




def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Dict:





  """simple docstring"""





  _lowercase: Union[str, Any]						     =    len(_A							)
  # Create a stack and push index of fist element to it
  _lowercase: Tuple						     =    []
  st.append(0							)

  # Span value of first element is always 1
  _lowercase: Union[str, Any]						     =    1

  # Calculate span values for rest of the elements
  for i in range(1					,     _A							):
    # Pop elements from stack while stack is not
    # empty and top of stack is smaller than price[i]
    while len(_A							) > 0 and price[st[0]] <= price[i]:
      st.pop()

    # If stack becomes empty, then price[i] is greater
    # than all elements on left of it, i.e. price[0],
    # price[1], ..price[i-1]. Else the price[i]  is
    # greater than elements after top of stack
    _lowercase: Optional[int]						     =    i + 1 if len(_A							) <= 0 else (i - st[0])

    # Push this element to stack
    st.append(_A							)


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  for i in range(0					,     _A							):
    print(arr[i]					,     end=' '							)


# Driver program to test above function
UpperCAmelCase__						:       Any							=    [10, 4, 5, 90, 120, 80]
UpperCAmelCase__						:       List[Any]							=    [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

     if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'     )
       _lowercase: Dict						     =    {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage3'],
           'embedding_dynamic_padding': True,
       }

     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       if "model_type" in backbone_config:
         _lowercase: int						     =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
         logger.info(
             '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'     )
         _lowercase: Optional[int]						     =    BitConfig
       _lowercase: List[Any]						     =    backbone_config_class(**UpperCamelCase_     )

     _lowercase: List[Any]						     =    backbone_featmap_shape
     _lowercase: Dict						     =    backbone_config
     _lowercase: Optional[int]						     =    hidden_size
     _lowercase: List[Any]						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: str						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[str]						     =    attention_probs_dropout_prob
     _lowercase: Tuple						     =    initializer_range
     _lowercase: Any						     =    layer_norm_eps
     _lowercase: Any						     =    image_size
     _lowercase: List[Any]						     =    patch_size
     _lowercase: Dict						     =    num_channels
     _lowercase: Tuple						     =    qkv_bias






   def  _a						(	self   :       Optional[int]     )->			Dict[str, any]:
     _lowercase: List[str]						     =    copy.deepcopy(self.__dict__     )
     _lowercase: Tuple						     =    self.backbone_config.to_dict()
     _lowercase: int						     =    self.__class__.model_type
     return output
