

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    '''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 				a  (	SCREAMING_SNAKE_CASE__				):
						_lowercase    						=			"roformer"


						def __init__(       self						,					A_=50000						,					A_=None						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=1536						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_=False						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=_lowercase						,					**_lowercase	)

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







class 				a  (	SCREAMING_SNAKE_CASE__				):


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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







from __future__ import annotations

import numpy as np

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[float] )   ->       Dict:
				return np.maximum(0  ,      lowerCAmelCase )


if __name__ == "__main__":
					print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


