
'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


A__   :		List[str] 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''pixel_values''']
 def __init__(   self			:    Optional[Any]				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    PILImageResampling = PIL.Image.BICUBIC				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    Union[int, float] = 1 / 2_55				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )
   __A		:     Optional[int]												=		size if size is not None else {"""height""": 2_56, """width""": 2_56}
   __A		:     Tuple												=		get_size_dict(lowerCamelCase       )
   __A		:     Union[str, Any]												=		crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
   __A		:     Optional[Any]												=		get_size_dict(lowerCamelCase				,   param_name="""crop_size"""       )

   __A		:     int												=		do_resize
   __A		:     Any												=		size
   __A		:     Union[str, Any]												=		resample
   __A		:     Tuple												=		do_center_crop
   __A		:     Tuple												=		crop_size
   __A		:     str												=		do_rescale
   __A		:     List[Any]												=		rescale_factor
   __A		:     Optional[Any]												=		do_normalize
   __A		:     Union[str, Any]												=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __A		:     Optional[int]												=		image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Dict[str, int]				,   lowerCamelCase			:    PILImageResampling = PIL.Image.BICUBIC				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    int				,   ):



   """simple docstring"""





   __A		:     List[str]												=		get_size_dict(lowerCamelCase       )
   if "height" not in size or "width" not in size:
     raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"       )
   return resize(
       lowerCamelCase				,   size=(size["""height"""], size["""width"""])				,   resample=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Dict[str, int]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    Tuple				,   ):



   """simple docstring"""





   __A		:     List[Any]												=		get_size_dict(lowerCamelCase       )
   if "height" not in size or "width" not in size:
     raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"       )
   return center_crop(lowerCamelCase				,   size=(size["""height"""], size["""width"""])				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Union[int, float]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   return rescale(lowerCamelCase				,   scale=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    Union[float, List[float]]				,   lowerCamelCase			:    Union[float, List[float]]				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   return normalize(lowerCamelCase				,   mean=lowerCamelCase				,   std=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )






 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    ImageInput				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    List[Any]=None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Dict[str, int] = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    bool = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[float, List[float]]] = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    ChannelDimension = ChannelDimension.FIRST				,   **lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   __A		:     List[str]												=		do_resize if do_resize is not None else self.do_resize
   __A		:     Union[str, Any]												=		resample if resample is not None else self.resample
   __A		:     Union[str, Any]												=		do_center_crop if do_center_crop is not None else self.do_center_crop
   __A		:     Tuple												=		do_rescale if do_rescale is not None else self.do_rescale
   __A		:     Optional[Any]												=		rescale_factor if rescale_factor is not None else self.rescale_factor
   __A		:     str												=		do_normalize if do_normalize is not None else self.do_normalize
   __A		:     Optional[int]												=		image_mean if image_mean is not None else self.image_mean
   __A		:     Optional[int]												=		image_std if image_std is not None else self.image_std

   __A		:     Any												=		size if size is not None else self.size
   __A		:     Dict												=		get_size_dict(lowerCamelCase       )
   __A		:     Tuple												=		crop_size if crop_size is not None else self.crop_size
   __A		:     Optional[Any]												=		get_size_dict(lowerCamelCase				,   param_name="""crop_size"""       )

   __A		:     Any												=		make_list_of_images(lowerCamelCase       )

   if not valid_images(lowerCamelCase       ):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""       )

   if do_resize and size is None or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize is True."""       )

   if do_center_crop and crop_size is None:
     raise ValueError("""Crop size must be specified if do_center_crop is True."""       )

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

   # All transformations expect numpy arrays.
   __A		:     List[str]												=		[to_numpy_array(lowerCamelCase       ) for image in images]

   if do_resize:
     __A		:     Optional[int]												=		[self.resize(image=lowerCamelCase				,   size=lowerCamelCase				,   resample=lowerCamelCase       ) for image in images]

   if do_center_crop:
     __A		:     Dict												=		[self.center_crop(image=lowerCamelCase				,   size=lowerCamelCase       ) for image in images]

   if do_rescale:
     __A		:     Optional[Any]												=		[self.rescale(image=lowerCamelCase				,   scale=lowerCamelCase       ) for image in images]

   if do_normalize:
     __A		:     str												=		[self.normalize(image=lowerCamelCase				,   mean=lowerCamelCase				,   std=lowerCamelCase       ) for image in images]

   __A		:     List[str]												=		[to_channel_dimension_format(lowerCamelCase				,   lowerCamelCase       ) for image in images]

   __A		:     Any												=		{"""pixel_values""": images}
   return BatchFeature(data=lowerCamelCase				,   tensor_type=lowerCamelCase       )





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  __A		:     Tuple												=		generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
  for row_idx in range(__SCREAMING_SNAKE_CASE ):
    # 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_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		populate_current_row(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    triangle.append(__SCREAMING_SNAKE_CASE )
  return triangle







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  int )				->       list[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		[-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  __A							,					__A		:     Tuple												=		1, 1
  for current_col_idx in range(1			,     __SCREAMING_SNAKE_CASE ):
    calculate_current_element(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return current_row







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  list[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     )				->       None:





  """simple docstring"""

  __A		:     str												=		triangle[current_row_idx - 1][current_col_idx - 1]
  __A		:     Union[str, Any]												=		triangle[current_row_idx - 1][current_col_idx]
  __A		:     Any												=		above_to_left_elt + above_to_right_elt







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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			,     __SCREAMING_SNAKE_CASE ):
    __A		:     Optional[Any]												=		[0] + result[-1] + [0]
    __A		:     Optional[Any]												=		row_index + 1
    # Calculate the number of distinct elements in a row
    __A		:     int												=		sum(divmod(__SCREAMING_SNAKE_CASE			,     2 ) )
    __A		:     str												=		[
        temp_row[i - 1] + temp_row[i] for i in range(1			,     distinct_elements + 1 )
    ]
    __A		:     Tuple												=		row_first_half[: (row_index + 1) // 2]
    row_second_half.reverse()
    __A		:     Any												=		row_first_half + row_second_half
    result.append(__SCREAMING_SNAKE_CASE )

  return result







def        A_     (						)				->       None:





  """simple docstring"""

  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  int ) -> None:
    __A		:     List[Any]												=		F"{func.__name__}({value})"
    __A		:     Dict												=		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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



