

"""simple docstring"""






import numpy as np
def       UpperCamelCase					(			_lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						Optional[int]="utf-8",  _lowerCAmelCase     :						str="surrogatepass" )				->			Optional[Any]:
	_UpperCAmelCase    :							Tuple									=					bin(int.from_bytes(text.encode(_lowerCAmelCase,  _lowerCAmelCase ),  """big""" ) )[2:]
	return bits.zfill(8 * ((len(_lowerCAmelCase ) + 7) // 8) )
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						int="utf-8",  _lowerCAmelCase     :						List[str]="surrogatepass" )				->			Tuple:
	_UpperCAmelCase    :							Optional[Any]									=					int(_lowerCAmelCase,  2 )
	return n.to_bytes((n.bit_length() + 7) // 8,  """big""" ).decode(_lowerCAmelCase,  _lowerCAmelCase ) or "\0"
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						Any )				->			Optional[int]:
	if size_par + len(_lowerCAmelCase ) <= 2**size_par - (len(_lowerCAmelCase ) - 1):
		raise ValueError("""size of parity don't match with size of data""" )

	_UpperCAmelCase    :							int									=					[]
	_UpperCAmelCase    :							str									=					[]
	_UpperCAmelCase    :							Any									=					[bin(_lowerCAmelCase )[2:] for x in range(1,  size_par + len(_lowerCAmelCase ) + 1 )]

	# sorted information data for the size of the output data
	_UpperCAmelCase    :							List[str]									=					[]
	# data position template + parity
	_UpperCAmelCase    :							Any									=					[]
	# parity bit counter
	_UpperCAmelCase    :							List[Any]									=					0
	# counter position of data bits
	_UpperCAmelCase    :							Dict									=					0

	for x in range(1,  size_par + len(_lowerCAmelCase ) + 1 ):
		# Performs a template of bit positions - who should be given,
		# and who should be parity
		if qtd_bp < size_par:
			if (np.log(_lowerCAmelCase ) / np.log(2 )).is_integer():
				data_out_gab.append("""P""" )
				_UpperCAmelCase    :							Optional[Any]									=					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(_lowerCAmelCase )

    # Calculates parity
	_UpperCAmelCase    :							List[Any]									=					0  # parity bit counter
	for bp in range(1,  size_par + 1 ):
		# Bit counter one for a given parity
		_UpperCAmelCase    :							Optional[int]									=					0
		# counter to control the loop reading
		_UpperCAmelCase    :							Optional[int]									=					0
		for x in data_ord:
			if x is not None:
				try:
					_UpperCAmelCase    :							Any									=					(bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					_UpperCAmelCase    :							List[str]									=					"""0"""
				if aux == "1" and x == "1":
					cont_bo += 1
			cont_loop += 1
		parity.append(cont_bo % 2 )

		qtd_bp += 1

	# Mount the message
	_UpperCAmelCase    :							str									=					0  # parity bit counter
	for x in range(0,  size_par + len(_lowerCAmelCase ) ):
		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       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int )				->			List[Any]:
	_UpperCAmelCase    :							Dict									=					[]
	# Parity bit counter
	_UpperCAmelCase    :							int									=					0
	# Counter p data bit reading
	_UpperCAmelCase    :							Tuple									=					0
	# list of parity received
	_UpperCAmelCase    :							str									=					[]
	_UpperCAmelCase    :							int									=					[]

	for x in range(1,  len(_lowerCAmelCase ) + 1 ):
		# Performs a template of bit positions - who should be given,
		#  and who should be parity
		if qtd_bp < size_par and (np.log(_lowerCAmelCase ) / np.log(2 )).is_integer():
			data_out_gab.append("""P""" )
			_UpperCAmelCase    :							Dict									=					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
	_UpperCAmelCase    :							int									=					[]
	_UpperCAmelCase    :							Optional[Any]									=					[]
	_UpperCAmelCase    :							str									=					[bin(_lowerCAmelCase )[2:] for x in range(1,  size_par + len(_lowerCAmelCase ) + 1 )]

	#  sorted information data for the size of the output data
	_UpperCAmelCase    :							List[str]									=					[]
	# Data position feedback + parity
	_UpperCAmelCase    :							Tuple									=					[]
	# Parity bit counter
	_UpperCAmelCase    :							Optional[int]									=					0
	# Counter p data bit reading
	_UpperCAmelCase    :							Tuple									=					0

	for x in range(1,  size_par + len(_lowerCAmelCase ) + 1 ):
		# Performs a template position of bits - who should be given,
		# and who should be parity
		if qtd_bp < size_par and (np.log(_lowerCAmelCase ) / np.log(2 )).is_integer():
			data_out_gab.append("""P""" )
			_UpperCAmelCase    :							Tuple									=					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(_lowerCAmelCase )

    # Calculates parity
	_UpperCAmelCase    :							Tuple									=					0  # parity bit counter
	for bp in range(1,  size_par + 1 ):
		# Bit counter one for a certain parity
		_UpperCAmelCase    :							Optional[Any]									=					0
		# Counter to control loop reading
		_UpperCAmelCase    :							Optional[int]									=					0
		for x in data_ord:
			if x is not None:
				try:
					_UpperCAmelCase    :							str									=					(bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					_UpperCAmelCase    :							Tuple									=					"""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
	_UpperCAmelCase    :							Any									=					0  # Parity bit counter
	for x in range(0,  size_par + len(_lowerCAmelCase ) ):
		if data_ord[x] is None:
			data_out.append(str(parity[cont_bp] ) )
			cont_bp += 1
		else:
			data_out.append(data_ord[x] )

	_UpperCAmelCase    :							Dict									=					parity_received == parity
	return data_output, ack








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__				:  Optional[int]													=  {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  ['''T5Tokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[str]													=  ['''T5TokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Optional[Any]													=  [
						    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''T5EncoderModel''',
						    '''T5ForConditionalGeneration''',
						    '''T5Model''',
						    '''T5PreTrainedModel''',
						    '''load_tf_weights_in_t5''',
						    '''T5ForQuestionAnswering''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  int													=  [
						    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFT5EncoderModel''',
						    '''TFT5ForConditionalGeneration''',
						    '''TFT5Model''',
						    '''TFT5PreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''FlaxT5EncoderModel''',
						    '''FlaxT5ForConditionalGeneration''',
						    '''FlaxT5Model''',
						    '''FlaxT5PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta import TaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta_fast import TaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ta import (
												    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TaEncoderModel,
												    TaForConditionalGeneration,
												    TaForQuestionAnswering,
												    TaModel,
												    TaPreTrainedModel,
												    load_tf_weights_in_ta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_ta import (
												    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTaEncoderModel,
												    TFTaForConditionalGeneration,
												    TFTaModel,
												    TFTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_ta import (
												    FlaxTaEncoderModel,
												    FlaxTaForConditionalGeneration,
												    FlaxTaModel,
												    FlaxTaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__				:  Dict													=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





