from typing import Any

import numpy as np







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__    )						->				None:
       '''simple docstring'''





       if np.any(tableau[:, -1]		, where=tableau[:, -1] < 0    ):
             raise ValueError('''RHS must be > 0'''    )

       UpperCAmelCase     :			Dict     	=tableau
       UpperCAmelCase	,    UpperCAmelCase     :			Any     	=tableau.shape

       # Number of decision variables x1, x2, x3...
       UpperCAmelCase     :			Dict     	=n_vars

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

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

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

       # Objectives for each stage
       UpperCAmelCase     :			Dict     	=['''max''']

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

       UpperCAmelCase     :			Optional[int]     	=['''''']

       # Index of current pivot row and column
       UpperCAmelCase     :			Any     	=None
       UpperCAmelCase     :			Any     	=None

       # Does objective row only contain (non)-negative values?
       UpperCAmelCase     :			str     	=False
 @staticmethod
 def 						UpperCAmelCase__							(	*snake_case__    )						->				list[str]:
       '''simple docstring'''





       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
       UpperCAmelCase     :			Optional[int]     	=['''x''', '''s''', '''a''']
       UpperCAmelCase     :			List[Any]     	=[]
       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 						UpperCAmelCase__							(	self		, snake_case__    )						->				tuple[Any, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.objectives[-1]

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

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

       # Elements of pivot column within slice
       UpperCAmelCase     :			Dict     	=tableau[s, col_idx]

       # Array filled with nans
       UpperCAmelCase     :			Optional[int]     	=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     :			Optional[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
       UpperCAmelCase     :			List[str]     	=np.nanargmin(snake_case__    ) + self.n_stages
       return row_idx, col_idx
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				np.ndarray:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=tableau[row_idx].copy()

       UpperCAmelCase     :			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 						UpperCAmelCase__							(	self		, snake_case__    )						->				np.ndarray:
       '''simple docstring'''





       self.objectives.pop()

       if not self.objectives:
             return tableau

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

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

       # Delete the objective row of the first stage
       UpperCAmelCase     :			int     	=np.delete(snake_case__		, 0		, axis=0    )

       UpperCAmelCase     :			Union[str, Any]     	=1
       self.n_rows -= 1
       UpperCAmelCase     :			Dict     	=0
       UpperCAmelCase     :			List[Any]     	=False
       return tableau
 def 						UpperCAmelCase__							(	self    )						->				dict[Any, 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:
                   UpperCAmelCase     :			Optional[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     :			Union[str, Any]     	=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     :			Any     	=self.change_stage(self.tableau    )
             else:
                   UpperCAmelCase     :			Dict     	=self.pivot(self.tableau		, snake_case__		, snake_case__    )
       return {}






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				dict[str, float]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	={'''P''': abs(tableau[0, -1]    )}

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

             # First entry in the nonzero ids
             UpperCAmelCase     :			Dict     	=nonzero[0][0]
             UpperCAmelCase     :			Union[str, Any]     	=tableau[nonzero_rowidx, i]

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

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 EfficientNetImageProcessor







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=3		, snake_case__=18		, snake_case__=30		, snake_case__=400		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=[0.5, 0.5, 0.5]		, )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=size if size is not None else {'''height''': 18, '''width''': 18}
       UpperCAmelCase     :			Tuple     	=parent
       UpperCAmelCase     :			List[str]     	=batch_size
       UpperCAmelCase     :			int     	=num_channels
       UpperCAmelCase     :			List[str]     	=image_size
       UpperCAmelCase     :			str     	=min_resolution
       UpperCAmelCase     :			Optional[int]     	=max_resolution
       UpperCAmelCase     :			Optional[int]     	=do_resize
       UpperCAmelCase     :			int     	=size
       UpperCAmelCase     :			Union[str, Any]     	=do_normalize
       UpperCAmelCase     :			Dict     	=image_mean
       UpperCAmelCase     :			List[Any]     	=image_std






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''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,
       }







@require_torch
@require_vision
class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   EfficientNetImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=EfficientNetImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=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 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size		, {'''height''': 18, '''width''': 18}    )

       UpperCAmelCase     :			List[str]     	=self.image_processing_class.from_dict(self.image_processor_dict		, size=42    )
       self.assertEqual(image_processor.size		, {'''height''': 42, '''width''': 42}    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       UpperCAmelCase     :			Optional[Any]     	=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
       UpperCAmelCase     :			int     	=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
       UpperCAmelCase     :			int     	=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 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			List[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
       UpperCAmelCase     :			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
       UpperCAmelCase     :			Union[str, 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 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       UpperCAmelCase     :			int     	=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
       UpperCAmelCase     :			List[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
       UpperCAmelCase     :			List[str]     	=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'''],
           )		, )
