






"""simple docstring"""

import numpy as np






def _lowerCAmelCase						(	__lowerCamelCase:Optional[int]			,    __lowerCamelCase:Any="utf-8"			,    __lowerCamelCase:List[Any]="surrogatepass" ):

						'''simple docstring'''






						__magic_name__      =  bin(int.from_bytes(text.encode(_lowerCAmelCase			,    _lowerCAmelCase )			,    "big" ) )[2:]
						return bits.zfill(8 * ((len(_lowerCAmelCase ) + 7) // 8) )






def _lowerCAmelCase						(	__lowerCamelCase:Optional[Any]			,    __lowerCamelCase:Tuple="utf-8"			,    __lowerCamelCase:Tuple="surrogatepass" ):

						'''simple docstring'''






						__magic_name__      =  int(_lowerCAmelCase			,    2 )
						return n.to_bytes((n.bit_length() + 7) // 8			,    "big" ).decode(_lowerCAmelCase			,    _lowerCAmelCase ) or "\0"






def _lowerCAmelCase						(	__lowerCamelCase:Dict			,    __lowerCamelCase:str ):

						'''simple docstring'''






						if size_par + len(_lowerCAmelCase ) <= 2**size_par - (len(_lowerCAmelCase ) - 1):
												raise ValueError("size of parity don't match with size of data" )

						__magic_name__      =  []
						__magic_name__      =  []
						__magic_name__      =  [bin(_lowerCAmelCase )[2:] for x in range(1			,    size_par + len(_lowerCAmelCase ) + 1 )]

						# sorted information data for the size of the output data
						__magic_name__      =  []
						# data position template + parity
						__magic_name__      =  []
						# parity bit counter
						__magic_name__      =  0
						# counter position of data bits
						__magic_name__      =  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" )
																								__magic_name__      =  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
						__magic_name__      =  0  # parity bit counter
						for bp in range(1			,    size_par + 1 ):
												# Bit counter one for a given parity
												__magic_name__      =  0
												# counter to control the loop reading
												__magic_name__      =  0
												for x in data_ord:
																		if x is not None:
																								try:
																														__magic_name__      =  (bin_pos[cont_loop])[-1 * (bp)]
																								except IndexError:
																														__magic_name__      =  "0"
																								if aux == "1" and x == "1":
																														cont_bo += 1
																		cont_loop += 1
												parity.append(cont_bo % 2 )

												qtd_bp += 1

						# Mount the message
						__magic_name__      =  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 _lowerCAmelCase						(	__lowerCamelCase:Any			,    __lowerCamelCase:List[str] ):

						'''simple docstring'''






						__magic_name__      =  []
						# Parity bit counter
						__magic_name__      =  0
						# Counter p data bit reading
						__magic_name__      =  0
						# list of parity received
						__magic_name__      =  []
						__magic_name__      =  []

						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" )
																		__magic_name__      =  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
						__magic_name__      =  []
						__magic_name__      =  []
						__magic_name__      =  [bin(_lowerCAmelCase )[2:] for x in range(1			,    size_par + len(_lowerCAmelCase ) + 1 )]

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

						__magic_name__      =  parity_received == parity
						return data_output, ack




"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A       			=      {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      ["""RealmTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      [
     """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RealmEmbedder""",
     """RealmForOpenQA""",
     """RealmKnowledgeAugEncoder""",
     """RealmPreTrainedModel""",
     """RealmReader""",
     """RealmScorer""",
     """load_tf_weights_in_realm""",
 ]
 __A       			=      ["""RealmRetriever"""]


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 __A       			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
