
"""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


A__ :       Union[str, Any]				= logging.get_logger(__name__)

A__ :       Dict				= {
    '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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''yolos'''
       def __init__(  self			,					A_=768			,					A_=12			,					A_=12			,					A_=3072			,					A_="gelu"			,					A_=0.0			,					A_=0.0			,					A_=0.02			,					A_=1E-12			,					A_=[512, 864]			,					A_=16			,					A_=3			,					A_=True			,					A_=100			,					A_=True			,					A_=False			,					A_=1			,					A_=5			,					A_=2			,					A_=5			,					A_=2			,					A_=0.1			,					**A_			,					)  ->						List[Any]:





         """simple docstring"""




         super().__init__(**A_							)

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


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		version.parse('''1.11'''			)
       @property
       def 				lowercase_				(  self							)  ->						Mapping[str, Mapping[int, str]]:





         """simple docstring"""




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





         """simple docstring"""




         return 1E-4



       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return 12


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
