


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   ):
					def __init__(   self     :str    ,__snake_case     :Any    ,__snake_case     :List[Any]=7    ,__snake_case     :Dict=3    ,__snake_case     :Dict=10    ,__snake_case     :Dict=18    ,__snake_case     :Any=30    ,__snake_case     :int=4_00    ,__snake_case     :Union[str, Any]=True    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Any=True    ,__snake_case     :int=[0.5, 0.5, 0.5]    ,__snake_case     :List[Any]=[0.5, 0.5, 0.5]    ,__snake_case     :Any=None    ,)      ->					Union[str, Any]:
												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__    =  crop_size



					def 				lowerCamelCase__(   self     :List[str]   )      ->					Dict:
												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_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					List[str]					   =    VivitImageProcessor if is_vision_available() else None
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												a__    =  VivitImageProcessingTester(self   )
					@property
					def 				lowerCamelCase__(   self     :List[str]   )      ->					List[Any]:
												return self.image_processor_tester.prepare_image_processor_dict()
					def 				lowerCamelCase__(   self     :str   )      ->					Dict:
												a__    =  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    ,'do_center_crop'   )   )
												self.assertTrue(hasattr(__snake_case    ,'size'   )   )
					def 				lowerCamelCase__(   self     :Dict   )      ->					str:
												a__    =  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}   )

												a__    =  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 				lowerCamelCase__(   self     :Any   )      ->					int:
												# Initialize image_processing
												a__    =  self.image_processing_class(**self.image_processor_dict   )
												# create random PIL videos
												a__    =  prepare_video_inputs(self.image_processor_tester    ,equal_resolution=__snake_case   )
												for video in video_inputs:
																			self.assertIsInstance(__snake_case    ,__snake_case   )
																			self.assertIsInstance(video[0]    ,Image.Image   )

												# Test not batched input
												a__    =  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
												a__    =  image_processing(__snake_case    ,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 				lowerCamelCase__(   self     :str   )      ->					Any:
												# Initialize image_processing
												a__    =  self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												a__    =  prepare_video_inputs(self.image_processor_tester    ,equal_resolution=__snake_case    ,numpify=__snake_case   )
												for video in video_inputs:
																			self.assertIsInstance(__snake_case    ,__snake_case   )
																			self.assertIsInstance(video[0]    ,np.ndarray   )

												# Test not batched input
												a__    =  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
												a__    =  image_processing(__snake_case    ,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 				lowerCamelCase__(   self     :Tuple   )      ->					Any:
												# Initialize image_processing
												a__    =  self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												a__    =  prepare_video_inputs(self.image_processor_tester    ,equal_resolution=__snake_case    ,torchify=__snake_case   )
												for video in video_inputs:
																			self.assertIsInstance(__snake_case    ,__snake_case   )
																			self.assertIsInstance(video[0]    ,torch.Tensor   )

												# Test not batched input
												a__    =  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
												a__    =  image_processing(__snake_case    ,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 							__lowercase     (				__lowerCAmelCase							:       int     ):
							a__    =  generate_pascal_triangle(__lowerCAmelCase     )
							for row_idx in range(__lowerCAmelCase     ):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1     ):
																					print(end=' '     )
														# Print row values
														for col_idx in range(row_idx + 1     ):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]      ,					end=' '     )
																					else:
																												print(triangle[row_idx][col_idx]      ,					end=''     )
														print()






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise TypeError('The input value of \'num_rows\' should be \'int\''     )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'     )

							a__    =  []
							for current_row_idx in range(__lowerCAmelCase     ):
														a__    =  populate_current_row(__lowerCAmelCase      ,					__lowerCAmelCase     )
														triangle.append(__lowerCAmelCase     )
							return triangle






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       int     ):
							a__    =  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							a__   ,      a__    =  1, 1
							for current_col_idx in range(1      ,					__lowerCAmelCase     ):
														calculate_current_element(
														    __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							return current_row






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       list[int]      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					):
							a__    =  triangle[current_row_idx - 1][current_col_idx - 1]
							a__    =  triangle[current_row_idx - 1][current_col_idx]
							a__    =  above_to_left_elt + above_to_right_elt






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise TypeError('The input value of \'num_rows\' should be \'int\''     )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'     )

							a__    =  [[1]]

							for row_index in range(1      ,					__lowerCAmelCase     ):
														a__    =  [0] + result[-1] + [0]
														a__    =  row_index + 1
														# Calculate the number of distinct elements in a row
														a__    =  sum(divmod(__lowerCAmelCase      ,					2     )     )
														a__    =  [
														    temp_row[i - 1] + temp_row[i] for i in range(1      ,					distinct_elements + 1     )
														]
														a__    =  row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														a__    =  row_first_half + row_second_half
														result.append(__lowerCAmelCase     )

							return result






def 							__lowercase     (				):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(__lowerCAmelCase							:       Callable      ,					__lowerCAmelCase							:       int     ) -> None:
														a__    =  F'{func.__name__}({value})'
														a__    =  timeit(F'__main__.{call}'      ,					setup='import __main__'     )
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F'{call:38} -- {timing:.4f} seconds'     )

							for value in range(1_5     ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(__lowerCAmelCase      ,					__lowerCAmelCase     )
														print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()




