

'''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


lowerCamelCase			:				Optional[Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				List[Any] 			=					{
    "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 							A__   (	A__  ):
  A__					=    'yolos'





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





       '''simple docstring'''






       super().__init__(**_a			)

       _SCREAMING_SNAKE_CASE							   =hidden_size
       _SCREAMING_SNAKE_CASE							   =num_hidden_layers
       _SCREAMING_SNAKE_CASE							   =num_attention_heads
       _SCREAMING_SNAKE_CASE							   =intermediate_size
       _SCREAMING_SNAKE_CASE							   =hidden_act
       _SCREAMING_SNAKE_CASE							   =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							   =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							   =initializer_range
       _SCREAMING_SNAKE_CASE							   =layer_norm_eps
       _SCREAMING_SNAKE_CASE							   =image_size
       _SCREAMING_SNAKE_CASE							   =patch_size
       _SCREAMING_SNAKE_CASE							   =num_channels
       _SCREAMING_SNAKE_CASE							   =qkv_bias
       _SCREAMING_SNAKE_CASE							   =num_detection_tokens
       _SCREAMING_SNAKE_CASE							   =use_mid_position_embeddings
       _SCREAMING_SNAKE_CASE							   =auxiliary_loss
       # Hungarian matcher
       _SCREAMING_SNAKE_CASE							   =class_cost
       _SCREAMING_SNAKE_CASE							   =bbox_cost
       _SCREAMING_SNAKE_CASE							   =giou_cost
       # Loss coefficients
       _SCREAMING_SNAKE_CASE							   =bbox_loss_coefficient
       _SCREAMING_SNAKE_CASE							   =giou_loss_coefficient
       _SCREAMING_SNAKE_CASE							   =eos_coefficient







class 							A__   (	A__  ):
  A__					=    version.parse('1.11'  )





  @property
  def 						A  (		self				:     Union[str, Any]			)     ->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]			)





  @property
  def 						A  (		self				:     int			)     ->   float:





       '''simple docstring'''






       return 1e-4





  @property
  def 						A  (		self				:     Tuple			)     ->   int:





       '''simple docstring'''






       return 12



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase			:				List[str] 			=					{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Union[str, Any] 			=					[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				str 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
