code: import numpy as np
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	bin(int.from_bytes(text.encode(_A ,_A					) ,"""big"""					)					)[2:]
  							return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
  
  
  def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	int(_A ,2					)
  							return n.to_bytes((n.bit_length() + 7) // 8 ,"""big"""					).decode(_A ,_A					) or "\0"
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
  														raise ValueError("""size of parity don't match with size of data"""					)
  
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							# sorted information data for the size of the output data
  							_lowercase				=	[]
  							# data position template + parity
  							_lowercase				=	[]
  							# parity bit counter
  							_lowercase				=	0
  							# counter position of data bits
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par:
  																					if (np.log(_A					) / np.log(2					)).is_integer():
  																												data_out_gab.append("""P"""					)
  																												_lowercase				=	qtd_bp + 1
  																					else:
  																												data_out_gab.append("""D"""					)
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a given parity
  														_lowercase				=	0
  														# counter to control the loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(cont_bo % 2					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							return data_out
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  							# list of parity received
  							_lowercase				=	[]
  							_lowercase				=	[]
  
  							for x in range(1 ,len(_A					) + 1					):
  														# Performs a template of bit positions - who should be given,
  														#  and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_output.append(data[cont_data]					)
  														else:
  																					parity_received.append(data[cont_data]					)
  														cont_data += 1
  
  							# -----------calculates the parity with the data
  							_lowercase				=	[]
  							_lowercase				=	[]
  							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]
  
  							#  sorted information data for the size of the output data
  							_lowercase				=	[]
  							# Data position feedback + parity
  							_lowercase				=	[]
  							# Parity bit counter
  							_lowercase				=	0
  							# Counter p data bit reading
  							_lowercase				=	0
  
  							for x in range(1 ,size_par + len(_A					) + 1					):
  														# Performs a template position of bits - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
  																					data_out_gab.append("""P"""					)
  																					_lowercase				=	qtd_bp + 1
  														else:
  																					data_out_gab.append("""D"""					)
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data_output[cont_data]					)
  																					cont_data += 1
  														else:
  																					data_ord.append(_A					)
  
      # Calculates parity
  							_lowercase				=	0  # parity bit counter
  							for bp in range(1 ,size_par + 1					):
  														# Bit counter one for a certain parity
  														_lowercase				=	0
  														# Counter to control loop reading
  														_lowercase				=	0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			_lowercase				=	"""0"""
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(str(cont_bo % 2					)					)
  
  														qtd_bp += 1
  
  							# Mount the message
  							_lowercase				=	0  # Parity bit counter
  							for x in range(0 ,size_par + len(_A					)					):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp]					)					)
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x]					)
  
  							_lowercase				=	parity_received == parity
  							return data_output, ack
  
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  