'''simple docstring'''




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						Dict      			=		"layoutlm"




      def __init__( self		:       int		,      A		:       Optional[int]=30522		,      A		:       Optional[int]=768		,      A		:       Optional[int]=12		,      A		:       Tuple=12		,      A		:       int=3072		,      A		:       Tuple="gelu"		,      A		:       str=0.1		,      A		:       Optional[int]=0.1		,      A		:       str=512		,      A		:       Union[str, Any]=2		,      A		:       Optional[int]=0.02		,      A		:       List[Any]=1E-12		,      A		:       List[Any]=0		,      A		:       int="absolute"		,      A		:       List[Any]=True		,      A		:       Optional[Any]=1024		,      **A		:       Optional[int]		,      ):
             super().__init__(pad_token_id=A		,      **A )
             _UpperCAmelCase  :						List[str]							   =					vocab_size
             _UpperCAmelCase  :						str							   =					hidden_size
             _UpperCAmelCase  :						List[Any]							   =					num_hidden_layers
             _UpperCAmelCase  :						Optional[int]							   =					num_attention_heads
             _UpperCAmelCase  :						int							   =					hidden_act
             _UpperCAmelCase  :						str							   =					intermediate_size
             _UpperCAmelCase  :						Optional[Any]							   =					hidden_dropout_prob
             _UpperCAmelCase  :						Dict							   =					attention_probs_dropout_prob
             _UpperCAmelCase  :						Any							   =					max_position_embeddings
             _UpperCAmelCase  :						List[Any]							   =					type_vocab_size
             _UpperCAmelCase  :						List[str]							   =					initializer_range
             _UpperCAmelCase  :						Optional[Any]							   =					layer_norm_eps
             _UpperCAmelCase  :						List[str]							   =					position_embedding_type
             _UpperCAmelCase  :						Union[str, Any]							   =					use_cache
             _UpperCAmelCase  :						Union[str, Any]							   =					max_ad_position_embeddings

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      def __init__( self		:       Tuple		,      A		:       PretrainedConfig		,      A		:       str = "default"		,      A		:       List[PatchingSpec] = None		,      ):
             super().__init__(A		,      task=A		,      patching_specs=A )
             _UpperCAmelCase  :						Optional[Any]							   =					config.max_ad_position_embeddings - 1




      @property
      def   _A			( self		:       Optional[Any] ):
             return OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "sequence"}),
                     ("bbox", {0: "batch", 1: "sequence"}),
                     ("attention_mask", {0: "batch", 1: "sequence"}),
                     ("token_type_ids", {0: "batch", 1: "sequence"}),
                 ] )





      def   _A			( self		:       Dict		,      A		:       PreTrainedTokenizer		,      A		:       int = -1		,      A		:       int = -1		,      A		:       bool = False		,      A		:       Optional[TensorType] = None		,      ):
             _UpperCAmelCase  :						List[Any]							   =					super().generate_dummy_inputs(
                 A		,      batch_size=A		,      seq_length=A		,      is_pair=A		,      framework=A )

             # Generate a dummy bbox
             _UpperCAmelCase  :						int							   =					[48, 84, 73, 128]

             if not framework == TensorType.PYTORCH:
                    raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch." )

             if not is_torch_available():
                    raise ValueError("Cannot generate dummy inputs without PyTorch installed." )
             import torch

             _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					input_dict["input_ids"].shape
             _UpperCAmelCase  :						List[Any]							   =					torch.tensor([*[box] * seq_length] ).tile(A		,      1		,      1 )
             return input_dict


'''simple docstring'''




from typing import Any

import numpy as np

class 			lowerCamelCase_			:
      '''simple docstring'''

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

             _UpperCAmelCase  :						Union[str, Any]							   =					tableau
             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					tableau.shape

             # Number of decision variables x1, x2, x3...
             _UpperCAmelCase  :						Any							   =					n_vars

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

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

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

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

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

             _UpperCAmelCase  :						Any							   =					[""]

             # Index of current pivot row and column
             _UpperCAmelCase  :						Optional[Any]							   =					None
             _UpperCAmelCase  :						Dict							   =					None

             # Does objective row only contain (non)-negative values?
             _UpperCAmelCase  :						List[str]							   =					False




      @staticmethod
      def   _A			( *A		:       int ):
             if len(A ) != 3:
                    raise ValueError("Must provide n_vars, n_slack, and n_art_vars" )

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

             # decision | slack | artificial
             _UpperCAmelCase  :						Optional[Any]							   =					["x", "s", "a"]
             _UpperCAmelCase  :						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   _A			( self		:       List[str]		,      A		:       np.ndarray ):
             _UpperCAmelCase  :						str							   =					self.objectives[-1]

             # Find entries of highest magnitude in objective rows
             _UpperCAmelCase  :						List[str]							   =					(objective == "min") - (objective == "max")
             _UpperCAmelCase  :						Optional[Any]							   =					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:
                    _UpperCAmelCase  :						Dict							   =					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
             _UpperCAmelCase  :						Any							   =					slice(self.n_stages		,      self.n_rows )

             # RHS
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, -1]

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

             # Array filled with nans
             _UpperCAmelCase  :						List[Any]							   =					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
             _UpperCAmelCase  :						str							   =					np.divide(A		,      A		,      out=A		,      where=divisor > 0 )

             # Arg of minimum quotient excluding the nan values. n_stages is added
             # to compensate for earlier exclusion of objective columns
             _UpperCAmelCase  :						Optional[Any]							   =					np.nanargmin(A ) + self.n_stages
             return row_idx, col_idx




      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       int		,      A		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					tableau[row_idx].copy()

             _UpperCAmelCase  :						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
             _UpperCAmelCase  :						Optional[Any]							   =					piv_row
             return tableau




      def   _A			( self		:       Optional[Any]		,      A		:       np.ndarray ):
             self.objectives.pop()

             if not self.objectives:
                    return tableau

             # Slice containing ids for artificial columns
             _UpperCAmelCase  :						Union[str, Any]							   =					slice(-self.n_art_vars - 1		,      -1 )

             # Delete the artificial variable columns
             _UpperCAmelCase  :						List[Any]							   =					np.delete(A		,      A		,      axis=1 )

             # Delete the objective row of the first stage
             _UpperCAmelCase  :						Union[str, Any]							   =					np.delete(A		,      0		,      axis=0 )

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




      def   _A			( self		:       Optional[Any] ):
             # 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:
                           _UpperCAmelCase  :						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 )

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					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
                           _UpperCAmelCase  :						int							   =					self.change_stage(self.tableau )
                    else:
                           _UpperCAmelCase  :						Optional[Any]							   =					self.pivot(self.tableau		,      A		,      A )
             return {}





      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       list[str] ):
             _UpperCAmelCase  :						str							   =					{"P": abs(tableau[0, -1] )}

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

                    # First entry in the nonzero ids
                    _UpperCAmelCase  :						str							   =					nonzero[0][0]
                    _UpperCAmelCase  :						str							   =					tableau[nonzero_rowidx, i]

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()

