





import numpy as np


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__="utf-8"       ,lowerCAmelCase__="surrogatepass"	):
		lowerCamelCase_  :    int   	=			bin(int.from_bytes(text.encode(lowerCAmelCase__       ,lowerCAmelCase__	)       ,'big'	)	)[2:]
		return bits.zfill(8 * ((len(lowerCAmelCase__	) + 7) // 8)	)


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__="utf-8"       ,lowerCAmelCase__="surrogatepass"	):
		lowerCamelCase_  :    str   	=			int(lowerCAmelCase__       ,2	)
		return n.to_bytes((n.bit_length() + 7) // 8       ,'big'	).decode(lowerCAmelCase__       ,lowerCAmelCase__	) or "\0"


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		if size_par + len(lowerCAmelCase__	) <= 2**size_par - (len(lowerCAmelCase__	) - 1):
				raise ValueError('size of parity don\'t match with size of data'	)

		lowerCamelCase_  :    List[Any]   	=			[]
		lowerCamelCase_  :    List[str]   	=			[]
		lowerCamelCase_  :    Any   	=			[bin(lowerCAmelCase__	)[2:] for x in range(1       ,size_par + len(lowerCAmelCase__	) + 1	)]

		# sorted information data for the size of the output data
		lowerCamelCase_  :    Tuple   	=			[]
		# data position template + parity
		lowerCamelCase_  :    Dict   	=			[]
		# parity bit counter
		lowerCamelCase_  :    Any   	=			0
		# counter position of data bits
		lowerCamelCase_  :    Any   	=			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'	)
								lowerCamelCase_  :    Union[str, 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
		lowerCamelCase_  :    List[str]   	=			0  # parity bit counter
		for bp in range(1       ,size_par + 1	):
				# Bit counter one for a given parity
				lowerCamelCase_  :    str   	=			0
				# counter to control the loop reading
				lowerCamelCase_  :    List[str]   	=			0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase_  :    List[str]   	=			(bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase_  :    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
		lowerCamelCase_  :    Dict   	=			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 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    str   	=			[]
		# Parity bit counter
		lowerCamelCase_  :    Any   	=			0
		# Counter p data bit reading
		lowerCamelCase_  :    Union[str, Any]   	=			0
		# list of parity received
		lowerCamelCase_  :    Tuple   	=			[]
		lowerCamelCase_  :    Any   	=			[]

		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'	)
						lowerCamelCase_  :    Any   	=			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
		lowerCamelCase_  :    Optional[Any]   	=			[]
		lowerCamelCase_  :    Tuple   	=			[]
		lowerCamelCase_  :    Dict   	=			[bin(lowerCAmelCase__	)[2:] for x in range(1       ,size_par + len(lowerCAmelCase__	) + 1	)]

		#  sorted information data for the size of the output data
		lowerCamelCase_  :    Any   	=			[]
		# Data position feedback + parity
		lowerCamelCase_  :    Any   	=			[]
		# Parity bit counter
		lowerCamelCase_  :    Tuple   	=			0
		# Counter p data bit reading
		lowerCamelCase_  :    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'	)
						lowerCamelCase_  :    Union[str, Any]   	=			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
		lowerCamelCase_  :    Dict   	=			0  # parity bit counter
		for bp in range(1       ,size_par + 1	):
				# Bit counter one for a certain parity
				lowerCamelCase_  :    Optional[int]   	=			0
				# Counter to control loop reading
				lowerCamelCase_  :    int   	=			0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase_  :    Optional[Any]   	=			(bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase_  :    int   	=			'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
		lowerCamelCase_  :    Dict   	=			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]	)

		lowerCamelCase_  :    List[str]   	=			parity_received == parity
		return data_output, ack







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,
)


_lowercase		:     int 					={
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Any 					=["""MarianTokenizer"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Any 					=[
		    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MarianForCausalLM""",
		    """MarianModel""",
		    """MarianMTModel""",
		    """MarianPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Dict 					=["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Optional[Any] 					=["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_marian import MarianTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_marian import (
				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MarianForCausalLM,
				    MarianModel,
				    MarianMTModel,
				    MarianPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
		import sys

		_lowercase		:     List[str] 					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
