





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 VivitImageProcessor







class    __snake_case	(							unittest.TestCase      ):





							'''simple docstring'''







							def __init__(  self			:   Optional[int]							,    A			:   Optional[Any]							,    A			:   Any=7							,    A			:   int=3							,    A			:   Optional[Any]=10							,    A			:   Optional[int]=18							,    A			:   Optional[Any]=30							,    A			:   List[str]=400							,    A			:   Union[str, Any]=True							,    A			:   int=None							,    A			:   Optional[Any]=True							,    A			:   Dict=[0.5, 0.5, 0.5]							,    A			:   Any=[0.5, 0.5, 0.5]							,    A			:   Optional[int]=None							,    ):
									__snake_case:						Dict			  =						size if size is not None else {"""shortest_edge""": 18}
									__snake_case:						List[str]			  =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									__snake_case:						Union[str, Any]			  =						parent
									__snake_case:						Tuple			  =						batch_size
									__snake_case:						Optional[Any]			  =						num_channels
									__snake_case:						List[str]			  =						num_frames
									__snake_case:						List[Any]			  =						image_size
									__snake_case:						Dict			  =						min_resolution
									__snake_case:						str			  =						max_resolution
									__snake_case:						Optional[int]			  =						do_resize
									__snake_case:						int			  =						size
									__snake_case:						Tuple			  =						do_normalize
									__snake_case:						Any			  =						image_mean
									__snake_case:						Tuple			  =						image_std
									__snake_case:						int			  =						crop_size






							def 							UpperCAmelCase__			(  self			:   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,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class    __snake_case	(							__A       ,      unittest.TestCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					VivitImageProcessor if is_vision_available() else None




							def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
									__snake_case:						Tuple			  =						VivitImageProcessingTester(self		)




							@property
							def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
									return self.image_processor_tester.prepare_image_processor_dict()




							def 							UpperCAmelCase__			(  self			:   Any		):
									__snake_case:						int			  =						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			:   Tuple		):
									__snake_case:						Union[str, Any]			  =						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}		)

									__snake_case:						List[str]			  =						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			:   Dict		):
									# Initialize image_processing
									__snake_case:						Dict			  =						self.image_processing_class(**self.image_processor_dict		)
									# create random PIL videos
									__snake_case:						Tuple			  =						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
									__snake_case:						Optional[int]			  =						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
									__snake_case:						List[str]			  =						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			:   List[Any]		):
									# Initialize image_processing
									__snake_case:						int			  =						self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									__snake_case:						Tuple			  =						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
									__snake_case:						str			  =						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
									__snake_case:						Dict			  =						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			:   List[Any]		):
									# Initialize image_processing
									__snake_case:						int			  =						self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									__snake_case:						Dict			  =						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
									__snake_case:						Union[str, Any]			  =						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
									__snake_case:						List[str]			  =						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"""],
									    )							,    )













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						float(
        d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creates a copy of the matrix with swapped positions of the elements
    __snake_case:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 3
      and len(matrix[0]) == 3
      and len(matrix[1]) == 3
      and len(matrix[2]) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    __snake_case:						Any			  =						float(
        (
            (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
            + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
            + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
        )
        - (
            (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
            + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
            + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
        ))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creating cofactor matrix
    __snake_case:						Tuple			  =						[
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
    ]
    __snake_case:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__)

        # Calculate the inverse of the matrix
    return [[float(d(SCREAMING_SNAKE_CASE__)) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")






