





from collections import OrderedDict
from typing import Mapping

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


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

_UpperCAmelCase       : Any			     =       {
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class      lowerCAmelCase							(      __a    ):
 UpperCAmelCase__									=	"""roformer"""
 def __init__(   self						:   List[str]    ,  UpperCAmelCase						:   Tuple=50000    ,  UpperCAmelCase						:   Optional[Any]=None    ,  UpperCAmelCase						:   List[str]=768    ,  UpperCAmelCase						:   Union[str, Any]=12    ,  UpperCAmelCase						:   Optional[int]=12    ,  UpperCAmelCase						:   Optional[Any]=3072    ,  UpperCAmelCase						:   List[str]="gelu"    ,  UpperCAmelCase						:   List[str]=0.1    ,  UpperCAmelCase						:   Tuple=0.1    ,  UpperCAmelCase						:   Optional[int]=1536    ,  UpperCAmelCase						:   Any=2    ,  UpperCAmelCase						:   Optional[int]=0.0_2    ,  UpperCAmelCase						:   Tuple=1e-12    ,  UpperCAmelCase						:   Dict=0    ,  UpperCAmelCase						:   str=False    ,  UpperCAmelCase						:   Dict=True    ,  **UpperCAmelCase						:   Dict    ,  )    ->			List[str]:
     super().__init__(pad_token_id=a_    ,  **a_			)

     lowerCamelCase__					:   int      				=	vocab_size
     lowerCamelCase__					:   int      				=	hidden_size if embedding_size is None else embedding_size
     lowerCamelCase__					:   Dict      				=	hidden_size
     lowerCamelCase__					:   Optional[int]      				=	num_hidden_layers
     lowerCamelCase__					:   Any      				=	num_attention_heads
     lowerCamelCase__					:   Dict      				=	hidden_act
     lowerCamelCase__					:   Optional[int]      				=	intermediate_size
     lowerCamelCase__					:   List[Any]      				=	hidden_dropout_prob
     lowerCamelCase__					:   Union[str, Any]      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   Any      				=	max_position_embeddings
     lowerCamelCase__					:   Tuple      				=	type_vocab_size
     lowerCamelCase__					:   List[Any]      				=	initializer_range
     lowerCamelCase__					:   List[Any]      				=	layer_norm_eps
     lowerCamelCase__					:   Optional[Any]      				=	rotary_value
     lowerCamelCase__					:   List[str]      				=	use_cache


class      lowerCAmelCase							(      __a    ):
 @property
 def   A_			(   self						:   Dict			)    ->			str:



     if self.task == "multiple-choice":
         lowerCamelCase__					:   str      				=	{0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
         lowerCamelCase__					:   List[str]      				=	{0: """batch""", 1: """sequence"""}
     lowerCamelCase__					:   List[Any]      				=	{0: """batch""", 1: """sequence"""}
     return OrderedDict(
         [
             ('input_ids', dynamic_axis),
             ('attention_mask', dynamic_axis),
             ('token_type_ids', 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      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Tuple			)    ->			int:
     lowerCamelCase__					:   Optional[int]      				=	torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]			)
     lowerCamelCase__					:   Dict      				=	get_activation('gelu'			)
     self.assertTrue(torch.allclose(gelu_python(UpperCAmelCase			)    ,  torch_builtin(UpperCAmelCase			)			)			)
     self.assertFalse(torch.allclose(gelu_python(UpperCAmelCase			)    ,  gelu_new(UpperCAmelCase			)			)			)
 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]			)
     lowerCamelCase__					:   str      				=	get_activation('gelu'			)
     lowerCamelCase__					:   Tuple      				=	get_activation('gelu_10'			)

     lowerCamelCase__					:   Tuple      				=	torch_builtin(UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	geluaa(UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	torch.where(y_gelu_aa < 1_0.0    ,  1    ,  0			)

     self.assertTrue(torch.max(UpperCAmelCase			).item() == 1_0.0			)
     self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,  y_gelu_aa * clipped_mask			)			)
 def   A_			(   self						:   str			)    ->			List[str]:
     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(UpperCAmelCase			):
         get_activation('bogus'			)
     with self.assertRaises(UpperCAmelCase			):
         get_activation(UpperCAmelCase			)







 def   A_			(   self						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	get_activation('gelu'			)
     lowerCamelCase__					:   Union[str, Any]      				=	1
     lowerCamelCase__					:   Optional[Any]      				=	get_activation('gelu'			)
     self.assertEqual(acta.a    ,  1			)
     with self.assertRaises(UpperCAmelCase			):
         lowerCamelCase__					:   str      				=	acta.a





