





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """data2vec-text"""


   def __init__( self    ,   _UpperCAmelCase=30_522    ,   _UpperCAmelCase=768    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=3_072    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=512    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=1E-1_2    ,   _UpperCAmelCase=1    ,   _UpperCAmelCase=0    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase="absolute"    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(pad_token_id=_UpperCAmelCase    ,   bos_token_id=_UpperCAmelCase    ,   eos_token_id=_UpperCAmelCase    ,   **_UpperCAmelCase					)

       A       :       int							=						vocab_size
       A       :       List[str]							=						hidden_size
       A       :       Dict							=						num_hidden_layers
       A       :       Tuple							=						num_attention_heads
       A       :       Union[str, Any]							=						hidden_act
       A       :       Tuple							=						intermediate_size
       A       :       str							=						hidden_dropout_prob
       A       :       Optional[int]							=						attention_probs_dropout_prob
       A       :       str							=						max_position_embeddings
       A       :       Optional[int]							=						type_vocab_size
       A       :       Optional[Any]							=						initializer_range
       A       :       List[str]							=						layer_norm_eps
       A       :       Optional[Any]							=						position_embedding_type
       A       :       Dict							=						use_cache
       A       :       Any							=						classifier_dropout







class   _lowercase						(							a				):


   @property
   def 					snake_case				( self					):
       if self.task == "multiple-choice":
           A       :       Union[str, Any]							=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
           A       :       List[str]							=						{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]					)







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  None:
    A       :       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 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[list[int]]:

    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'''		)

    A       :       list[list[int]]							=						[]
    for current_row_idx in range(UpperCamelCase__		):
        A       :       Optional[int]							=						populate_current_row(UpperCamelCase__    ,       UpperCamelCase__		)
        triangle.append(UpperCamelCase__		)
    return triangle
def 							_lowerCamelCase(  UpperCamelCase__    : list[list[int]]    ,       UpperCamelCase__    : int		)		->  list[int]:
    A       :       Dict							=						[-1] * (current_row_idx + 1)
    # first and last elements of current row are equal to 1
    A,	A       :       Union[str, Any]							=						1, 1
    for current_col_idx in range(1    ,       UpperCamelCase__		):
        calculate_current_element(
            UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    return current_row
def 							_lowerCamelCase(  UpperCamelCase__    : list[list[int]]    ,       UpperCamelCase__    : list[int]    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : int    ,       )		->  None:
    A       :       Dict							=						triangle[current_row_idx - 1][current_col_idx - 1]
    A       :       Any							=						triangle[current_row_idx - 1][current_col_idx]
    A       :       Optional[int]							=						above_to_left_elt + above_to_right_elt
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[list[int]]:

    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'''		)

    A       :       list[list[int]]							=						[[1]]

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

    return result
def 							_lowerCamelCase(  )		->  None:
    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(UpperCamelCase__    : Callable    ,       UpperCamelCase__    : int		) -> None:
        A       :       Dict							=						F'''{func.__name__}({value})'''
        A       :       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(UpperCamelCase__    ,       UpperCamelCase__		)
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()
