


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


__lowercase	:						int       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "roberta-prelayernorm"




	def __init__(   self						,				__a=5_0265						,				__a=768						,				__a=12						,				__a=12						,				__a=3072						,				__a="gelu"						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=2						,				__a=0.02						,				__a=1E-1_2						,				__a=1						,				__a=0						,				__a=2						,				__a="absolute"						,				__a=True						,				__a=None						,				**__a						,				):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,				bos_token_id=__a						,				eos_token_id=__a						,				**__a							)

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





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__a			:       List[Any]					    =  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__a			:       Any					    =  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)








'''simple docstring'''





from __future__ import annotations

import math
from collections.abc import Callable

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 100       ,)      ->      float:
  __lowerCamelCase       :       Dict           = x_start
  __lowerCamelCase       :       int           = fnc(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = 0.0

  for _ in range(_lowerCAmelCase       ):
    # Approximates curve as a sequence of linear lines and sums their length
    __lowerCamelCase       :       List[str]           = (x_end - x_start) / steps + xa
    __lowerCamelCase       :       List[Any]           = fnc(_lowerCAmelCase       )
    length += math.hypot(xa - xa       ,fxa - fxa       )

    # Increment step
    __lowerCamelCase       :       Any           = xa
    __lowerCamelCase       :       Tuple           = fxa

  return length


if __name__ == "__main__":

   def        a_     (							_lowerCAmelCase       )      ->      Dict:
     return math.sin(10 * x       )

   print('f(x) = sin(10 * x)')
   print('The length of the curve from x = -10 to x = 10 is:')
   _UpperCamelCase											=						10
   while i <= 100000:
      print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
      i *= 10






