



from collections import OrderedDict
from typing import Mapping

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


A__     				=	logging.get_logger(__name__)

A__     				=	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}





class  __lowerCAmelCase			(					lowerCamelCase__	):
    __lowerCamelCase			=    '''mega'''




    def __init__(							self						,    _snake_case=30522						,    _snake_case=128						,    _snake_case=4						,    _snake_case=256						,    _snake_case=16						,    _snake_case=True						,    _snake_case=64						,    _snake_case=False						,    _snake_case=-1						,    _snake_case=None						,    _snake_case=True						,    _snake_case="scalenorm"						,    _snake_case=True						,    _snake_case="silu"						,    _snake_case="softmax"						,    _snake_case=0.1						,    _snake_case=0.1						,    _snake_case=0.1						,    _snake_case=False						,    _snake_case=True						,    _snake_case=256						,    _snake_case=True						,    _snake_case=0.1						,    _snake_case=2048						,    _snake_case=False						,    _snake_case=2						,    _snake_case=0.02						,    _snake_case=0.2						,    _snake_case=0.02						,    _snake_case=1.0						,    _snake_case=1						,    _snake_case=0						,    _snake_case=2						,    _snake_case="rotary"						,    _snake_case=None						,    _snake_case=True						,    _snake_case=True						,    **_snake_case						,    ):



         """simple docstring"""





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

         _lowerCAmelCase      =   vocab_size
         _lowerCAmelCase      =   hidden_size
         _lowerCAmelCase      =   num_hidden_layers
         _lowerCAmelCase      =   activation
         _lowerCAmelCase      =   attention_activation
         _lowerCAmelCase      =   intermediate_size
         _lowerCAmelCase      =   ema_projection_size
         _lowerCAmelCase      =   bidirectional
         _lowerCAmelCase      =   shared_representation_size
         _lowerCAmelCase      =   use_chunking
         _lowerCAmelCase      =   chunk_size
         _lowerCAmelCase      =   truncation
         _lowerCAmelCase      =   normalize_before_mega
         _lowerCAmelCase      =   normalization_type
         _lowerCAmelCase      =   norm_affine
         _lowerCAmelCase      =   dropout_prob
         _lowerCAmelCase      =   hidden_dropout_prob
         _lowerCAmelCase      =   attention_probs_dropout_prob
         _lowerCAmelCase      =   use_feature_dropout
         _lowerCAmelCase      =   use_normalized_ffn
         _lowerCAmelCase      =   nffn_hidden_size
         _lowerCAmelCase      =   normalize_before_ffn
         _lowerCAmelCase      =   nffn_activation_dropout_prob
         _lowerCAmelCase      =   max_positions
         _lowerCAmelCase      =   add_token_type_embeddings
         _lowerCAmelCase      =   type_vocab_size
         _lowerCAmelCase      =   initializer_range
         _lowerCAmelCase      =   ema_delta_alpha_range
         _lowerCAmelCase      =   ema_beta_range
         _lowerCAmelCase      =   ema_gamma_omega_range
         _lowerCAmelCase      =   relative_positional_bias
         _lowerCAmelCase      =   use_cache
         _lowerCAmelCase      =   classifier_dropout
         _lowerCAmelCase      =   add_lm_hidden_dense_layer
         _lowerCAmelCase      =   1  # not used but required by Hugging Face





class  __lowerCAmelCase			(					lowerCamelCase__	):




    @property
    def 							snake_case       (							self		):



         """simple docstring"""





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








from math import exp


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					return 8 * x - 2 * exp(-x      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   lower_bound
					_lowerCAmelCase      =   upper_bound
					for _ in range(0     ,						snake_case      ):
										_lowerCAmelCase     ,     _lowerCAmelCase      =   xa, xa - (f(snake_case      ) * (xa - xa)) / (f(snake_case      ) - f(snake_case      ))
					return xa


if __name__ == "__main__":
	print(f"Example: {secant_method(1, 3, 2)}")



