


"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _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       A_   (   _lowerCAmelCase     :					int       ):






     """simple docstring"""

     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       A_   (   _lowerCAmelCase     :					list[list[int]],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _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       A_   (   _lowerCAmelCase     :					list[list[int]],     _lowerCAmelCase     :					list[int],     _lowerCAmelCase     :					int,     _lowerCAmelCase     :					int,     ):






     """simple docstring"""

     _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       A_   (   _lowerCAmelCase     :					int       ):






     """simple docstring"""

     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       A_   (   ):






     """simple docstring"""

     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(15       ):  # (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()



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
   )
   parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument(
       '''--hf_config''',
       default='''facebook/mbart-large-cc25''',
       type=str,
       help='''Which huggingface architecture to use: mbart-large''',
   )
   parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)