
def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Any       =      generate_pascal_triangle(_lowercase				)
     for row_idx in range(_lowercase				):
          # 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 lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if not isinstance(_lowercase			,						_lowercase				):
          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'''				)

     UpperCAmelCase_      :							list[list[int]]       =      []
     for current_row_idx in range(_lowercase				):
          UpperCAmelCase_      :							Any       =      populate_current_row(_lowercase			,						_lowercase				)
          triangle.append(_lowercase				)
     return triangle







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      triangle[current_row_idx - 1][current_col_idx - 1]
     UpperCAmelCase_      :							Any       =      triangle[current_row_idx - 1][current_col_idx]
     UpperCAmelCase_      :							Any       =      above_to_left_elt + above_to_right_elt







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if not isinstance(_lowercase			,						_lowercase				):
          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'''				)

     UpperCAmelCase_      :							list[list[int]]       =      [[1]]

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

     return result







def lowerCamelCase__       (      ):

     '''simple docstring'''


     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(_lowercase			,						_lowercase				) -> None:
          UpperCAmelCase_      :							Dict       =      f'''{func.__name__}({value})'''
          UpperCAmelCase_      :							List[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(_lowercase			,						_lowercase				)
          print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[int]       =      tempfile.mkdtemp()

         UpperCAmelCase_      :							str       =      BlipImageProcessor()
         UpperCAmelCase_      :							Dict       =      GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''  )

         UpperCAmelCase_      :							Optional[Any]       =      BlipaProcessor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         processor.save_pretrained(self.tmpdirname  )





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).tokenizer





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).image_processor





    def 							a__				(				self  )			->		List[Any]:
         shutil.rmtree(self.tmpdirname  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Tuple       =      [np.random.randint(255       ,size=(3, 30, 400)       ,dtype=np.uinta  )]

         UpperCAmelCase_      :							Optional[Any]       =      [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE       ,0       ,-1  )  ) for x in image_inputs]

         return image_inputs





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Dict       =      BlipaProcessor(tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor()  )
         processor.save_pretrained(self.tmpdirname  )

         UpperCAmelCase_      :							str       =      self.get_tokenizer(bos_token='''(BOS)'''       ,eos_token='''(EOS)'''  )
         UpperCAmelCase_      :							int       =      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         UpperCAmelCase_      :							Union[str, Any]       =      BlipaProcessor.from_pretrained(
             self.tmpdirname       ,bos_token='''(BOS)'''       ,eos_token='''(EOS)'''       ,do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer_add_kwargs.get_vocab()  )
         self.assertIsInstance(processor.tokenizer       ,_SCREAMING_SNAKE_CASE  )

         self.assertEqual(processor.image_processor.to_json_string()       ,image_processor_add_kwargs.to_json_string()  )
         self.assertIsInstance(processor.image_processor       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Dict       =      self.get_image_processor()
         UpperCAmelCase_      :							Any       =      self.get_tokenizer()

         UpperCAmelCase_      :							str       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Optional[Any]       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							int       =      processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1e-2  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      '''lower newer'''

         UpperCAmelCase_      :							Optional[int]       =      processor(text=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      tokenizer(_SCREAMING_SNAKE_CASE       ,return_token_type_ids=_SCREAMING_SNAKE_CASE  )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Tuple       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      '''lower newer'''
         UpperCAmelCase_      :							int       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							List[str]       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )

         # test if it raises when no input is passed
         with pytest.raises(_SCREAMING_SNAKE_CASE  ):
              processor()





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      self.get_image_processor()
         UpperCAmelCase_      :							Dict       =      self.get_tokenizer()

         UpperCAmelCase_      :							List[str]       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_      :							List[str]       =      processor.batch_decode(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							Union[str, Any]       =      self.get_image_processor()
         UpperCAmelCase_      :							int       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      '''lower newer'''
         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Any       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )