

'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase     :			str    =logging.get_logger(__name__)

_lowerCAmelCase     :			Optional[int]    ={
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}

class __UpperCamelCase    (							A__		):






					'''simple docstring'''



					__magic_name__     			=      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
					__magic_name__     			=      "squeezebert"





					def __init__(							self							,			lowerCamelCase__=3_0_5_2_2							,			lowerCamelCase__=7_6_8							,			lowerCamelCase__=1_2							,			lowerCamelCase__=1_2							,			lowerCamelCase__=3_0_7_2							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.1							,			lowerCamelCase__=5_1_2							,			lowerCamelCase__=2							,			lowerCamelCase__=0.02							,			lowerCamelCase__=1e-12							,			lowerCamelCase__=0							,			lowerCamelCase__=7_6_8							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			lowerCamelCase__=1							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			**lowerCamelCase__							,			):
						super().__init__(pad_token_id=_lowerCAmelCase							,			**_lowerCAmelCase	)

						UpperCAmelCase__:  List[str]		       =   vocab_size
						UpperCAmelCase__:  str		       =   hidden_size
						UpperCAmelCase__:  List[str]		       =   num_hidden_layers
						UpperCAmelCase__:  str		       =   num_attention_heads
						UpperCAmelCase__:  Optional[Any]		       =   hidden_act
						UpperCAmelCase__:  Dict		       =   intermediate_size
						UpperCAmelCase__:  Dict		       =   hidden_dropout_prob
						UpperCAmelCase__:  int		       =   attention_probs_dropout_prob
						UpperCAmelCase__:  Dict		       =   max_position_embeddings
						UpperCAmelCase__:  List[str]		       =   type_vocab_size
						UpperCAmelCase__:  List[Any]		       =   initializer_range
						UpperCAmelCase__:  Union[str, Any]		       =   layer_norm_eps
						UpperCAmelCase__:  str		       =   embedding_size
						UpperCAmelCase__:  List[Any]		       =   q_groups
						UpperCAmelCase__:  Union[str, Any]		       =   k_groups
						UpperCAmelCase__:  str		       =   v_groups
						UpperCAmelCase__:  List[Any]		       =   post_attention_groups
						UpperCAmelCase__:  str		       =   intermediate_groups
						UpperCAmelCase__:  Any		       =   output_groups

class __UpperCamelCase    (							A__		):






					'''simple docstring'''



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


import numpy as np



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)

	# Add up the all the exponentials
	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)

	# Divide every exponent by the sum of all exponents
	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents

	return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))