
from typing import Any

import numpy as np







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            # Check if RHS is negative
            if np.any(tableau[:, -1]							,       where=tableau[:, -1] < 0    ):
                   raise ValueError('RHS must be > 0'    )

            __snake_case						       =   tableau
            __snake_case       ,       __snake_case						       =   tableau.shape

            # Number of decision variables x1, x2, x3...
            __snake_case						       =   n_vars

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

            # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
            __snake_case						       =   (self.n_art_vars > 0) + 1

            # Number of slack variables added to make inequalities into equalities
            __snake_case						       =   self.n_rows - self.n_stages

            # Objectives for each stage
            __snake_case						       =   ['max']

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

            __snake_case						       =   ['']

            # Index of current pivot row and column
            __snake_case						       =   None
            __snake_case						       =   None

            # Does objective row only contain (non)-negative values?
            __snake_case						       =   False

     @staticmethod
     def 						__lowerCamelCase				(  *SCREAMING_SNAKE_CASE_    ):
            if len(SCREAMING_SNAKE_CASE_    ) != 3:
                   raise ValueError('Must provide n_vars, n_slack, and n_art_vars'    )

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

            # decision | slack | artificial
            __snake_case						       =   ['x', 's', 'a']
            __snake_case						       =   []
            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 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   self.objectives[-1]

            # Find entries of highest magnitude in objective rows
            __snake_case						       =   (objective == 'min') - (objective == 'max')
            __snake_case						       =   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:
                   __snake_case						       =   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
            __snake_case						       =   slice(self.n_stages							,       self.n_rows    )

            # RHS
            __snake_case						       =   tableau[s, -1]

            # Elements of pivot column within slice
            __snake_case						       =   tableau[s, col_idx]

            # Array filled with nans
            __snake_case						       =   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
            __snake_case						       =   np.divide(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       out=SCREAMING_SNAKE_CASE_							,       where=divisor > 0    )

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   tableau[row_idx].copy()

            __snake_case						       =   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
            __snake_case						       =   piv_row
            return tableau

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            self.objectives.pop()

            if not self.objectives:
                   return tableau

            # Slice containing ids for artificial columns
            __snake_case						       =   slice(-self.n_art_vars - 1							,       -1    )

            # Delete the artificial variable columns
            __snake_case						       =   np.delete(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       axis=1    )

            # Delete the objective row of the first stage
            __snake_case						       =   np.delete(SCREAMING_SNAKE_CASE_							,       0							,       axis=0    )

            __snake_case						       =   1
            self.n_rows -= 1
            __snake_case						       =   0
            __snake_case						       =   False
            return tableau

     def 						__lowerCamelCase				(  self    ):
            # Stop simplex algorithm from cycling.
            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:
                          __snake_case						       =   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    )

                   __snake_case       ,       __snake_case						       =   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
                          __snake_case						       =   self.change_stage(self.tableau    )
                   else:
                          __snake_case						       =   self.pivot(self.tableau							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            return {}

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   {'P': abs(tableau[0, -1]    )}

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

                   # First entry in the nonzero ids
                   __snake_case						       =   nonzero[0][0]
                   __snake_case						       =   tableau[nonzero_rowidx, i]

                   # If there is only one nonzero value in column, which is one
                   if n_nonzero == nonzero_val == 1:
                          __snake_case						       =   tableau[nonzero_rowidx, -1]
                          __snake_case						       =   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(SCREAMING_SNAKE_CASE_							,       0    )
            return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    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(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
