





'''simple docstring'''

import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


lowerCAmelCase__	:					Optional[Any]	     =       collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)


def 		__UpperCamelCase		(       _UpperCAmelCase   ):
		print(F"Reading saved model from {model_file}"   )
		__UpperCAmelCase     :				int					   =			torch.load(_UpperCamelCase,    map_location=lambda _UpperCAmelCase,    _UpperCAmelCase   : default_restore_location(_UpperCamelCase,    "cpu"   )   )
		return CheckpointState(**_UpperCamelCase   )


class 			SCREAMING_SNAKE_CASE__	:







					"""simple docstring"""






					def __init__(					self    :	Dict						,     UpperCAmelCase_    :	List[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				int					   =			src_file
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						):







							"""simple docstring"""
							raise NotImplementedError






					@staticmethod
					def 						lowerCamelCase_					(					UpperCAmelCase_    :	str						,     *UpperCAmelCase_    :	Union[str, Any]						,     **UpperCAmelCase_    :	Union[str, Any]						):







							"""simple docstring"""
							if comp_type.startswith("c"						):
									return DPRContextEncoderState(*_SCREAMING_SNAKE_CASE						,     **_SCREAMING_SNAKE_CASE						)
							if comp_type.startswith("q"						):
									return DPRQuestionEncoderState(*_SCREAMING_SNAKE_CASE						,     **_SCREAMING_SNAKE_CASE						)
							if comp_type.startswith("r"						):
									return DPRReaderState(*_SCREAMING_SNAKE_CASE						,     **_SCREAMING_SNAKE_CASE						)
							else:
									raise ValueError("Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'."						)






class 			SCREAMING_SNAKE_CASE__	(					_UpperCamelCase						):







					"""simple docstring"""






					def 						lowerCamelCase_					(					self    :	Optional[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Tuple					   =			DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
							print(f"Loading DPR biencoder from {self.src_file}"						)
							__UpperCAmelCase     :				Tuple					   =			load_states_from_checkpoint(self.src_file						)
							__UpperCAmelCase   ,       __UpperCAmelCase     :				Tuple					   =			model.ctx_encoder, "ctx_model."
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							__UpperCAmelCase     :				List[Any]					   =			{"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
							for key, value in saved_state.model_dict.items():
									if key.startswith(_SCREAMING_SNAKE_CASE						):
											__UpperCAmelCase     :				Tuple					   =			key[len(_SCREAMING_SNAKE_CASE						) :]
											if not key.startswith("encode_proj."						):
													__UpperCAmelCase     :				int					   =			"bert_model." + key
											__UpperCAmelCase     :				str					   =			value
							encoder.load_state_dict(_SCREAMING_SNAKE_CASE						)
							return model






class 			SCREAMING_SNAKE_CASE__	(					_UpperCamelCase						):







					"""simple docstring"""






					def 						lowerCamelCase_					(					self    :	Optional[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Tuple					   =			DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
							print(f"Loading DPR biencoder from {self.src_file}"						)
							__UpperCAmelCase     :				List[Any]					   =			load_states_from_checkpoint(self.src_file						)
							__UpperCAmelCase   ,       __UpperCAmelCase     :				Tuple					   =			model.question_encoder, "question_model."
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							__UpperCAmelCase     :				Optional[int]					   =			{"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
							for key, value in saved_state.model_dict.items():
									if key.startswith(_SCREAMING_SNAKE_CASE						):
											__UpperCAmelCase     :				Dict					   =			key[len(_SCREAMING_SNAKE_CASE						) :]
											if not key.startswith("encode_proj."						):
													__UpperCAmelCase     :				Tuple					   =			"bert_model." + key
											__UpperCAmelCase     :				List[Any]					   =			value
							encoder.load_state_dict(_SCREAMING_SNAKE_CASE						)
							return model






class 			SCREAMING_SNAKE_CASE__	(					_UpperCamelCase						):







					"""simple docstring"""






					def 						lowerCamelCase_					(					self    :	Optional[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Optional[int]					   =			DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
							print(f"Loading DPR reader from {self.src_file}"						)
							__UpperCAmelCase     :				List[Any]					   =			load_states_from_checkpoint(self.src_file						)
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							__UpperCAmelCase     :				List[Any]					   =			{
							    "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
							}
							for key, value in saved_state.model_dict.items():
									if key.startswith("encoder."						) and not key.startswith("encoder.encode_proj"						):
											__UpperCAmelCase     :				Optional[Any]					   =			"encoder.bert_model." + key[len("encoder."						) :]
									__UpperCAmelCase     :				int					   =			value
							model.span_predictor.load_state_dict(_SCREAMING_SNAKE_CASE						)
							return model






def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				Dict					   =			Path(_UpperCamelCase   )
		dest_dir.mkdir(exist_ok=_UpperCamelCase   )

		__UpperCAmelCase     :				str					   =			DPRState.from_type(_UpperCamelCase,    src_file=_UpperCamelCase   )
		__UpperCAmelCase     :				int					   =			dpr_state.load_dpr_model()
		model.save_pretrained(_UpperCamelCase   )
		model.from_pretrained(_UpperCamelCase   )  # sanity check


if __name__ == "__main__":
			lowerCAmelCase__	:					str	     =       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--type", type=str, help="Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'."
			)
			parser.add_argument(
			    "--src",
			    type=str,
			    help=(
			        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
			        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
			        " \'retriever\' checkpoints."
			    ),
			)
			parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
			lowerCAmelCase__	:					int	     =       parser.parse_args()

			lowerCAmelCase__	:					Union[str, Any]	     =       Path(args.src)
			lowerCAmelCase__	:					List[Any]	     =       f"converted-{src_file.name}" if args.dest is None else args.dest
			lowerCAmelCase__	:					Tuple	     =       Path(dest_dir)
			assert src_file.exists()
			assert (
			    args.type is not None
			), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
			convert(args.type, src_file, dest_dir)





'''simple docstring'''




__snake_case   :      List[Any]						    =  [chr(i) for i in range(32, 126)]
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      List[Any]						    =  list(reversed(range(len(alphabets))))
__snake_case   :      Optional[int]						    =  []
__snake_case   :      List[Any]						    =  0





def      _UpperCAmelCase    (							)	->      Any:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    A_     =   gear_one[0]
    gear_one.append(_UpperCamelCase   )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(_UpperCamelCase   )   ) == 0:
        A_     =   gear_two[0]
        gear_two.append(_UpperCamelCase   )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(_UpperCamelCase   )   ) == 0:
            A_     =   gear_three[0]
            gear_three.append(_UpperCamelCase   )
            del gear_three[0]
            gear_three_pos += 1





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      Any:
    A_     =   alphabets.index(_UpperCamelCase   )
    A_     =   gear_one[target]
    A_     =   gear_two[target]
    A_     =   gear_three[target]
    A_     =   reflector[target]
    A_     =   gear_three.index(_UpperCamelCase   )
    A_     =   gear_two.index(_UpperCamelCase   )
    A_     =   gear_one.index(_UpperCamelCase   )
    code.append(alphabets[target]   )
    rotator()


if __name__ == "__main__":
  __snake_case   :      List[str]						    =  list(input('Type your message:\n'))
  while True:
    try:
      __snake_case   :      Any						    =  int(input('Please set token:(must be only digits)\n'))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print('\n' + ''.join(code))
  print(
      F"""\nYour Token is {token} please write it down.\nIf you want to decode """
      'this message again you should input same digits as token!'
  )






