

"""simple docstring"""
import numpy as np







def       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					str="utf-8"     , lowerCamelCase							:					int="surrogatepass"   )						->     str:



       """simple docstring"""







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







def       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Union[str, Any]="utf-8"     , lowerCamelCase							:					List[str]="surrogatepass"   )						->     List[Any]:



       """simple docstring"""







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







def       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Optional[Any]   )						->     List[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__			:		List[Any]        =     []
       __magic_name__			:		Any        =     []
       __magic_name__			:		Optional[int]        =     [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__			:		Optional[Any]        =     []
       # data position template + parity
       __magic_name__			:		Dict        =     []
       # parity bit counter
       __magic_name__			:		str        =     0
       # counter position of data bits
       __magic_name__			:		Tuple        =     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__			:		Tuple        =     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__			:		Optional[Any]        =     0  # parity bit counter
       for bp in range(1     , size_par + 1   ):
              # Bit counter one for a given parity
              __magic_name__			:		Dict        =     0
              # counter to control the loop reading
              __magic_name__			:		Tuple        =     0
              for x in data_ord:
                     if x is not None:
                            try:
                                   __magic_name__			:		Any        =     (bin_pos[cont_loop])[-1 * (bp)]
                            except IndexError:
                                   __magic_name__			:		Optional[int]        =     '''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__			:		Tuple        =     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       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Any   )						->     Optional[int]:



       """simple docstring"""







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

       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__			:		List[Any]        =     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__			:		Optional[int]        =     []
       __magic_name__			:		str        =     []
       __magic_name__			:		Union[str, Any]        =     [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__			:		List[str]        =     []
       # Data position feedback + parity
       __magic_name__			:		Any        =     []
       # Parity bit counter
       __magic_name__			:		str        =     0
       # Counter p data bit reading
       __magic_name__			:		str        =     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__			:		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_ord.append(data_output[cont_data]   )
                     cont_data += 1
              else:
                     data_ord.append(lowerCamelCase   )

    # Calculates parity
       __magic_name__			:		Optional[Any]        =     0  # parity bit counter
       for bp in range(1     , size_par + 1   ):
              # Bit counter one for a certain parity
              __magic_name__			:		Any        =     0
              # Counter to control loop reading
              __magic_name__			:		List[Any]        =     0
              for x in data_ord:
                     if x is not None:
                            try:
                                   __magic_name__			:		Optional[Any]        =     (bin_pos[cont_loop])[-1 * (bp)]
                            except IndexError:
                                   __magic_name__			:		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
       __magic_name__			:		Optional[Any]        =     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__			:		str        =     parity_received == parity
       return data_output, ack



"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
