
'''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 MobileNetVaImageProcessor







class  __A				( unittest.TestCase			):
 def __init__(   self			:    Any				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    List[Any]=7				,   lowerCamelCase			:    Tuple=3				,   lowerCamelCase			:    Optional[int]=18				,   lowerCamelCase			:    List[Any]=30				,   lowerCamelCase			:    Tuple=4_00				,   lowerCamelCase			:    Optional[Any]=True				,   lowerCamelCase			:    int=None				,   lowerCamelCase			:    List[str]=True				,   lowerCamelCase			:    Optional[int]=None				,   ):



   """simple docstring"""





   __A		:     Dict												=		size if size is not None else {"""shortest_edge""": 20}
   __A		:     List[str]												=		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
   __A		:     str												=		parent
   __A		:     Optional[Any]												=		batch_size
   __A		:     Union[str, Any]												=		num_channels
   __A		:     Optional[int]												=		image_size
   __A		:     Dict												=		min_resolution
   __A		:     Union[str, Any]												=		max_resolution
   __A		:     Any												=		do_resize
   __A		:     Dict												=		size
   __A		:     Union[str, Any]												=		do_center_crop
   __A		:     Dict												=		crop_size






 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
   }







@require_torch
@require_vision
class  __A				( _UpperCAmelCase    ,						unittest.TestCase			):
 lowerCamelCase				       =MobileNetVaImageProcessor if is_vision_available() else None
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		MobileNetVaImageProcessingTester(self       )
 @property
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   return self.image_processor_tester.prepare_image_processor_dict()
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     List[str]												=		self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(lowercase__				,   """do_resize"""       )       )
   self.assertTrue(hasattr(lowercase__				,   """size"""       )       )
   self.assertTrue(hasattr(lowercase__				,   """do_center_crop"""       )       )
   self.assertTrue(hasattr(lowercase__				,   """crop_size"""       )       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     Tuple												=		self.image_processing_class.from_dict(self.image_processor_dict       )
   self.assertEqual(image_processor.size				,   {"""shortest_edge""": 20}       )
   self.assertEqual(image_processor.crop_size				,   {"""height""": 18, """width""": 18}       )

   __A		:     Union[str, Any]												=		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 		lowercase_(   self			:    int       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     Tuple												=		self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __A		:     Optional[int]												=		prepare_image_inputs(self.image_processor_tester				,   equal_resolution=lowercase__       )
   for image in image_inputs:
     self.assertIsInstance(lowercase__				,   Image.Image       )

   # Test not batched input
   __A		:     Any												=		image_processing(image_inputs[0]				,   return_tensors="""pt"""       ).pixel_values
   self.assertEqual(
       encoded_images.shape				,   (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )				,   )

   # Test batched
   __A		:     int												=		image_processing(lowercase__				,   return_tensors="""pt"""       ).pixel_values
   self.assertEqual(
       encoded_images.shape				,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )				,   )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __A		:     Tuple												=		prepare_image_inputs(self.image_processor_tester				,   equal_resolution=lowercase__				,   numpify=lowercase__       )
   for image in image_inputs:
     self.assertIsInstance(lowercase__				,   np.ndarray       )

   # Test not batched input
   __A		:     Dict												=		image_processing(image_inputs[0]				,   return_tensors="""pt"""       ).pixel_values
   self.assertEqual(
       encoded_images.shape				,   (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )				,   )

   # Test batched
   __A		:     List[Any]												=		image_processing(lowercase__				,   return_tensors="""pt"""       ).pixel_values
   self.assertEqual(
       encoded_images.shape				,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )				,   )






 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Tuple												=		self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __A		:     int												=		prepare_image_inputs(self.image_processor_tester				,   equal_resolution=lowercase__				,   torchify=lowercase__       )
   for image in image_inputs:
     self.assertIsInstance(lowercase__				,   torch.Tensor       )

   # Test not batched input
   __A		:     Tuple												=		image_processing(image_inputs[0]				,   return_tensors="""pt"""       ).pixel_values
   self.assertEqual(
       encoded_images.shape				,   (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )				,   )

   # Test batched
   __A		:     int												=		image_processing(lowercase__				,   return_tensors="""pt"""       ).pixel_values
   self.assertEqual(
       encoded_images.shape				,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )				,   )





'''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()



