



"""simple docstring"""
from typing import Any

import numpy as np
class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							List[Any]						,					snake_case:							np.ndarray						,					snake_case:							int						)       ->     None:
											# Check if RHS is negative
											if np.any(tableau[:, -1]						,					where=tableau[:, -1] < 0						):
																	raise ValueError("""RHS must be > 0"""						)

											snake_case_			:str						=      tableau
											snake_case_			:Any						=      tableau.shape

											# Number of decision variables x1, x2, x3...
											snake_case_			:Optional[int]						=      n_vars

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

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

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

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

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

											snake_case_			:List[Any]						=      [""""""]

											# Index of current pivot row and column
											snake_case_			:Union[str, Any]						=      None
											snake_case_			:List[Any]						=      None

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






					@staticmethod
					def        lowerCAmelCase_    (	*snake_case:							int						)       ->     list[str]:
											if len(snake_case						) != 3:
																	raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""						)

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

											# decision | slack | artificial
											snake_case_			:List[Any]						=      ["""x""", """s""", """a"""]
											snake_case_			:Tuple						=      []
											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:							List[Any]						,					snake_case:							np.ndarray						)       ->     tuple[Any, Any]:
											snake_case_			:Any						=      self.objectives[-1]

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

											# RHS
											snake_case_			:List[str]						=      tableau[s, -1]

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

											# Array filled with nans
											snake_case_			:Union[str, 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
											snake_case_			:int						=      np.divide(snake_case						,					snake_case						,					out=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_			:Union[str, Any]						=      np.nanargmin(snake_case						) + self.n_stages
											return row_idx, col_idx






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							np.ndarray						,					snake_case:							int						,					snake_case:							int						)       ->     np.ndarray:
											snake_case_			:Optional[Any]						=      tableau[row_idx].copy()

											snake_case_			:int						=      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_			:Dict						=      piv_row
											return tableau






					def        lowerCAmelCase_    (	self:							int						,					snake_case:							np.ndarray						)       ->     np.ndarray:
											self.objectives.pop()

											if not self.objectives:
																	return tableau

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

											# Delete the artificial variable columns
											snake_case_			:str						=      np.delete(snake_case						,					snake_case						,					axis=1						)

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

											snake_case_			:Union[str, Any]						=      1
											self.n_rows -= 1
											snake_case_			:Dict						=      0
											snake_case_			:Tuple						=      False
											return tableau






					def        lowerCAmelCase_    (	self:							List[str]						)       ->     dict[Any, 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:
																							snake_case_			:Dict						=      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_			:int						=      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_			:Optional[int]						=      self.change_stage(self.tableau						)
																	else:
																							snake_case_			:Optional[int]						=      self.pivot(self.tableau						,					snake_case						,					snake_case						)
											return {}






					def        lowerCAmelCase_    (	self:							str						,					snake_case:							np.ndarray						,					snake_case:							list[str]						)       ->     dict[str, float]:
											snake_case_			:Tuple						=      {"""P""": abs(tableau[0, -1]						)}

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

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

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							List[str]						,					snake_case:							List[str]=7						,					snake_case:							Union[str, Any]=3						,					snake_case:							List[str]=18						,					snake_case:							Any=30						,					snake_case:							Any=400						,					snake_case:							Any=True						,					snake_case:							List[str]=None						,					snake_case:							Union[str, Any]=True						,					snake_case:							int=[0.5, 0.5, 0.5]						,					snake_case:							Tuple=[0.5, 0.5, 0.5]						,					)       ->     int:
											snake_case_			:Dict						=      size if size is not None else {"""height""": 18, """width""": 18}
											snake_case_			:Optional[Any]						=      parent
											snake_case_			:Tuple						=      batch_size
											snake_case_			:Tuple						=      num_channels
											snake_case_			:Optional[int]						=      image_size
											snake_case_			:Tuple						=      min_resolution
											snake_case_			:str						=      max_resolution
											snake_case_			:Any						=      do_resize
											snake_case_			:Union[str, Any]						=      size
											snake_case_			:Tuple						=      do_normalize
											snake_case_			:int						=      image_mean
											snake_case_			:List[Any]						=      image_std






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}

@require_torch
@require_vision
class     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					ViTImageProcessor if is_vision_available() else None






					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											snake_case_			:Dict						=      ViTImageProcessingTester(self						)






					@property
					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Optional[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()






					def        lowerCAmelCase_    (	self:							str						)       ->     List[str]:
											snake_case_			:List[str]						=      self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case						,					"""image_mean"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""image_std"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_resize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""size"""						)						)






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,					{"""height""": 18, """width""": 18}						)

											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						)
											self.assertEqual(image_processor.size						,					{"""height""": 42, """width""": 42}						)






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     int:
											pass






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											# Initialize image_processing
											snake_case_			:Any						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					Image.Image						)

											# Test not batched input
											snake_case_			:List[str]						=      image_processing(image_inputs[0]						,					return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Dict						=      image_processing(snake_case						,					return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Tuple:
											# Initialize image_processing
											snake_case_			:int						=      self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					numpify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					np.ndarray						)

											# Test not batched input
											snake_case_			:Any						=      image_processing(image_inputs[0]						,					return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Any						=      image_processing(snake_case						,					return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Optional[int]:
											# Initialize image_processing
											snake_case_			:Union[str, Any]						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_			:List[Any]						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					torchify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					torch.Tensor						)

											# Test not batched input
											snake_case_			:Optional[Any]						=      image_processing(image_inputs[0]						,					return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,					(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:List[Any]						=      image_processing(snake_case						,					return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)
