



def  lowerCamelCase_	(      lowerCAmelCase__	:	list[int]				)       ->							int:

							'''simple docstring'''






							if not numbers:
														return 0

							if not isinstance(lowerCAmelCase__			,	(list, tuple)				) or not all(
							    isinstance(lowerCAmelCase__			,	lowerCAmelCase__				) for number in numbers				):
														raise ValueError('numbers must be an iterable of integers'				)

							A      						=    A      						=    A      						=    numbers[0]

							for i in range(1			,	len(lowerCAmelCase__				)				):
														# update the maximum and minimum subarray products
														A      						=    numbers[i]
														if number < 0:
																					A						,   A      						=    min_till_now, max_till_now
														A      						=    max(lowerCAmelCase__			,	max_till_now * number				)
														A      						=    min(lowerCAmelCase__			,	min_till_now * number				)

														# update the maximum product found till now
														A      						=    max(lowerCAmelCase__			,	lowerCAmelCase__				)

							return max_prod



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





