



"""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_video_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import VideoMAEImageProcessor







class    A_    (  unittest.TestCase       ):



						"""simple docstring"""






						def __init__(      self				:Dict     ,						lowercase_				:Dict     ,						lowercase_				:Dict=7     ,						lowercase_				:str=3     ,						lowercase_				:str=10     ,						lowercase_				:Optional[int]=18     ,						lowercase_				:Dict=30     ,						lowercase_				:Optional[int]=4_00     ,						lowercase_				:Any=True     ,						lowercase_				:Union[str, Any]=None     ,						lowercase_				:List[str]=True     ,						lowercase_				:Any=[0.5, 0.5, 0.5]     ,						lowercase_				:Tuple=[0.5, 0.5, 0.5]     ,						lowercase_				:Union[str, Any]=None     ,						)       ->    str:
												UpperCAmelCase    =				size if size is not None else {'shortest_edge': 18}
												UpperCAmelCase    =				crop_size if crop_size is not None else {'height': 18, 'width': 18}

												UpperCAmelCase    =				parent
												UpperCAmelCase    =				batch_size
												UpperCAmelCase    =				num_channels
												UpperCAmelCase    =				num_frames
												UpperCAmelCase    =				image_size
												UpperCAmelCase    =				min_resolution
												UpperCAmelCase    =				max_resolution
												UpperCAmelCase    =				do_resize
												UpperCAmelCase    =				size
												UpperCAmelCase    =				do_normalize
												UpperCAmelCase    =				image_mean
												UpperCAmelCase    =				image_std
												UpperCAmelCase    =				crop_size






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    Optional[int]:
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "crop_size": self.crop_size,
												}







@require_torch
@require_vision
class    A_    (  SCREAMING_SNAKE_CASE_			,     unittest.TestCase       ):



						"""simple docstring"""
						__UpperCamelCase	       =							VideoMAEImageProcessor if is_vision_available() else None






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    Any:
												UpperCAmelCase    =				VideoMAEImageProcessingTester(self      )






						@property
						def 			UpperCAmelCase__  (      self				:List[str]      )       ->    Dict:
												return self.image_processor_tester.prepare_image_processor_dict()






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    str:
												UpperCAmelCase    =				self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(lowercase_     ,						'image_mean'      )      )
												self.assertTrue(hasattr(lowercase_     ,						'image_std'      )      )
												self.assertTrue(hasattr(lowercase_     ,						'do_normalize'      )      )
												self.assertTrue(hasattr(lowercase_     ,						'do_resize'      )      )
												self.assertTrue(hasattr(lowercase_     ,						'do_center_crop'      )      )
												self.assertTrue(hasattr(lowercase_     ,						'size'      )      )






						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    Optional[int]:
												UpperCAmelCase    =				self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size     ,						{'shortest_edge': 18}      )
												self.assertEqual(image_processor.crop_size     ,						{'height': 18, 'width': 18}      )

												UpperCAmelCase    =				self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84      )
												self.assertEqual(image_processor.size     ,						{'shortest_edge': 42}      )
												self.assertEqual(image_processor.crop_size     ,						{'height': 84, 'width': 84}      )






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    Union[str, Any]:
												pass






						def 			UpperCAmelCase__  (      self				:List[str]      )       ->    Any:
												# Initialize image_processing
												UpperCAmelCase    =				self.image_processing_class(**self.image_processor_dict      )
												# create random PIL videos
												UpperCAmelCase    =				prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=lowercase_      )
												for video in video_inputs:
																		self.assertIsInstance(lowercase_     ,						lowercase_      )
																		self.assertIsInstance(video[0]     ,						Image.Image      )

												# Test not batched input
												UpperCAmelCase    =				image_processing(video_inputs[0]     ,						return_tensors='pt'      ).pixel_values
												self.assertEqual(
												    encoded_videos.shape     ,						(
												        1,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )     ,						)

												# Test batched
												UpperCAmelCase    =				image_processing(lowercase_     ,						return_tensors='pt'      ).pixel_values
												self.assertEqual(
												    encoded_videos.shape     ,						(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )     ,						)






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    str:
												# Initialize image_processing
												UpperCAmelCase    =				self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												UpperCAmelCase    =				prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=lowercase_     ,						numpify=lowercase_      )
												for video in video_inputs:
																		self.assertIsInstance(lowercase_     ,						lowercase_      )
																		self.assertIsInstance(video[0]     ,						np.ndarray      )

												# Test not batched input
												UpperCAmelCase    =				image_processing(video_inputs[0]     ,						return_tensors='pt'      ).pixel_values
												self.assertEqual(
												    encoded_videos.shape     ,						(
												        1,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )     ,						)

												# Test batched
												UpperCAmelCase    =				image_processing(lowercase_     ,						return_tensors='pt'      ).pixel_values
												self.assertEqual(
												    encoded_videos.shape     ,						(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )     ,						)






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    Dict:
												# Initialize image_processing
												UpperCAmelCase    =				self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												UpperCAmelCase    =				prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=lowercase_     ,						torchify=lowercase_      )
												for video in video_inputs:
																		self.assertIsInstance(lowercase_     ,						lowercase_      )
																		self.assertIsInstance(video[0]     ,						torch.Tensor      )

												# Test not batched input
												UpperCAmelCase    =				image_processing(video_inputs[0]     ,						return_tensors='pt'      ).pixel_values
												self.assertEqual(
												    encoded_videos.shape     ,						(
												        1,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )     ,						)

												# Test batched
												UpperCAmelCase    =				image_processing(lowercase_     ,						return_tensors='pt'      ).pixel_values
												self.assertEqual(
												    encoded_videos.shape     ,						(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )     ,						)










"""simple docstring"""


from typing import Any

import numpy as np







class    A_    :



						"""simple docstring"""






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

												UpperCAmelCase    =				tableau
												UpperCAmelCase   ,					UpperCAmelCase    =				tableau.shape

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

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

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

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

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

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

												UpperCAmelCase    =				['']

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

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






						@staticmethod
						def 			UpperCAmelCase__  (      *lowercase_				:int      )       ->    list[str]:
												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
												UpperCAmelCase    =				['x', 's', 'a']
												UpperCAmelCase    =				[]
												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				:List[str]     ,						lowercase_				:np.ndarray      )       ->    tuple[Any, Any]:
												UpperCAmelCase    =				self.objectives[-1]

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

												# RHS
												UpperCAmelCase    =				tableau[s, -1]

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

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






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:np.ndarray     ,						lowercase_				:int     ,						lowercase_				:int      )       ->    np.ndarray:
												UpperCAmelCase    =				tableau[row_idx].copy()

												UpperCAmelCase    =				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    =				piv_row
												return tableau






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:np.ndarray      )       ->    np.ndarray:
												self.objectives.pop()

												if not self.objectives:
																		return tableau

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

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

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

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






						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    dict[Any, Any]:
												# 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:
																								UpperCAmelCase    =				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    =				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    =				self.change_stage(self.tableau      )
																		else:
																								UpperCAmelCase    =				self.pivot(self.tableau     ,						lowercase_     ,						lowercase_      )
												return {}






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:np.ndarray     ,						lowercase_				:list[str]      )       ->    dict[str, float]:
												UpperCAmelCase    =				{'P': abs(tableau[0, -1]      )}

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

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

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





