


def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						None:




						"""simple docstring"""

						snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						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'''      )

						snake_case_ =   []
						for current_row_idx in range(SCREAMING_SNAKE_CASE      ):
												snake_case_ =   populate_current_row(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
												triangle.append(SCREAMING_SNAKE_CASE      )
						return triangle






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						list[int]:




						"""simple docstring"""

						snake_case_ =   [-1] * (current_row_idx + 1)
						# first and last elements of current row are equal to 1
						snake_case_ ,					snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      )->						None:




						"""simple docstring"""

						snake_case_ =   triangle[current_row_idx - 1][current_col_idx - 1]
						snake_case_ =   triangle[current_row_idx - 1][current_col_idx]
						snake_case_ =   above_to_left_elt + above_to_right_elt






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						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'''      )

						snake_case_ =   [[1]]

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

						return result






def  __lowerCAmelCase					()->						None:




						"""simple docstring"""

						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ) -> None:
												snake_case_ =   f'''{func.__name__}({value})'''
												snake_case_ =   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()



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=2	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase="cls"	,   _UpperCAmelCase=False	,   _UpperCAmelCase=True	,   **_UpperCAmelCase	,   ):
       super().__init__(pad_token_id=_UpperCAmelCase	,   bos_token_id=_UpperCAmelCase	,   eos_token_id=_UpperCAmelCase	,   **_UpperCAmelCase      )
       snake_case_ =   project_dim
       snake_case_ =   pooler_fn
       snake_case_ =   learn_encoder
       snake_case_ =   use_attention_mask







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						[R"pooler", R"logit_scale"]
 __snake_case				=						[R"position_ids", R"predictions.decoder.bias"]
 __snake_case				=						"roberta"
 __snake_case				=						RobertaSeriesConfig



 def __init__(			self	,   _UpperCAmelCase      ):
       super().__init__(_UpperCAmelCase      )
       snake_case_ =   XLMRobertaModel(_UpperCAmelCase      )
       snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
       snake_case_ =   getattr(_UpperCAmelCase	,   '''has_pre_transformation'''	,   _UpperCAmelCase      )
       if self.has_pre_transformation:
             snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
             snake_case_ =   nn.LayerNorm(config.hidden_size	,   eps=config.layer_norm_eps      )
       self.post_init()





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):
       snake_case_ =   return_dict if return_dict is not None else self.config.use_return_dict

       snake_case_ =   self.base_model(
           input_ids=_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   position_ids=_UpperCAmelCase	,   head_mask=_UpperCAmelCase	,   inputs_embeds=_UpperCAmelCase	,   encoder_hidden_states=_UpperCAmelCase	,   encoder_attention_mask=_UpperCAmelCase	,   output_attentions=_UpperCAmelCase	,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,   return_dict=_UpperCAmelCase	,   )

       if self.has_pre_transformation:
             snake_case_ =   outputs['''hidden_states'''][-2]
             snake_case_ =   self.pre_LN(_UpperCAmelCase      )
             snake_case_ =   self.transformation_pre(_UpperCAmelCase      )

             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )
       else:
             snake_case_ =   self.transformation(outputs.last_hidden_state      )
             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )