

def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       generate_pascal_triangle(_UpperCAmelCase    )
						for row_idx in range(_UpperCAmelCase    ):
												# 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_      (    _UpperCAmelCase    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												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"    )

						SCREAMING_SNAKE_CASE_: list[list[int]]  		=       []
						for current_row_idx in range(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: List[Any]  		=       populate_current_row(_UpperCAmelCase				,	_UpperCAmelCase    )
												triangle.append(_UpperCAmelCase    )
						return triangle







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       [-1] * (current_row_idx + 1)
						# first and last elements of current row are equal to 1
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       1, 1
						for current_col_idx in range(1				,	_UpperCAmelCase    ):
												calculate_current_element(
												    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						return current_row







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	):
						SCREAMING_SNAKE_CASE_: str  		=       triangle[current_row_idx - 1][current_col_idx - 1]
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       triangle[current_row_idx - 1][current_col_idx]
						SCREAMING_SNAKE_CASE_: str  		=       above_to_left_elt + above_to_right_elt







def 			A_      (    _UpperCAmelCase    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												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"    )

						SCREAMING_SNAKE_CASE_: list[list[int]]  		=       [[1]]

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

						return result







def 			A_      (    ):
						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_UpperCAmelCase				,	_UpperCAmelCase    ) -> None:
												SCREAMING_SNAKE_CASE_: int  		=       f"{func.__name__}({value})"
												SCREAMING_SNAKE_CASE_: List[str]  		=       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(_UpperCAmelCase				,	_UpperCAmelCase    )
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__lowerCamelCase							=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase							=       {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": 5_12,
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     VOCAB_FILES_NAMES
   A__					:		Optional[int]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Tuple						     =     PRETRAINED_INIT_CONFIGURATION
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		List[Any]						     =     LxmertTokenizer



   def __init__(self		:					Dict    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple="[UNK]"    ,						snake_case__		:					Optional[Any]="[SEP]"    ,						snake_case__		:					Optional[Any]="[PAD]"    ,						snake_case__		:					List[Any]="[CLS]"    ,						snake_case__		:					Tuple="[MASK]"    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Union[str, Any]=None    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(
              snake_case__    ,						tokenizer_file=snake_case__    ,						do_lower_case=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						pad_token=snake_case__    ,						cls_token=snake_case__    ,						mask_token=snake_case__    ,						tokenize_chinese_chars=snake_case__    ,						strip_accents=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   int							=      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get("lowercase"    ,						snake_case__ ) != do_lower_case
              or normalizer_state.get("strip_accents"    ,						snake_case__ ) != strip_accents
              or normalizer_state.get("handle_chinese_chars"    ,						snake_case__ ) != tokenize_chinese_chars
          ):
                 snake_case       :   Union[str, Any]							=      getattr(snake_case__    ,						normalizer_state.pop("type" ) )
                 snake_case       :   str							=      do_lower_case
                 snake_case       :   List[Any]							=      strip_accents
                 snake_case       :   Optional[int]							=      tokenize_chinese_chars
                 snake_case       :   int							=      normalizer_class(**snake_case__ )

          snake_case       :   Optional[Any]							=      do_lower_case



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict=None )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

          return output



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      [self.sep_token_id]
          snake_case       :   Optional[Any]							=      [self.cls_token_id]
          if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self._tokenizer.model.save(snake_case__    ,						name=snake_case__ )
          return tuple(snake_case__ )





