





"""simple docstring"""




import numpy as np





def        a_				(							_lowercase       ,						_lowercase="utf-8"       ,						_lowercase="surrogatepass"   ):
						_UpperCamelCase			:					Union[str, Any]     =    bin(int.from_bytes(text.encode(_lowercase       ,						_lowercase   )       ,						'''big'''   )   )[2:]
						return bits.zfill(8 * ((len(_lowercase   ) + 7) // 8)   )





def        a_				(							_lowercase       ,						_lowercase="utf-8"       ,						_lowercase="surrogatepass"   ):
						_UpperCamelCase			:					int     =    int(_lowercase       ,						2   )
						return n.to_bytes((n.bit_length() + 7) // 8       ,						'''big'''   ).decode(_lowercase       ,						_lowercase   ) or "\0"





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

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

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

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

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

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

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

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

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

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










import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  # Save pytorch-model
  _snake_case      : List[Any]														=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _snake_case      : Optional[Any]														=		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
  torch.save(model.state_dict()			,							lowerCAmelCase )
  print(F"""Save configuration file to {pytorch_config_dump_path}""" )
  with open(lowerCAmelCase			,							'w'			,							encoding='utf-8' ) as f:
    f.write(config.to_json_string() )


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--imagegpt_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the TensorFlow checkpoint path.""",
      )
      parser.add_argument(
          """--model_size""",
          default=None,
          type=str,
          required=True,
          help="""Size of the model (can be either 'small', 'medium' or 'large').""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )




