

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


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[Any]														=					"""canine"""





    def __init__(				self						,       a=768						,       a=12						,       a=12						,       a=3072						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=1_6384						,       a=16						,       a=0.02						,       a=1e-12						,       a=0						,       a=0xE_000						,       a=0xE_001						,       a=4						,       a=4						,       a=8						,       a=1_6384						,       a=128						,       **a						,       ):
         super().__init__(pad_token_id=a						,       bos_token_id=a						,       eos_token_id=a						,       **a)

         lowercase__   :       int				=	max_position_embeddings
         lowercase__   :       List[str]				=	hidden_size
         lowercase__   :       int				=	num_hidden_layers
         lowercase__   :       str				=	num_attention_heads
         lowercase__   :       List[Any]				=	intermediate_size
         lowercase__   :       Optional[int]				=	hidden_act
         lowercase__   :       Optional[Any]				=	hidden_dropout_prob
         lowercase__   :       List[str]				=	attention_probs_dropout_prob
         lowercase__   :       Optional[int]				=	initializer_range
         lowercase__   :       Dict				=	type_vocab_size
         lowercase__   :       Any				=	layer_norm_eps

         # Character config:
         lowercase__   :       List[Any]				=	downsampling_rate
         lowercase__   :       Dict				=	upsampling_kernel_size
         lowercase__   :       Any				=	num_hash_functions
         lowercase__   :       Any				=	num_hash_buckets
         lowercase__   :       Optional[int]				=	local_transformer_stride




from __future__ import annotations

from collections.abc import Callable

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Callable[[int | float], int | float]     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int = 100     ,					):



     '''simple docstring'''





     lowercase__   :       Tuple				=	x_start
     lowercase__   :       Tuple				=	fnc(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	0.0
     for _ in range(SCREAMING_SNAKE_CASE_      ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          lowercase__   :       Any				=	(x_end - x_start) / steps + xa
          lowercase__   :       Optional[Any]				=	fnc(SCREAMING_SNAKE_CASE_      )
          area += abs(fxa + fxa      ) * (xa - xa) / 2
          # Increment step
          lowercase__   :       Any				=	xa
          lowercase__   :       str				=	fxa
     return area


if __name__ == "__main__":

       def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



            '''simple docstring'''





            return x**3 + x**2

       print('''f(x) = x^3 + x^2''')
       print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
       snake_case_        =			10
       while i <= 100_000:
              print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
              i *= 10

