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



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__)


