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


UpperCAmelCase_           =				logging.get_logger(__name__)

UpperCAmelCase_           =				{
    '''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__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = '''yolos'''



   def __init__(						self       :	str		,	_lowercase       :	Optional[Any]=768		,	_lowercase       :	Any=12		,	_lowercase       :	Any=12		,	_lowercase       :	Union[str, Any]=3_072		,	_lowercase       :	Any="gelu"		,	_lowercase       :	Any=0.0		,	_lowercase       :	Tuple=0.0		,	_lowercase       :	str=0.02		,	_lowercase       :	Any=1E-12		,	_lowercase       :	Optional[Any]=[512, 864]		,	_lowercase       :	Tuple=16		,	_lowercase       :	int=3		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Dict=100		,	_lowercase       :	List[str]=True		,	_lowercase       :	Optional[int]=False		,	_lowercase       :	Dict=1		,	_lowercase       :	List[str]=5		,	_lowercase       :	int=2		,	_lowercase       :	Optional[Any]=5		,	_lowercase       :	Tuple=2		,	_lowercase       :	List[Any]=0.1		,	**_lowercase       :	List[str]		,	):



    """simple docstring"""



    super().__init__(**_lowercase				)

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




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = version.parse('''1.11'''     )



   @property
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



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



   @property
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    return 1E-4



   @property
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    return 12
UpperCAmelCase_           =				'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_           =				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_           =				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}