
"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {
    '''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_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Optional[Any]					=			'''roformer'''


						def __init__(       self   :					Tuple     ,A_   :					Optional[int]=5_0000     ,A_   :					Tuple=None     ,A_   :					Optional[Any]=768     ,A_   :					Dict=12     ,A_   :					Optional[int]=12     ,A_   :					Union[str, Any]=3072     ,A_   :					Dict="gelu"     ,A_   :					Dict=0.1     ,A_   :					List[Any]=0.1     ,A_   :					List[Any]=1536     ,A_   :					List[str]=2     ,A_   :					Any=0.02     ,A_   :					str=1e-12     ,A_   :					Optional[int]=0     ,A_   :					List[str]=False     ,A_   :					Tuple=True     ,**A_   :					List[str]     ,)   ->      Dict:
							super().__init__(pad_token_id=A_     ,**A_					)

							A        =      vocab_size
							A        =      hidden_size if embedding_size is None else embedding_size
							A        =      hidden_size
							A        =      num_hidden_layers
							A        =      num_attention_heads
							A        =      hidden_act
							A        =      intermediate_size
							A        =      hidden_dropout_prob
							A        =      attention_probs_dropout_prob
							A        =      max_position_embeddings
							A        =      type_vocab_size
							A        =      initializer_range
							A        =      layer_norm_eps
							A        =      rotary_value
							A        =      use_cache







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						@property
						def   _SCREAMING_SNAKE_CASE					(       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'}
							A        =      {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       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

