

"""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


__A		     =    collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			CheckpointState:
		print(F"Reading saved model from {model_file}"				)
		__lowerCAmelCase: Optional[int]					   =    torch.load(snake_case_      ,				map_location=lambda __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				: default_restore_location(snake_case_      ,				"cpu"				)				)
		return CheckpointState(**snake_case_				)





class 					snake_case			:

				def __init__(			self							:       List[Any]  ,						UpperCamelCase__							:       Tuple)->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: Dict					   =    src_file

				def 							lowercase_  (			self							:       Optional[Any])->     Optional[Any]:



						'''simple docstring'''


						raise NotImplementedError




				@staticmethod
				def 							lowercase_  (			UpperCamelCase__							:       Tuple  ,						*UpperCamelCase__							:       Dict  ,						**UpperCamelCase__							:       int)->     "DPRState":



						'''simple docstring'''


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





class 					snake_case			(       UpperCamelCase_						):

				def 							lowercase_  (			self							:       Optional[Any])->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0]))
						print(f"Loading DPR biencoder from {self.src_file}")
						__lowerCAmelCase: Optional[int]					   =    load_states_from_checkpoint(self.src_file)
						__lowerCAmelCase: List[str]					   =    model.ctx_encoder, '''ctx_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						__lowerCAmelCase: Optional[int]					   =    {'''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(__UpperCamelCase):
										__lowerCAmelCase: int					   =    key[len(__UpperCamelCase) :]
										if not key.startswith("encode_proj."):
												__lowerCAmelCase: Any					   =    '''bert_model.''' + key
										__lowerCAmelCase: List[str]					   =    value
						encoder.load_state_dict(__UpperCamelCase)
						return model





class 					snake_case			(       UpperCamelCase_						):

				def 							lowercase_  (			self							:       Tuple)->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: Union[str, Any]					   =    DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0]))
						print(f"Loading DPR biencoder from {self.src_file}")
						__lowerCAmelCase: List[str]					   =    load_states_from_checkpoint(self.src_file)
						__lowerCAmelCase: Optional[int]					   =    model.question_encoder, '''question_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						__lowerCAmelCase: Optional[Any]					   =    {'''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(__UpperCamelCase):
										__lowerCAmelCase: List[Any]					   =    key[len(__UpperCamelCase) :]
										if not key.startswith("encode_proj."):
												__lowerCAmelCase: Tuple					   =    '''bert_model.''' + key
										__lowerCAmelCase: str					   =    value
						encoder.load_state_dict(__UpperCamelCase)
						return model





class 					snake_case			(       UpperCamelCase_						):

				def 							lowercase_  (			self							:       Any)->     int:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0]))
						print(f"Loading DPR reader from {self.src_file}")
						__lowerCAmelCase: Dict					   =    load_states_from_checkpoint(self.src_file)
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						__lowerCAmelCase: Optional[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"):
										__lowerCAmelCase: List[Any]					   =    '''encoder.bert_model.''' + key[len("encoder.") :]
								__lowerCAmelCase: Optional[int]					   =    value
						model.span_predictor.load_state_dict(__UpperCamelCase)
						return model






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Optional[int]:
		__lowerCAmelCase: Optional[int]					   =    Path(snake_case_				)
		dest_dir.mkdir(exist_ok=snake_case_				)

		__lowerCAmelCase: Tuple					   =    DPRState.from_type(snake_case_      ,				src_file=snake_case_				)
		__lowerCAmelCase: str					   =    dpr_state.load_dpr_model()
		model.save_pretrained(snake_case_				)
		model.from_pretrained(snake_case_				)  # sanity check


if __name__ == "__main__":
	__A		     =    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.")
	__A		     =    parser.parse_args()

	__A		     =    Path(args.src)
	__A		     =    F'''converted-{src_file.name}''' if args.dest is None else args.dest
	__A		     =    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"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    "", "", ""
		__lowerCAmelCase: Any					   =    []

		for character in message_part:
				tmp.append(character_to_number[character]				)

		for each in tmp:
				one += each[0]
				two += each[1]
				three += each[2]

		return one + two + three






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, str]:
		__lowerCAmelCase   ,				__lowerCAmelCase: Union[str, Any]					   =    "", ""
		__lowerCAmelCase: int					   =    []

		for character in message_part:
				this_part += character_to_number[character]

		for digit in this_part:
				tmp += digit
				if len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				):
						result.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: Union[str, Any]					   =    ""

		return result[0], result[1], result[2]






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, dict[str, str], dict[str, str]]:
		# Validate message and alphabet, set to upper and remove spaces
		__lowerCAmelCase: int					   =    alphabet.replace(" "      ,				""				).upper()
		__lowerCAmelCase: int					   =    message.replace(" "      ,				""				).upper()

		# Check length and characters
		if len(__SCREAMING_SNAKE_CASE				) != 2_7:
				raise KeyError("Length of alphabet has to be 27."				)
		for each in message:
				if each not in alphabet:
						raise ValueError("Each message character has to be included in alphabet!"				)

    # Generate dictionares
		__lowerCAmelCase: Optional[int]					   =    (
		    "111",
		    "112",
		    "113",
		    "121",
		    "122",
		    "123",
		    "131",
		    "132",
		    "133",
		    "211",
		    "212",
		    "213",
		    "221",
		    "222",
		    "223",
		    "231",
		    "232",
		    "233",
		    "311",
		    "312",
		    "313",
		    "321",
		    "322",
		    "323",
		    "331",
		    "332",
		    "333",
		)
		__lowerCAmelCase: Tuple					   =    {}
		__lowerCAmelCase: Optional[int]					   =    {}
		for letter, number in zip(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: List[Any]					   =    number
				__lowerCAmelCase: Any					   =    letter

		return message, alphabet, character_to_number, number_to_character






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase   ,				__lowerCAmelCase: str					   =    "", ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				encrypted_numeric += __encrypt_part(
				    message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				)      ,				3				):
				encrypted += number_to_character[encrypted_numeric[i : i + 3]]

		return encrypted






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Dict					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: List[str]					   =    []
		__lowerCAmelCase: Optional[int]					   =    ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    __decrypt_part(message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

				for j in range(0      ,				len(__SCREAMING_SNAKE_CASE				)				):
						decrypted_numeric.append(a[j] + b[j] + c[j]				)

		for each in decrypted_numeric:
				decrypted += number_to_character[each]

		return decrypted


if __name__ == "__main__":
			__A		     =    "DEFEND THE EAST WALL OF THE CASTLE."
			__A		     =    encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			__A		     =    decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')


