



'''simple docstring'''






A_				:					Optional[Any]							=      [0.33, 0.44, 0.55, 0.44, 0.33]
A_				:					str							=      3
A_				:					List[Any]							=      5

# Buffer Space (with Parameters Space)
A_				:					list[float]							=      []
A_				:					list[float]							=      []

# Machine Time
A_				:					str							=      0




def        snake_case_   (	lowerCAmelCase_						)->						List[str]:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       # Choosing Dynamical Systems (All)
       for key, value in enumerate(lowerCAmelCase_						):
              # Evolution Parameter
              _UpperCAmelCase    :		List[Any]     	=					float(seed / value						)

              # Control Theory: Orbit Change
              _UpperCAmelCase    :		Any     	=					(buffer_space[(key + 1) % m] + e) % 1

              # Control Theory: Trajectory Change
              _UpperCAmelCase    :		List[str]     	=					(params_space[key] + e) % 1 + 3

              # Modification (Transition Function) - Jumps
              _UpperCAmelCase    :		int     	=					round(float(r * value * (1 - value)						)			,  10						)
              _UpperCAmelCase    :		Union[str, Any]     	=					r  # Saving to Parameters Space

       # Logistic Map
       assert max(lowerCAmelCase_						) < 1
       assert max(lowerCAmelCase_						) < 4

       # Machine Time
       machine_time += 1




def        snake_case_   (	)->						str:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       # PRNG (Xorshift by George Marsaglia)
       def xorshift(lowerCAmelCase_			,  lowerCAmelCase_						):
              x ^= y >> 13
              y ^= x << 17
              x ^= y >> 5
              return x

       # Choosing Dynamical Systems (Increment)
       _UpperCAmelCase    :		int     	=					machine_time % m

       # Evolution (Time Length)
       for _ in range(0			,  lowerCAmelCase_						):
              # Variables (Position + Parameters)
              _UpperCAmelCase    :		Tuple     	=					params_space[key]
              _UpperCAmelCase    :		Optional[Any]     	=					buffer_space[key]

              # Modification (Transition Function) - Flow
              _UpperCAmelCase    :		Any     	=					round(float(r * value * (1 - value)						)			,  10						)
              _UpperCAmelCase    :		str     	=					(machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

       # Choosing Chaotic Data
       _UpperCAmelCase    :		List[Any]     	=					int(buffer_space[(key + 2) % m] * (10**10)						)
       _UpperCAmelCase    :		Union[str, Any]     	=					int(buffer_space[(key - 2) % m] * (10**10)						)

       # Machine Time
       machine_time += 1

       return xorshift(lowerCAmelCase_			,  lowerCAmelCase_						) % 0xFF_FFF_FFF




def        snake_case_   (	)->						Union[str, Any]:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       _UpperCAmelCase    :		int     	=					K
       _UpperCAmelCase    :		List[Any]     	=					[0] * m
       _UpperCAmelCase    :		Optional[Any]     	=					0


if __name__ == "__main__":
   # Initialization
   reset()

   # Pushing Data (Input)
   import random

   A_				:					Dict							=      random.sample(range(0xffffffff), 1_0_0)
   for chunk in message:
      push(chunk)

   # for controlling
   A_				:					str							=      """"""

   # Pulling Data (Output)
   while inp in ("e", "E"):
      print(f"""{format(pull(), "#04x")}""")
      print(buffer_space)
      print(params_space)
      A_				:					Optional[Any]							=      input("""(e)exit? """).strip()




'''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        UpperCAmelCase__   (				_lowerCAmelCase    :			str)    ->		CheckpointState:



						"""simple docstring"""

						print(f"""Reading saved model from {model_file}""")
						snake_case :       Optional[Any]				=    torch.load(_lowerCAmelCase,      map_location=lambda _lowerCAmelCase,      _lowerCAmelCase: default_restore_location(_lowerCAmelCase,      "cpu"))
						return CheckpointState(**_lowerCAmelCase)




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    src_file



			def   lowerCamelCase (							self					):
									raise NotImplementedError






			@staticmethod
			def   lowerCamelCase (							snake_case_				,			*snake_case_				,			**snake_case_					):
									if comp_type.startswith("c"					):
															return DPRContextEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("q"					):
															return DPRQuestionEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("r"					):
															return DPRReaderState(*snake_case_				,			**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					):
									snake_case :       List[Any]				=    DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR biencoder from {self.src_file}"""					)
									snake_case :       List[Any]				=    load_states_from_checkpoint(self.src_file					)
									snake_case				,					snake_case :       List[str]				=    model.ctx_encoder, "ctx_model."
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       List[str]				=    {"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(snake_case_					):
																					snake_case :       str				=    key[len(snake_case_					) :]
																					if not key.startswith("encode_proj."					):
																											snake_case :       int				=    "bert_model." + key
																					snake_case :       Union[str, Any]				=    value
									encoder.load_state_dict(snake_case_					)
									return model







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






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







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR reader from {self.src_file}"""					)
									snake_case :       List[str]				=    load_states_from_checkpoint(self.src_file					)
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       List[str]				=    {
									    "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"					):
																					snake_case :       List[Any]				=    "encoder.bert_model." + key[len("encoder."					) :]
															snake_case :       Union[str, Any]				=    value
									model.span_predictor.load_state_dict(snake_case_					)
									return model







def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			Path,      _lowerCAmelCase    :			Path)    ->		List[Any]:



						"""simple docstring"""

						snake_case :       Any				=    Path(_lowerCAmelCase)
						dest_dir.mkdir(exist_ok=_lowerCAmelCase)

						snake_case :       Tuple				=    DPRState.from_type(_lowerCAmelCase,      src_file=_lowerCAmelCase)
						snake_case :       Optional[int]				=    dpr_state.load_dpr_model()
						model.save_pretrained(_lowerCAmelCase)
						model.from_pretrained(_lowerCAmelCase)  # 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)





