


import numpy as np




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase="utf-8",    _UpperCamelCase="surrogatepass"     )    ->Tuple:







      """simple docstring"""

      lowercase			:   Any					   =		bin(int.from_bytes(text.encode(_UpperCamelCase,    _UpperCamelCase     ),    '''big'''     )     )[2:]
      return bits.zfill(8 * ((len(_UpperCamelCase     ) + 7) // 8)     )




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase="utf-8",    _UpperCamelCase="surrogatepass"     )    ->List[str]:







      """simple docstring"""

      lowercase			:   Dict					   =		int(_UpperCamelCase,    2     )
      return n.to_bytes((n.bit_length() + 7) // 8,    '''big'''     ).decode(_UpperCamelCase,    _UpperCamelCase     ) or "\0"




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Optional[int]:







      """simple docstring"""

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

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

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

      for x in range(1,    size_par + len(_UpperCamelCase     ) + 1     ):
            # Performs a template of bit positions - who should be given,
            # and who should be parity
            if qtd_bp < size_par:
                  if (np.log(_UpperCamelCase     ) / np.log(2     )).is_integer():
                        data_out_gab.append('''P'''     )
                        lowercase			:   str					   =		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(_UpperCamelCase     )

    # Calculates parity
      lowercase			:   List[str]					   =		0  # parity bit counter
      for bp in range(1,    size_par + 1     ):
            # Bit counter one for a given parity
            lowercase			:   int					   =		0
            # counter to control the loop reading
            lowercase			:   int					   =		0
            for x in data_ord:
                  if x is not None:
                        try:
                              lowercase			:   List[Any]					   =		(bin_pos[cont_loop])[-1 * (bp)]
                        except IndexError:
                              lowercase			:   Any					   =		'''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			:   Tuple					   =		0  # parity bit counter
      for x in range(0,    size_par + len(_UpperCamelCase     )     ):
            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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Union[str, Any]:







      """simple docstring"""

      lowercase			:   Dict					   =		[]
      # Parity bit counter
      lowercase			:   Any					   =		0
      # Counter p data bit reading
      lowercase			:   List[str]					   =		0
      # list of parity received
      lowercase			:   Tuple					   =		[]
      lowercase			:   Any					   =		[]

      for x in range(1,    len(_UpperCamelCase     ) + 1     ):
            # Performs a template of bit positions - who should be given,
            #  and who should be parity
            if qtd_bp < size_par and (np.log(_UpperCamelCase     ) / np.log(2     )).is_integer():
                  data_out_gab.append('''P'''     )
                  lowercase			:   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_output.append(data[cont_data]     )
            else:
                  parity_received.append(data[cont_data]     )
            cont_data += 1

      # -----------calculates the parity with the data
      lowercase			:   Dict					   =		[]
      lowercase			:   int					   =		[]
      lowercase			:   List[Any]					   =		[bin(_UpperCamelCase     )[2:] for x in range(1,    size_par + len(_UpperCamelCase     ) + 1     )]

      #  sorted information data for the size of the output data
      lowercase			:   Any					   =		[]
      # Data position feedback + parity
      lowercase			:   Dict					   =		[]
      # Parity bit counter
      lowercase			:   Optional[Any]					   =		0
      # Counter p data bit reading
      lowercase			:   List[Any]					   =		0

      for x in range(1,    size_par + len(_UpperCamelCase     ) + 1     ):
            # Performs a template position of bits - who should be given,
            # and who should be parity
            if qtd_bp < size_par and (np.log(_UpperCamelCase     ) / np.log(2     )).is_integer():
                  data_out_gab.append('''P'''     )
                  lowercase			:   List[str]					   =		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(_UpperCamelCase     )

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

      lowercase			:   int					   =		parity_received == parity
      return data_output, ack





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   				=      {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   				=      [
      '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DecisionTransformerGPT2Model''',
      '''DecisionTransformerGPT2PreTrainedModel''',
      '''DecisionTransformerModel''',
      '''DecisionTransformerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DecisionTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DecisionTransformerGPTaModel,
        DecisionTransformerGPTaPreTrainedModel,
        DecisionTransformerModel,
        DecisionTransformerPreTrainedModel,
    )


else:
  import sys

  __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

