake_case_		:							str			       =						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()