

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


class    a__  (							__lowerCamelCase  ):
   _SCREAMING_SNAKE_CASE    :     Union[str, Any]	       =							"""roberta-prelayernorm"""


   def __init__( self			,				_UpperCamelCase=50265			,				_UpperCamelCase=768			,				_UpperCamelCase=12			,				_UpperCamelCase=12			,				_UpperCamelCase=3072			,				_UpperCamelCase="gelu"			,				_UpperCamelCase=0.1			,				_UpperCamelCase=0.1			,				_UpperCamelCase=512			,				_UpperCamelCase=2			,				_UpperCamelCase=0.0_2			,				_UpperCamelCase=1E-1_2			,				_UpperCamelCase=1			,				_UpperCamelCase=0			,				_UpperCamelCase=2			,				_UpperCamelCase="absolute"			,				_UpperCamelCase=True			,				_UpperCamelCase=None			,				**_UpperCamelCase			,				):





      """simple docstring"""





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

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




class    a__  (							__lowerCamelCase  ):


   @property
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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











import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class       a							(			unittest.TestCase				):



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      Union[str, Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      int     		=	get_activation('''gelu'''  )
						self.assertTrue(torch.allclose(gelu_python(__lowercase  )   ,torch_builtin(__lowercase  )  )  )
						self.assertFalse(torch.allclose(gelu_python(__lowercase  )   ,gelu_new(__lowercase  )  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      List[Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      Union[str, Any]     		=	get_activation('''gelu'''  )
						snake_case__	:      int     		=	get_activation('''gelu_10'''  )

						snake_case__	:      Optional[int]     		=	torch_builtin(__lowercase  )
						snake_case__	:      str     		=	geluaa(__lowercase  )

						snake_case__	:      Tuple     		=	torch.where(y_gelu_aa < 10.0   ,1   ,0  )

						self.assertTrue(torch.max(__lowercase  ).item() == 10.0  )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,y_gelu_aa * clipped_mask  )  )



		def 	__lowerCamelCase			(     self     :Any  ):
						get_activation('''gelu'''  )
						get_activation('''gelu_10'''  )
						get_activation('''gelu_fast'''  )
						get_activation('''gelu_new'''  )
						get_activation('''gelu_python'''  )
						get_activation('''gelu_pytorch_tanh'''  )
						get_activation('''linear'''  )
						get_activation('''mish'''  )
						get_activation('''quick_gelu'''  )
						get_activation('''relu'''  )
						get_activation('''sigmoid'''  )
						get_activation('''silu'''  )
						get_activation('''swish'''  )
						get_activation('''tanh'''  )
						with self.assertRaises(__lowercase  ):
										get_activation('''bogus'''  )
						with self.assertRaises(__lowercase  ):
										get_activation(__lowercase  )






		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      str     		=	get_activation('''gelu'''  )
						snake_case__	:      List[Any]     		=	1
						snake_case__	:      Optional[Any]     		=	get_activation('''gelu'''  )
						self.assertEqual(acta.a   ,1  )
						with self.assertRaises(__lowercase  ):
										snake_case__	:      str     		=	acta.a

