from typing import Any

import numpy as np
class        a_				:






	'''simple docstring'''






	def __init__(    self      :						Optional[int]    ,					lowercase__      :						int    ,					lowercase__      :						List[Any]):







								'''simple docstring'''



								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__      :						Dict):







								'''simple docstring'''



								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
								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      :						str    ,					lowercase__      :						Tuple):







								'''simple docstring'''



								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(__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
								lowerCAmelCase__  			=      np.nanargmin(__SCREAMING_SNAKE_CASE) + self.n_stages
								return row_idx, col_idx




	def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						Dict    ,					lowercase__      :						List[Any]    ,					lowercase__      :						Optional[Any]):







								'''simple docstring'''



								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      :						int    ,					lowercase__      :						Any):







								'''simple docstring'''



								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(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					axis=1)

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

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




	def 	__snake_case		(    self      :						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:
																						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    ,					__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE)
								return {}




	def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						List[str]    ,					lowercase__      :						Union[str, Any]):







								'''simple docstring'''



								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(__SCREAMING_SNAKE_CASE    ,					0)
								return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




 def __init__(    self      :						List[Any]    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						int=None    ,					**lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.'    ,					lowercase__    ,					)
               lowerCAmelCase__  			=      kwargs.pop('feature_extractor')

        lowerCAmelCase__  			=      image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.')
        if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.')

        super().__init__(lowercase__    ,					lowercase__)




 def __call__(    self      :						Any    ,					lowercase__      :						Union[str, Any]    ,					lowercase__      :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,					lowercase__      :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,					lowercase__      :						Union[List[List[int]], List[List[List[int]]]] = None    ,					lowercase__      :						Optional[Union[List[int], List[List[int]]]] = None    ,					lowercase__      :						bool = True    ,					lowercase__      :						Union[bool, str, PaddingStrategy] = False    ,					lowercase__      :						Union[bool, str, TruncationStrategy] = False    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						int = 0    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = True    ,					lowercase__      :						Optional[Union[str, TensorType]] = None    ,					**lowercase__      :						List[str]    ,					):







        '''simple docstring'''



        if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.')

        if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')

        if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.')

        # first, apply the image processor
        lowerCAmelCase__  			=      self.image_processor(images=lowercase__    ,					return_tensors=lowercase__)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(lowercase__    ,					lowercase__):
                      lowerCAmelCase__  			=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
               lowerCAmelCase__  			=      features['words']

        lowerCAmelCase__  			=      self.tokenizer(
            text=text if text is not None else features['words']    ,					text_pair=text_pair if text_pair is not None else None    ,					boxes=boxes if boxes is not None else features['boxes']    ,					word_labels=lowercase__    ,					add_special_tokens=lowercase__    ,					padding=lowercase__    ,					truncation=lowercase__    ,					max_length=lowercase__    ,					stride=lowercase__    ,					pad_to_multiple_of=lowercase__    ,					return_token_type_ids=lowercase__    ,					return_attention_mask=lowercase__    ,					return_overflowing_tokens=lowercase__    ,					return_special_tokens_mask=lowercase__    ,					return_offsets_mapping=lowercase__    ,					return_length=lowercase__    ,					verbose=lowercase__    ,					return_tensors=lowercase__    ,					**lowercase__    ,					)

        # add pixel values
        lowerCAmelCase__  			=      features.pop('pixel_values')
        if return_overflowing_tokens is True:
               lowerCAmelCase__  			=      self.get_overflowing_images(lowercase__    ,					encoded_inputs['overflow_to_sample_mapping'])
        lowerCAmelCase__  			=      images

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx])

        if len(lowercase__) != len(lowercase__):
               raise ValueError(
                   'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                   F""" {len(lowercase__)} and {len(lowercase__)}""")

        return images_with_overflow




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



        return self.tokenizer.batch_decode(*lowercase__    ,					**lowercase__)




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						str    ,					**lowercase__      :						List[Any]):







        '''simple docstring'''



        return self.tokenizer.decode(*lowercase__    ,					**lowercase__)




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					lowercase__    ,					)
        return self.image_processor_class




 @property
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					lowercase__    ,					)
        return self.image_processor
