


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

												# All transformations expect numpy arrays.
												a__    =  [to_numpy_array(__snake_case   ) for img in images]

												if do_resize:
																			a__    =  [self.resize(__snake_case    ,size_divisor=__snake_case    ,resample=__snake_case   ) for image in images]

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )








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()




