"""simple docstring"""


import numpy as np


def   __UpperCAmelCase     (	_snake_case		:       List[Any],				_snake_case		:       List[Any]="utf-8",				_snake_case		:       Optional[int]="surrogatepass"       ):
   _lowercase							     =       bin(int.from_bytes(text.encode(_snake_case,				_snake_case       ),				"big"       )       )[2:]
   return bits.zfill(8 * ((len(_snake_case       ) + 7) // 8)       )


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       List[Any]="utf-8",				_snake_case		:       Dict="surrogatepass"       ):
   _lowercase							     =       int(_snake_case,				2       )
   return n.to_bytes((n.bit_length() + 7) // 8,				"big"       ).decode(_snake_case,				_snake_case       ) or "\0"


def   __UpperCAmelCase     (	_snake_case		:       Optional[int],				_snake_case		:       Optional[int]       ):
   if size_par + len(_snake_case       ) <= 2**size_par - (len(_snake_case       ) - 1):
      raise ValueError("size of parity don't match with size of data"       )

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

   # sorted information data for the size of the output data
   _lowercase							     =       []
   # data position template + parity
   _lowercase							     =       []
   # parity bit counter
   _lowercase							     =       0
   # counter position of data bits
   _lowercase							     =       0

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

    # Calculates parity
   _lowercase							     =       0  # parity bit counter
   for bp in range(1,				size_par + 1       ):
      # Bit counter one for a given parity
      _lowercase							     =       0
      # counter to control the loop reading
      _lowercase							     =       0
      for x in data_ord:
         if x is not None:
            try:
               _lowercase							     =       (bin_pos[cont_loop])[-1 * (bp)]
            except IndexError:
               _lowercase							     =       "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							     =       0  # parity bit counter
   for x in range(0,				size_par + len(_snake_case       )       ):
      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     (	_snake_case		:       Optional[int],				_snake_case		:       List[Any]       ):
   _lowercase							     =       []
   # Parity bit counter
   _lowercase							     =       0
   # Counter p data bit reading
   _lowercase							     =       0
   # list of parity received
   _lowercase							     =       []
   _lowercase							     =       []

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

   #  sorted information data for the size of the output data
   _lowercase							     =       []
   # Data position feedback + parity
   _lowercase							     =       []
   # Parity bit counter
   _lowercase							     =       0
   # Counter p data bit reading
   _lowercase							     =       0

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

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

   _lowercase							     =       parity_received == parity
   return data_output, ack
"""simple docstring"""


from typing import TYPE_CHECKING

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


__UpperCamelCase       :    Any         =		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Optional[int]         =		["OpenAIGPTTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    List[Any]         =		[
      "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OpenAIGPTDoubleHeadsModel",
      "OpenAIGPTForSequenceClassification",
      "OpenAIGPTLMHeadModel",
      "OpenAIGPTModel",
      "OpenAIGPTPreTrainedModel",
      "load_tf_weights_in_openai_gpt",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Tuple         =		[
      "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFOpenAIGPTDoubleHeadsModel",
      "TFOpenAIGPTForSequenceClassification",
      "TFOpenAIGPTLMHeadModel",
      "TFOpenAIGPTMainLayer",
      "TFOpenAIGPTModel",
      "TFOpenAIGPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

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