



"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__					:						Dict												=			logging.get_logger(__name__)

lowercase__					:						int												=			{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}




class  _UpperCAmelCase (    lowerCAmelCase__):
       _lowerCAmelCase     :    int	      =	"""yolos"""

       def __init__(  self	: str						,  lowercase_	: Optional[Any]=768						,  lowercase_	: int=12						,  lowercase_	: Any=12						,  lowercase_	: Union[str, Any]=3072						,  lowercase_	: List[Any]="gelu"						,  lowercase_	: Dict=0.0						,  lowercase_	: str=0.0						,  lowercase_	: str=0.02						,  lowercase_	: int=1E-12						,  lowercase_	: Union[str, Any]=[512, 864]						,  lowercase_	: Any=16						,  lowercase_	: str=3						,  lowercase_	: Optional[int]=True						,  lowercase_	: str=100						,  lowercase_	: Tuple=True						,  lowercase_	: Optional[int]=False						,  lowercase_	: str=1						,  lowercase_	: Union[str, Any]=5						,  lowercase_	: str=2						,  lowercase_	: Dict=5						,  lowercase_	: Optional[Any]=2						,  lowercase_	: List[Any]=0.1						,  **lowercase_	: Tuple						,  ):
           super().__init__(**__A       )

           snake_case_				:  List[Any]       =			hidden_size
           snake_case_				:  List[str]       =			num_hidden_layers
           snake_case_				:  Any       =			num_attention_heads
           snake_case_				:  str       =			intermediate_size
           snake_case_				:  Optional[int]       =			hidden_act
           snake_case_				:  Union[str, Any]       =			hidden_dropout_prob
           snake_case_				:  Tuple       =			attention_probs_dropout_prob
           snake_case_				:  Any       =			initializer_range
           snake_case_				:  Optional[Any]       =			layer_norm_eps
           snake_case_				:  Dict       =			image_size
           snake_case_				:  Dict       =			patch_size
           snake_case_				:  Dict       =			num_channels
           snake_case_				:  List[str]       =			qkv_bias
           snake_case_				:  Optional[int]       =			num_detection_tokens
           snake_case_				:  Tuple       =			use_mid_position_embeddings
           snake_case_				:  List[str]       =			auxiliary_loss
           # Hungarian matcher
           snake_case_				:  List[Any]       =			class_cost
           snake_case_				:  List[str]       =			bbox_cost
           snake_case_				:  str       =			giou_cost
           # Loss coefficients
           snake_case_				:  Optional[int]       =			bbox_loss_coefficient
           snake_case_				:  List[str]       =			giou_loss_coefficient
           snake_case_				:  Tuple       =			eos_coefficient





class  _UpperCAmelCase (    lowerCAmelCase__):
       _lowerCAmelCase     :    List[Any]	      =	version.parse("""1.11""")

       @property
       def 		_snake_case      (  self	: str       ):
           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ]       )

       @property
       def 		_snake_case      (  self	: str       ):
           return 1E-4






       @property
       def 		_snake_case      (  self	: Dict       ):
           return 12










"""simple docstring"""

from statistics import mean, stdev




def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Optional[int]       =			min(_a					)
    snake_case_				:  str       =			max(_a					)
    # normalize data
    return [round((x - x_min) / (x_max - x_min)	,    _a					) for x in data]






def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Any       =			mean(_a					)
    snake_case_				:  str       =			stdev(_a					)
    # standardize data
    return [round((x - mu) / (sigma)	,    _a					) for x in data]





