


'''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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor






def 						_A					(lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:Any=10					,    lowerCAmelCase__	:int=10					,    lowerCAmelCase__	:Optional[int]=False					,    lowerCAmelCase__	:Any=False		) ->	Optional[int]:




     '''simple docstring'''



     _a         =				[]
     for i in range(image_processor_tester.num_frames		):
          video.append(np.random.randint(2_55					,    size=(image_processor_tester.num_channels, width, height)					,    dtype=np.uinta		)		)

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          _a         =				[Image.fromarray(np.moveaxis(lowercase__					,    0					,    -1		)		) for frame in video]

     if torchify:
          _a         =				[torch.from_numpy(lowercase__		) for frame in video]

     return video






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:Any=False					,    lowerCAmelCase__	:Optional[Any]=False					,    lowerCAmelCase__	:Optional[Any]=False		) ->	Union[str, Any]:




     '''simple docstring'''



     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     _a         =				[]
     for i in range(image_processor_tester.batch_size		):
          if equal_resolution:
               _a         =				image_processor_tester.max_resolution
          else:
               _a         =				np.random.choice(
                   np.arange(image_processor_tester.min_resolution					,    image_processor_tester.max_resolution		)					,    2		)
               _a         =				prepare_video(
                   image_processor_tester=lowercase__					,    width=lowercase__					,    height=lowercase__					,    numpify=lowercase__					,    torchify=lowercase__					,    )
          video_inputs.append(lowercase__		)

     return video_inputs




class 			a  (  unittest.TestCase							):
 def __init__(			self   ,					__magic_name__   ,					__magic_name__=7   ,					__magic_name__=3   ,					__magic_name__=4   ,					__magic_name__=18   ,					__magic_name__=30   ,					__magic_name__=4_00   ,					__magic_name__=True   ,					__magic_name__=None   ,					__magic_name__=True   ,					__magic_name__=[0.5, 0.5, 0.5]   ,					__magic_name__=[0.5, 0.5, 0.5]   ,					__magic_name__=True   ,					__magic_name__=None   ,					)       ->				str:
      _a         =				size if size is not None else {'shortest_edge': 18}
      _a         =				crop_size if crop_size is not None else {'height': 18, 'width': 18}

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




 def 				__UpperCAmelCase		(			self    )       ->				List[str]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }





@require_torch
@require_vision
class 			a  (  _A	,					unittest.TestCase							):
 _lowerCAmelCase			       =						TvltImageProcessor if is_vision_available() else None
 def 				__UpperCAmelCase		(			self    )       ->				Dict:
      _a         =				TvltImageProcessorTester(self    )
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Dict:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a         =				self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(_UpperCAmelCase   ,					'image_mean'    )    )
      self.assertTrue(hasattr(_UpperCAmelCase   ,					'image_std'    )    )
      self.assertTrue(hasattr(_UpperCAmelCase   ,					'do_normalize'    )    )
      self.assertTrue(hasattr(_UpperCAmelCase   ,					'do_resize'    )    )
      self.assertTrue(hasattr(_UpperCAmelCase   ,					'do_center_crop'    )    )
      self.assertTrue(hasattr(_UpperCAmelCase   ,					'size'    )    )
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      _a         =				self.image_processing_class(**self.image_processor_dict    )
      # create random PIL videos
      _a         =				prepare_video_inputs(self.image_processor_tester   ,					equal_resolution=_UpperCAmelCase    )
      for video in video_inputs:
           self.assertIsInstance(_UpperCAmelCase   ,					_UpperCAmelCase    )
           self.assertIsInstance(video[0]   ,					Image.Image    )

      # Test not batched input
      _a         =				image_processor(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
      _a         =				image_processor(_UpperCAmelCase   ,					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:
      _a         =				self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _a         =				prepare_video_inputs(self.image_processor_tester   ,					equal_resolution=_UpperCAmelCase   ,					numpify=_UpperCAmelCase    )
      for video in video_inputs:
           self.assertIsInstance(_UpperCAmelCase   ,					_UpperCAmelCase    )
           self.assertIsInstance(video[0]   ,					np.ndarray    )

      # Test not batched input
      _a         =				image_processor(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
      _a         =				image_processor(_UpperCAmelCase   ,					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    )       ->				Optional[Any]:
      _a         =				self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      _a         =				prepare_video_inputs(self.image_processor_tester   ,					equal_resolution=_UpperCAmelCase   ,					torchify=_UpperCAmelCase    )
      for video in video_inputs:
           self.assertIsInstance(_UpperCAmelCase   ,					_UpperCAmelCase    )
           self.assertIsInstance(video[0]   ,					torch.Tensor    )

      # Test not batched input
      _a         =				image_processor(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
      _a         =				image_processor(_UpperCAmelCase   ,					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'],
          )   ,					)





from __future__ import annotations

from typing import Any
def 			__SCREAMING_SNAKE_CASE							(					*lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if all(_check_not_integer(lowercase__					) for m in matrix_s					):
												for i in matrix_s[1:]:
																		_verify_matrix_sizes(matrix_s[0]   ,    lowercase__					)
												return [[sum(lowercase__					) for t in zip(*lowercase__					)] for m in zip(*lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if (
						    _check_not_integer(lowercase__					)
						    and _check_not_integer(lowercase__					)
						    and _verify_matrix_sizes(lowercase__   ,    lowercase__					)
						):
												return [[i - j for i, j in zip(*lowercase__					)] for m in zip(lowercase__   ,    lowercase__					)]
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int | float					)							->	list[list[float]]:
						return [[x * n for x in row] for row in matrix]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	list[list[int]]:
						if _check_not_integer(lowercase__					) and _check_not_integer(lowercase__					):
												__magic_name__		,			__magic_name__					:				str				=   _verify_matrix_sizes(lowercase__   ,    lowercase__					)

						if cols[0] != rows[1]:
												__magic_name__					:				Union[str, Any]				=   (
												    'Cannot multiply matrix of dimensions '
												    f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
												)
												raise ValueError(lowercase__					)
						return [
						    [sum(m * n for m, n in zip(lowercase__   ,    lowercase__					)					) for j in zip(*lowercase__					)] for i in matrix_a
						]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				Tuple				=   int(lowercase__					)
						return [[int(row == column					) for column in range(lowercase__					)] for row in range(lowercase__					)]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:bool = True					)							->	list[list[int]] | map[list[int]]:
						if _check_not_integer(lowercase__					):
												if return_map:
																		return map(lowercase__   ,    zip(*lowercase__					)					)
												else:
																		return list(map(lowercase__   ,    zip(*lowercase__					)					)					)
						raise TypeError('Expected a matrix, got int/list instead'					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:int   ,    lowercase__	:int					)							->	list[list[int]]:
						__magic_name__					:				int				=   matrix[:row] + matrix[row + 1 :]
						return [row[:column] + row[column + 1 :] for row in minor]
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	Any:
						if len(lowercase__					) == 1:
												return matrix[0][0]

						return sum(
						    x * determinant(minor(lowercase__   ,    0   ,    lowercase__					)					) * (-1) ** i
						    for i, x in enumerate(matrix[0]					)					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	list[list[float]] | None:
						__magic_name__					:				Tuple				=   determinant(lowercase__					)
						if det == 0:
												return None

						__magic_name__					:				Optional[int]				=   [
						    [determinant(minor(lowercase__   ,    lowercase__   ,    lowercase__					)					) for j in range(len(lowercase__					)					)]
						    for i in range(len(lowercase__					)					)
						]

						__magic_name__					:				Optional[Any]				=   [
						    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]					)]
						    for row in range(len(lowercase__					)					)
						]
						__magic_name__					:				Optional[Any]				=   list(transpose(lowercase__					)					)
						return scalar_multiply(lowercase__   ,    1 / det					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	bool:
						return not isinstance(lowercase__   ,    lowercase__					) and not isinstance(matrix[0]   ,    lowercase__					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]					)							->	tuple[int, int]:
						return len(lowercase__					), len(matrix[0]					)
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:list[list[int]]   ,    lowercase__	:list[list[int]]					)							->	tuple[tuple[int, int], tuple[int, int]]:
						__magic_name__					:				str				=   _shape(lowercase__					) + _shape(lowercase__					)
						if shape[0] != shape[3] or shape[1] != shape[2]:
												__magic_name__					:				Optional[Any]				=   (
												    'operands could not be broadcast together with shape '
												    f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
												)
												raise ValueError(lowercase__					)
						return (shape[0], shape[2]), (shape[1], shape[3])
def 			__SCREAMING_SNAKE_CASE							(					)							->	None:
						__magic_name__					:				Dict				=   [[1_2, 1_0], [3, 9]]
						__magic_name__					:				Optional[int]				=   [[3, 4], [7, 4]]
						__magic_name__					:				Optional[Any]				=   [[1_1, 1_2, 1_3, 1_4], [2_1, 2_2, 2_3, 2_4], [3_1, 3_2, 3_3, 3_4], [4_1, 4_2, 4_3, 4_4]]
						__magic_name__					:				List[str]				=   [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
						print(f"Add Operation, {add(lowercase__   ,    lowercase__					) = } \n"					)
						print(
						    f"Multiply Operation, {multiply(lowercase__   ,    lowercase__					) = } \n"   ,    )
						print(f"Identity: {identity(5					)}\n"					)
						print(f"Minor of {matrix_c} = {minor(lowercase__   ,    1   ,    2					)} \n"					)
						print(f"Determinant of {matrix_b} = {determinant(lowercase__					)} \n"					)
						print(f"Inverse of {matrix_d} = {inverse(lowercase__					)}\n"					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()


