




'''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 ViltImageProcessor




class      UpperCamelCase       (	unittest.TestCase       ):



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=7		,     __lowerCAmelCase=3		,     __lowerCAmelCase=18		,     __lowerCAmelCase=30		,     __lowerCAmelCase=400		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=2		,     __lowerCAmelCase=True		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     ):







      '''simple docstring'''


      _a				:							Tuple							       =      size if size is not None else {"""shortest_edge""": 30}
      _a				:							Optional[int]							       =      parent
      _a				:							Union[str, Any]							       =      batch_size
      _a				:							int							       =      num_channels
      _a				:							List[str]							       =      image_size
      _a				:							str							       =      min_resolution
      _a				:							Union[str, Any]							       =      max_resolution
      _a				:							Tuple							       =      do_resize
      _a				:							str							       =      size
      _a				:							Any							       =      size_divisor
      _a				:							List[str]							       =      do_normalize
      _a				:							Tuple							       =      image_mean
      _a				:							str							       =      image_std



  def     a_							(			self ):







      '''simple docstring'''


      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      if not batched:
          _a				:							List[Any]							       =      self.size["""shortest_edge"""]
          _a				:							Optional[int]							       =      image_inputs[0]
          if isinstance(__lowerCAmelCase		,     Image.Image ):
              _a       , _a				:							Any							       =      image.size
          else:
              _a       , _a				:							Any							       =      image.shape[1], image.shape[2]
          _a				:							int							       =      size / min(__lowerCAmelCase		,     __lowerCAmelCase )
          if h < w:
              _a       , _a				:							str							       =      size, scale * w
          else:
              _a       , _a				:							Any							       =      scale * h, size

          _a				:							List[Any]							       =      int((1333 / 800) * size )
          if max(__lowerCAmelCase		,     __lowerCAmelCase ) > max_size:
              _a				:							Optional[Any]							       =      max_size / max(__lowerCAmelCase		,     __lowerCAmelCase )
              _a				:							Optional[int]							       =      newh * scale
              _a				:							List[str]							       =      neww * scale

          _a       , _a				:							int							       =      int(newh + 0.5 ), int(neww + 0.5 )
          _a       , _a				:							Dict							       =      (
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )

      else:
          _a				:							int							       =      []
          for image in image_inputs:
              _a       , _a				:							Any							       =      self.get_expected_values([image] )
              expected_values.append((expected_height, expected_width) )
          _a				:							Tuple							       =      max(__lowerCAmelCase		,     key=lambda __lowerCAmelCase : item[0] )[0]
          _a				:							List[str]							       =      max(__lowerCAmelCase		,     key=lambda __lowerCAmelCase : item[1] )[1]

      return expected_height, expected_width






@require_torch
@require_vision
class      UpperCamelCase       (	snake_case__      ,		unittest.TestCase       ):
  a     :Any          =					ViltImageProcessor if is_vision_available() else None



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      ViltImageProcessingTester(self )



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return self.image_processor_tester.prepare_image_processor_dict()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_mean""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_std""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_normalize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_resize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """size""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """size_divisor""" ) )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size		,     {"""shortest_edge""": 30} )

      _a				:							List[Any]							       =      self.image_processing_class.from_dict(self.image_processor_dict		,     size=42 )
      self.assertEqual(image_processor.size		,     {"""shortest_edge""": 42} )



  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a				:							int							       =      prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase )
      for image in image_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     Image.Image )

      # Test not batched input
      _a				:							str							       =      image_processing(image_inputs[0]		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							int							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _a				:							Tuple							       =      image_processing(__lowerCAmelCase		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							int							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a				:							Union[str, Any]							       =      prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     numpify=__lowerCAmelCase )
      for image in image_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     np.ndarray )

      # Test not batched input
      _a				:							int							       =      image_processing(image_inputs[0]		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							List[Any]							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _a				:							List[Any]							       =      image_processing(__lowerCAmelCase		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							List[str]							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a				:							List[Any]							       =      prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     torchify=__lowerCAmelCase )
      for image in image_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     torch.Tensor )

      # Test not batched input
      _a				:							Optional[int]							       =      image_processing(image_inputs[0]		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							Any							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      _a				:							Optional[int]							       =      image_processing(__lowerCAmelCase		,     return_tensors="""pt""" ).pixel_values

      _a       , _a				:							List[Any]							       =      self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase )
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )







from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






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

											a__					   =		tableau
											a__   ,      a__					   =		tableau.shape

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

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

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

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

											# Objectives for each stage
											a__					   =		["""max"""]

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

											a__					   =		[""""""]

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

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


							@staticmethod
							def _a   (				*a_		):
											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
											a__					   =		["""x""", """s""", """a"""]
											a__					   =		[]
											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	,	a_		):
											a__					   =		self.objectives[-1]

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

											# RHS
											a__					   =		tableau[s, -1]

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

											# Array filled with nans
											a__					   =		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
											a__					   =		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
											a__					   =		np.nanargmin(a_		) + self.n_stages
											return row_idx, col_idx


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		tableau[row_idx].copy()

											a__					   =		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
											a__					   =		piv_row
											return tableau


							def _a   (				self	,	a_		):
											self.objectives.pop()

											if not self.objectives:
															return tableau

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

											# Delete the artificial variable columns
											a__					   =		np.delete(a_	,	a_	,	axis=1		)

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

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


							def _a   (				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:
																			a__					   =		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		)

															a__   ,      a__					   =		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
																			a__					   =		self.change_stage(self.tableau		)
															else:
																			a__					   =		self.pivot(self.tableau	,	a_	,	a_		)
											return {}







							def _a   (				self	,	a_	,	a_		):
											a__					   =		{"""P""": abs(tableau[0, -1]		)}

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

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

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

