




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  ):



 '''simple docstring'''


 def 							a__					(							self   :    Optional[int]    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]    )
       lowerCamelCase_							=       get_activation('gelu'    )
       self.assertTrue(torch.allclose(gelu_python(A_    )		,				torch_builtin(A_    )    )    )
       self.assertFalse(torch.allclose(gelu_python(A_    )		,				gelu_new(A_    )    )    )

 def 							a__					(							self   :    Union[str, Any]    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]    )
       lowerCamelCase_							=       get_activation('gelu'    )
       lowerCamelCase_							=       get_activation('gelu_10'    )

       lowerCamelCase_							=       torch_builtin(A_    )
       lowerCamelCase_							=       geluaa(A_    )

       lowerCamelCase_							=       torch.where(y_gelu_aa < 10.0		,				1		,				0    )

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

 def 							a__					(							self   :    Optional[Any]    )  ->      List[Any]:

       """simple docstring"""






       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(A_    ):
             get_activation('bogus'    )
       with self.assertRaises(A_    ):
             get_activation(A_    )





 def 							a__					(							self   :    Tuple    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       get_activation('gelu'    )
       lowerCamelCase_							=       1
       lowerCamelCase_							=       get_activation('gelu'    )
       self.assertEqual(acta.a		,				1    )
       with self.assertRaises(A_    ):
             lowerCamelCase_							=       acta.a










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



