


'''simple docstring'''

from typing import Any

import numpy as np


class    a__					:







				"""simple docstring"""



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

										__lowerCAmelCase											=					tableau
										__lowerCAmelCase							,       __lowerCAmelCase											=					tableau.shape

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

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

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

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

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

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

										__lowerCAmelCase											=					['''''']

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

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




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

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

										# decision | slack | artificial
										__lowerCAmelCase											=					['''x''', '''s''', '''a''']
										__lowerCAmelCase											=					[]
										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        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					self.objectives[-1]

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

										# RHS
										__lowerCAmelCase											=					tableau[s, -1]

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

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

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




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase		):
										__lowerCAmelCase											=					tableau[row_idx].copy()

										__lowerCAmelCase											=					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
										__lowerCAmelCase											=					piv_row
										return tableau




				def        _snake_case	(self							,      __lowercase		):
										self.objectives.pop()

										if not self.objectives:
																return tableau

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

										# Delete the artificial variable columns
										__lowerCAmelCase											=					np.delete(__lowercase							,      __lowercase							,      axis=1		)

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

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




				def        _snake_case	(self		):
										# Stop simplex algorithm from cycling.
										for _ in range(1_00		):
																# Completion of each stage removes an objective. If both stages
																# are complete, then no objectives are left
																if not self.objectives:
																						__lowerCAmelCase											=					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		)

																__lowerCAmelCase							,       __lowerCAmelCase											=					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
																						__lowerCAmelCase											=					self.change_stage(self.tableau		)
																else:
																						__lowerCAmelCase											=					self.pivot(self.tableau							,      __lowercase							,      __lowercase		)
										return {}




				def        _snake_case	(self							,      __lowercase							,      __lowercase		):
										__lowerCAmelCase											=					{'''P''': abs(tableau[0, -1]		)}

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

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


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

_UpperCAmelCase					:			str				     =					{
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
	import detectrona


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		List[Any]						=						'layoutlmv2'




				def __init__(self							,      __lowercase=3_05_22							,      __lowercase=7_68							,      __lowercase=12							,      __lowercase=12							,      __lowercase=30_72							,      __lowercase="gelu"							,      __lowercase=0.1							,      __lowercase=0.1							,      __lowercase=5_12							,      __lowercase=2							,      __lowercase=0.0_2							,      __lowercase=1e-12							,      __lowercase=0							,      __lowercase=10_24							,      __lowercase=1_28							,      __lowercase=32							,      __lowercase=True							,      __lowercase=2_56							,      __lowercase=64							,      __lowercase=True							,      __lowercase=[7, 7, 2_56]							,      __lowercase=1_28							,      __lowercase=1_28							,      __lowercase=True							,      __lowercase=True							,      __lowercase=False							,      __lowercase=None							,      **__lowercase							,      ):
										super().__init__(
										    vocab_size=__lowercase							,      hidden_size=__lowercase							,      num_hidden_layers=__lowercase							,      num_attention_heads=__lowercase							,      intermediate_size=__lowercase							,      hidden_act=__lowercase							,      hidden_dropout_prob=__lowercase							,      attention_probs_dropout_prob=__lowercase							,      max_position_embeddings=__lowercase							,      type_vocab_size=__lowercase							,      initializer_range=__lowercase							,      layer_norm_eps=__lowercase							,      pad_token_id=__lowercase							,      **__lowercase							,      )
										__lowerCAmelCase											=					max_ad_position_embeddings
										__lowerCAmelCase											=					max_rel_pos
										__lowerCAmelCase											=					rel_pos_bins
										__lowerCAmelCase											=					fast_qkv
										__lowerCAmelCase											=					max_rel_ad_pos
										__lowerCAmelCase											=					rel_ad_pos_bins
										__lowerCAmelCase											=					convert_sync_batchnorm
										__lowerCAmelCase											=					image_feature_pool_shape
										__lowerCAmelCase											=					coordinate_size
										__lowerCAmelCase											=					shape_size
										__lowerCAmelCase											=					has_relative_attention_bias
										__lowerCAmelCase											=					has_spatial_attention_bias
										__lowerCAmelCase											=					has_visual_segment_embedding
										__lowerCAmelCase											=					(
										    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
										)




				@classmethod
				def        _snake_case	(self		):
										return {
										    "MODEL.MASK_ON": True,
										    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
										    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
										    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
										    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
										    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
										    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
										    "MODEL.POST_NMS_TOPK_TEST": 10_00,
										    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
										    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
										    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
										    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
										    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
										    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
										    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
										    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
										    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
										    "MODEL.RESNETS.DEPTH": 1_01,
										    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
										    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.RESNETS.NUM_GROUPS": 32,
										    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
										    "MODEL.RESNETS.STRIDE_IN_1X1": False,
										}




				def        _snake_case	(self		):
										__lowerCAmelCase											=					detectrona.config.get_cfg()
										for k, v in self.detectrona_config_args.items():
																__lowerCAmelCase											=					k.split('''.'''		)
																__lowerCAmelCase											=					detectrona_config
																for attribute in attributes[:-1]:
																						__lowerCAmelCase											=					getattr(__lowercase							,      __lowercase		)
																setattr(__lowercase							,      attributes[-1]							,      __lowercase		)

										return detectrona_config

