





"""simple docstring"""






def       __A (     a_  :int)						->						None:
				__a	:  List[Any]         =					generate_pascal_triangle(a_)
				for row_idx in range(a_):
								# 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       __A (     a_  :int)						->						list[list[int]]:

				if not isinstance(a_				,    a_):
								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	:  list[list[int]]         =					[]
				for current_row_idx in range(a_):
								__a	:  int         =					populate_current_row(a_				,    a_)
								triangle.append(a_)
				return triangle

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

def       __A (     a_  :list[list[int]]				,    a_  :list[int]				,    a_  :int				,    a_  :int				,    )						->						None:
				__a	:  Dict         =					triangle[current_row_idx - 1][current_col_idx - 1]
				__a	:  Optional[Any]         =					triangle[current_row_idx - 1][current_col_idx]
				__a	:  List[Any]         =					above_to_left_elt + above_to_right_elt

def       __A (     a_  :int)						->						list[list[int]]:

				if not isinstance(a_				,    a_):
								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	:  list[list[int]]         =					[[1]]

				for row_index in range(1				,    a_):
								__a	:  str         =					[0] + result[-1] + [0]
								__a	:  Tuple         =					row_index + 1
								# Calculate the number of distinct elements in a row
								__a	:  Any         =					sum(divmod(a_				,    2))
								__a	:  List[str]         =					[
								    temp_row[i - 1] + temp_row[i] for i in range(1				,    distinct_elements + 1)
								]
								__a	:  List[str]         =					row_first_half[: (row_index + 1) // 2]
								row_second_half.reverse()
								__a	:  Optional[int]         =					row_first_half + row_second_half
								result.append(a_)

				return result

def       __A (     )						->						None:
				from collections.abc import Callable
				from timeit import timeit

				def benchmark_a_function(a_  :Callable				,    a_  :int) -> None:
								__a	:  Tuple         =					F"""{func.__name__}({value})"""
								__a	:  Union[str, Any]         =					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(15):  # (1, 7, 14):
								for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
												benchmark_a_function(a_				,    a_)
								print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()






"""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_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=3		,     _UpperCAmelCase=224		,     _UpperCAmelCase=30		,     _UpperCAmelCase=400		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=True		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     ):
									__a	:  int         =					size if size is not None else {'''height''': 18, '''width''': 18}
									__a	:  List[Any]         =					parent
									__a	:  Dict         =					batch_size
									__a	:  Dict         =					num_channels
									__a	:  int         =					image_size
									__a	:  Optional[Any]         =					min_resolution
									__a	:  Optional[int]         =					max_resolution
									__a	:  Dict         =					do_resize
									__a	:  List[Any]         =					size
									__a	:  int         =					do_normalize
									__a	:  Optional[Any]         =					image_mean
									__a	:  int         =					image_std







					def 	_lowerCamelCase						(  self       ):
									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 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					ViTImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					EfficientFormerImageProcessorTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_proc_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					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		,     '''size'''       )       )







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Any         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     Image.Image       )

									# Test not batched input
									__a	:  Dict         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Any         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     numpify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     np.ndarray       )

									# Test not batched input
									__a	:  Optional[int]         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  str         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  int         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  List[Any]         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     torchify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     torch.Tensor       )

									# Test not batched input
									__a	:  str         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Tuple         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )