


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """vilt"""






							def __init__(						self :     Tuple		, lowerCamelCase_ :     Optional[Any]=30522		, lowerCamelCase_ :     List[str]=2		, lowerCamelCase_ :     List[str]=2		, lowerCamelCase_ :     str=40		, lowerCamelCase_ :     Tuple=768		, lowerCamelCase_ :     Tuple=12		, lowerCamelCase_ :     str=12		, lowerCamelCase_ :     Tuple=3072		, lowerCamelCase_ :     Any="gelu"		, lowerCamelCase_ :     Union[str, Any]=0.0		, lowerCamelCase_ :     Union[str, Any]=0.0		, lowerCamelCase_ :     List[Any]=0.02		, lowerCamelCase_ :     Optional[int]=1E-12		, lowerCamelCase_ :     Optional[int]=384		, lowerCamelCase_ :     int=32		, lowerCamelCase_ :     Any=3		, lowerCamelCase_ :     Dict=True		, lowerCamelCase_ :     List[str]=-1		, lowerCamelCase_ :     List[str]=False		, lowerCamelCase_ :     Any=-1		, **lowerCamelCase_ :     Optional[int]		, ):
													super().__init__(tie_word_embeddings=lowerCamelCase_		, **lowerCamelCase_				)

													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    type_vocab_size
													UpperCAmelCase__		=    modality_type_vocab_size
													UpperCAmelCase__		=    max_position_embeddings

													UpperCAmelCase__		=    hidden_size
													UpperCAmelCase__		=    num_hidden_layers
													UpperCAmelCase__		=    num_attention_heads
													UpperCAmelCase__		=    intermediate_size
													UpperCAmelCase__		=    hidden_act
													UpperCAmelCase__		=    hidden_dropout_prob
													UpperCAmelCase__		=    attention_probs_dropout_prob
													UpperCAmelCase__		=    initializer_range
													UpperCAmelCase__		=    layer_norm_eps

													UpperCAmelCase__		=    image_size
													UpperCAmelCase__		=    patch_size
													UpperCAmelCase__		=    num_channels
													UpperCAmelCase__		=    qkv_bias
													UpperCAmelCase__		=    max_image_length
													UpperCAmelCase__		=    num_images



"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase = 100	,			):





						"""simple docstring"""

						UpperCAmelCase__		=    x_start
						UpperCAmelCase__		=    fnc(_UpperCamelCase )
						UpperCAmelCase__		=    0.0

						for _ in range(_UpperCamelCase ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												UpperCAmelCase__		=    (x_end - x_start) / steps + xa
												UpperCAmelCase__		=    fnc(_UpperCamelCase )
												area += abs(fxa + fxa ) * (xa - xa) / 2

												# Increment step
												UpperCAmelCase__		=    xa
												UpperCAmelCase__		=    fxa
						return area


if __name__ == "__main__":






						def 					a__ (       _UpperCamelCase ):





												"""simple docstring"""

												return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						__a			 =   10
						while i <= 10_00_00:
												__a			 =   trapezoidal_area(f, -5, 5, i)
												print(f'''with {i} steps: {area}''')
												i *= 10