






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=    {
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class 				A_     (    __lowercase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Optional[Any]			=       "lxmert"
 _SCREAMING_SNAKE_CASE				:      Dict			=       {}


 def __init__(						self				,	_A=30522				,	_A=768				,	_A=12				,	_A=9500				,	_A=1600				,	_A=400				,	_A=3072				,	_A="gelu"				,	_A=0.1				,	_A=0.1				,	_A=512				,	_A=2				,	_A=0.02				,	_A=1e-12				,	_A=9				,	_A=5				,	_A=5				,	_A=2048				,	_A=4				,	_A=6.67				,	_A=True				,	_A=True				,	_A=True				,	_A=True				,	_A=True				,	_A=True				,	_A=True				,	**_A				,	)			->		Dict:

     """simple docstring"""



     _UpperCAmelCase					:   str      	=					vocab_size
     _UpperCAmelCase					:   Union[str, Any]      	=					hidden_size
     _UpperCAmelCase					:   List[str]      	=					num_attention_heads
     _UpperCAmelCase					:   Tuple      	=					hidden_act
     _UpperCAmelCase					:   Union[str, Any]      	=					intermediate_size
     _UpperCAmelCase					:   Union[str, Any]      	=					hidden_dropout_prob
     _UpperCAmelCase					:   Dict      	=					attention_probs_dropout_prob
     _UpperCAmelCase					:   Optional[Any]      	=					max_position_embeddings
     _UpperCAmelCase					:   Optional[Any]      	=					type_vocab_size
     _UpperCAmelCase					:   str      	=					initializer_range
     _UpperCAmelCase					:   List[Any]      	=					layer_norm_eps
     _UpperCAmelCase					:   int      	=					num_qa_labels
     _UpperCAmelCase					:   int      	=					num_object_labels
     _UpperCAmelCase					:   List[str]      	=					num_attr_labels
     _UpperCAmelCase					:   List[str]      	=					l_layers
     _UpperCAmelCase					:   Optional[int]      	=					x_layers
     _UpperCAmelCase					:   int      	=					r_layers
     _UpperCAmelCase					:   Any      	=					visual_feat_dim
     _UpperCAmelCase					:   Any      	=					visual_pos_dim
     _UpperCAmelCase					:   List[Any]      	=					visual_loss_normalizer
     _UpperCAmelCase					:   Optional[int]      	=					task_matched
     _UpperCAmelCase					:   Optional[Any]      	=					task_mask_lm
     _UpperCAmelCase					:   str      	=					task_obj_predict
     _UpperCAmelCase					:   Dict      	=					task_qa
     _UpperCAmelCase					:   Dict      	=					visual_obj_loss
     _UpperCAmelCase					:   List[Any]      	=					visual_attr_loss
     _UpperCAmelCase					:   int      	=					visual_feat_loss
     _UpperCAmelCase					:   Tuple      	=					{'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
     super().__init__(**__A)






from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int | float]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int | float:
       if len(UpperCAmelCase_				) == 0:
              raise ValueError('''find_max() arg is an empty sequence'''				)
       if (
           left >= len(UpperCAmelCase_				)
           or left < -len(UpperCAmelCase_				)
           or right >= len(UpperCAmelCase_				)
           or right < -len(UpperCAmelCase_				)
       ):
              raise IndexError('''list index out of range'''				)
       if left == right:
              return nums[left]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=(left + right) >> 1  # the middle
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)  # find max in range[left, mid]
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							mid + 1			,							UpperCAmelCase_				)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
