

import numpy as np
def   _lowerCAmelCase(						__A ,							__A="utf-8" ,							__A="surrogatepass"      ):
  UpperCAmelCase          =	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"      ):
  UpperCAmelCase          =	int(__A ,							2      )
  return n.to_bytes((n.bit_length() + 7) // 8 ,							"big"      ).decode(__A ,							__A      ) or "\0"
def   _lowerCAmelCase(						__A ,							__A      ):
  if size_par + len(__A      ) <= 2**size_par - (len(__A      ) - 1):
    raise ValueError("size of parity don't match with size of data"      )

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

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

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

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

  UpperCAmelCase          =	parity_received == parity
  return data_output, ack






from typing import Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )
  UpperCAmelCase   ,						UpperCAmelCase          =	_calculate_expected_aligned_error(
      alignment_confidence_breaks=__A ,							aligned_distance_error_probs=__A ,							)

  return {
      "aligned_confidence_probs": aligned_confidence_probs,
      "predicted_aligned_error": predicted_aligned_error,
      "max_predicted_aligned_error": max_predicted_aligned_error,
  }





def   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  torch.sum(__A      )
  UpperCAmelCase          =	logits.shape[-2]
  UpperCAmelCase          =	max(__A ,							19      )

  UpperCAmelCase          =	1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )

  UpperCAmelCase          =	1.0 / (1 + (bin_centers**2) / (da**2))
  UpperCAmelCase          =	torch.sum(probs * tm_per_bin ,							dim=-1      )

  UpperCAmelCase          =	residue_weights / (eps + residue_weights.sum())
  UpperCAmelCase          =	torch.sum(predicted_tm_term * normed_residue_mask ,							dim=-1      )

  UpperCAmelCase          =	per_alignment * residue_weights

  UpperCAmelCase          =	(weighted == torch.max(__A      )).nonzero()[0]
  return per_alignment[tuple(__A      )]



