



"""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 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Tuple:
							print(f"""Reading saved model from {model_file}"""      )
							A    =       torch.load(lowerCamelCase__					,   map_location=lambda lowerCamelCase__					,   lowerCamelCase__      : default_restore_location(lowerCamelCase__					,   'cpu'      )      )
							return CheckpointState(**lowerCamelCase__      )






class   UpperCAmelCase__		:



		def __init__(			self	:     Tuple   ,				snake_case	:     Path					)      ->    Dict:






									'''simple docstring'''

									A    =       src_file



		def        A_		(			self	:     int					)      ->    List[str]:






									'''simple docstring'''

									raise NotImplementedError



		@staticmethod
		def        A_		(			snake_case	:     str   ,				*snake_case	:     Union[str, Any]   ,				**snake_case	:     str					)      ->    Dict:






									'''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   UpperCAmelCase__		(   __UpperCamelCase  ):



		def        A_		(			self	:     List[str]					)      ->    str:






									'''simple docstring'''

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






class   UpperCAmelCase__		(   __UpperCamelCase  ):



		def        A_		(			self	:     List[str]					)      ->    Any:






									'''simple docstring'''

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






class   UpperCAmelCase__		(   __UpperCamelCase  ):



		def        A_		(			self	:     Union[str, Any]					)      ->    List[Any]:






									'''simple docstring'''

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



def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Optional[Any]:
							A    =       Path(lowerCamelCase__      )
							dest_dir.mkdir(exist_ok=lowerCamelCase__      )

							A    =       DPRState.from_type(lowerCamelCase__					,   src_file=lowerCamelCase__      )
							A    =       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)











from typing import Any

import numpy as np
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    if np.any(tableau[:, -1]			,		where=tableau[:, -1] < 0						):
       raise ValueError("""RHS must be > 0"""						)

    lowercase__    :						List[str]       =			tableau
    lowercase__  ,       lowercase__    :						Optional[Any]       =			tableau.shape

    # Number of decision variables x1, x2, x3...
    lowercase__    :						List[str]       =			n_vars

    # Number of artificial variables to be minimised
    lowercase__    :						str       =			len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    lowercase__    :						Optional[Any]       =			(self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    lowercase__    :						Optional[int]       =			self.n_rows - self.n_stages

    # Objectives for each stage
    lowercase__    :						Any       =			["""max"""]

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
       self.objectives.append("""min"""						)

    lowercase__    :						Union[str, Any]       =			[""""""]

    # Index of current pivot row and column
    lowercase__    :						List[str]       =			None
    lowercase__    :						List[str]       =			None

    # Does objective row only contain (non)-negative values?
    lowercase__    :						str       =			False



 @staticmethod
 def     UpperCAmelCase     (						*UpperCAmelCase__ : int						):

    """simple docstring"""






    if len(UpperCAmelCase__						) != 3:
       raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""						)

    if not all(x >= 0 and isinstance(UpperCAmelCase__			,		UpperCAmelCase__						) for x in args						):
       raise ValueError("""All arguments must be non-negative integers"""						)

    # decision | slack | artificial
    lowercase__    :						Optional[int]       =			["""x""", """s""", """a"""]
    lowercase__    :						int       =			[]
    for i in range(3						):
       for j in range(args[i]						):
          titles.append(string_starts[i] + str(j + 1						)						)
    titles.append("""RHS"""						)
    return titles



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    lowercase__    :						Union[str, Any]       =			(objective == """min""") - (objective == """max""")
    lowercase__    :						Optional[int]       =			np.argmax(sign * tableau[0, : self.n_vars]						)

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
       lowercase__    :						Optional[int]       =			True
       return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    lowercase__    :						Any       =			slice(self.n_stages			,		self.n_rows						)

    # RHS
    lowercase__    :						Union[str, Any]       =			tableau[s, -1]

    # Elements of pivot column within slice
    lowercase__    :						Optional[int]       =			tableau[s, col_idx]

    # Array filled with nans
    lowercase__    :						Optional[int]       =			np.full(self.n_rows - self.n_stages			,		np.nan						)

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    lowercase__    :						Optional[int]       =			np.divide(UpperCAmelCase__			,		UpperCAmelCase__			,		out=UpperCAmelCase__			,		where=divisor > 0						)

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    lowercase__    :						Dict       =			np.nanargmin(UpperCAmelCase__						) + self.n_stages
    return row_idx, col_idx



 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						int       =			tableau[row_idx].copy()

    lowercase__    :						Optional[Any]       =			piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx]						):
       tableau[idx] += -coeff * piv_row
    lowercase__    :						Any       =			piv_row
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    self.objectives.pop()

    if not self.objectives:
       return tableau

    # Slice containing ids for artificial columns
    lowercase__    :						Optional[int]       =			slice(-self.n_art_vars - 1			,		-1						)

    # Delete the artificial variable columns
    lowercase__    :						Dict       =			np.delete(UpperCAmelCase__			,		UpperCAmelCase__			,		axis=1						)

    # Delete the objective row of the first stage
    lowercase__    :						Optional[Any]       =			np.delete(UpperCAmelCase__			,		0			,		axis=0						)

    lowercase__    :						str       =			1
    self.n_rows -= 1
    lowercase__    :						List[str]       =			0
    lowercase__    :						List[Any]       =			False
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    for _ in range(100						):
       # Completion of each stage removes an objective. If both stages
       # are complete, then no objectives are left
       if not self.objectives:
          lowercase__    :						List[Any]       =			self.generate_col_titles(
              self.n_vars			,		self.n_slack			,		self.n_art_vars						)

          # Find the values of each variable at optimal solution
          return self.interpret_tableau(self.tableau			,		self.col_titles						)

       lowercase__  ,       lowercase__    :						Optional[Any]       =			self.find_pivot(self.tableau						)

       # If there are no more negative values in objective row
       if self.stop_iter:
          # Delete artificial variable columns and rows. Update attributes
          lowercase__    :						Any       =			self.change_stage(self.tableau						)
       else:
          lowercase__    :						str       =			self.pivot(self.tableau			,		UpperCAmelCase__			,		UpperCAmelCase__						)
    return {}







 def     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : list[str]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			{"""P""": abs(tableau[0, -1]						)}

    for i in range(self.n_vars						):
       # Gives ids of nonzero entries in the ith column
       lowercase__    :						int       =			np.nonzero(tableau[:, i]						)
       lowercase__    :						Tuple       =			len(nonzero[0]						)

       # First entry in the nonzero ids
       lowercase__    :						int       =			nonzero[0][0]
       lowercase__    :						Tuple       =			tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
          lowercase__    :						Tuple       =			tableau[nonzero_rowidx, -1]
          lowercase__    :						Union[str, Any]       =			rhs_val

        # Check for basic variables
    for title in col_titles:
       # Don't add RHS or slack variables to output dict
       if title[0] not in "R-s-a":
          output_dict.setdefault(UpperCAmelCase__			,		0						)
    return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()


