


import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


__UpperCamelCase				:				List[Any]  				= logging.get_logger(__name__)

__UpperCamelCase				:				Tuple  				= {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class 					__magic_name__		(   UpperCAmelCase__):
     A:						Union[str, Any]								=					"detr"
     A:						str								=					["past_key_values"]
     A:						str								=					{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }


     def __init__(					self							:   Dict							,		lowerCamelCase__							:   str=True							,		lowerCamelCase__							:   int=None							,		lowerCamelCase__							:   List[Any]=3							,		lowerCamelCase__							:   List[str]=100							,		lowerCamelCase__							:   Dict=6							,		lowerCamelCase__							:   Any=2048							,		lowerCamelCase__							:   str=8							,		lowerCamelCase__							:   Any=6							,		lowerCamelCase__							:   Any=2048							,		lowerCamelCase__							:   Any=8							,		lowerCamelCase__							:   Union[str, Any]=0.0							,		lowerCamelCase__							:   List[str]=0.0							,		lowerCamelCase__							:   int=True							,		lowerCamelCase__							:   Optional[Any]="relu"							,		lowerCamelCase__							:   int=256							,		lowerCamelCase__							:   Dict=0.1							,		lowerCamelCase__							:   Dict=0.0							,		lowerCamelCase__							:   Optional[int]=0.0							,		lowerCamelCase__							:   Optional[Any]=0.02							,		lowerCamelCase__							:   Optional[int]=1.0							,		lowerCamelCase__							:   Any=False							,		lowerCamelCase__							:   Optional[int]="sine"							,		lowerCamelCase__							:   Union[str, Any]="resnet50"							,		lowerCamelCase__							:   str=True							,		lowerCamelCase__							:   Union[str, Any]=False							,		lowerCamelCase__							:   Optional[Any]=1							,		lowerCamelCase__							:   Optional[Any]=5							,		lowerCamelCase__							:   Dict=2							,		lowerCamelCase__							:   Optional[Any]=1							,		lowerCamelCase__							:   Dict=1							,		lowerCamelCase__							:   Tuple=5							,		lowerCamelCase__							:   int=2							,		lowerCamelCase__							:   str=0.1							,		**lowerCamelCase__							:   str							,		)     ->					List[Any]:





      '''simple docstring'''






      if backbone_config is not None and use_timm_backbone:
       raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''	)

      if not use_timm_backbone:
       if backbone_config is None:
        logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''	)
        UpperCamelCase__    :			List[Any]      				=      CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']	)
       elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__	):
        UpperCamelCase__    :			Tuple      				=      backbone_config.get('''model_type'''	)
        UpperCamelCase__    :			List[str]      				=      CONFIG_MAPPING[backbone_model_type]
        UpperCamelCase__    :			List[str]      				=      config_class.from_dict(UpperCAmelCase__	)
       # set timm attributes to None
       UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__    :			int      				=      None, None, None

      UpperCamelCase__    :			Optional[int]      				=      use_timm_backbone
      UpperCamelCase__    :			Optional[Any]      				=      backbone_config
      UpperCamelCase__    :			Optional[Any]      				=      num_channels
      UpperCamelCase__    :			Dict      				=      num_queries
      UpperCamelCase__    :			Tuple      				=      d_model
      UpperCamelCase__    :			List[str]      				=      encoder_ffn_dim
      UpperCamelCase__    :			Any      				=      encoder_layers
      UpperCamelCase__    :			List[Any]      				=      encoder_attention_heads
      UpperCamelCase__    :			List[str]      				=      decoder_ffn_dim
      UpperCamelCase__    :			Union[str, Any]      				=      decoder_layers
      UpperCamelCase__    :			Dict      				=      decoder_attention_heads
      UpperCamelCase__    :			Optional[int]      				=      dropout
      UpperCamelCase__    :			Union[str, Any]      				=      attention_dropout
      UpperCamelCase__    :			int      				=      activation_dropout
      UpperCamelCase__    :			Any      				=      activation_function
      UpperCamelCase__    :			List[str]      				=      init_std
      UpperCamelCase__    :			int      				=      init_xavier_std
      UpperCamelCase__    :			Optional[int]      				=      encoder_layerdrop
      UpperCamelCase__    :			str      				=      decoder_layerdrop
      UpperCamelCase__    :			Dict      				=      encoder_layers
      UpperCamelCase__    :			Optional[int]      				=      auxiliary_loss
      UpperCamelCase__    :			Tuple      				=      position_embedding_type
      UpperCamelCase__    :			Tuple      				=      backbone
      UpperCamelCase__    :			Union[str, Any]      				=      use_pretrained_backbone
      UpperCamelCase__    :			int      				=      dilation
      # Hungarian matcher
      UpperCamelCase__    :			Any      				=      class_cost
      UpperCamelCase__    :			int      				=      bbox_cost
      UpperCamelCase__    :			int      				=      giou_cost
      # Loss coefficients
      UpperCamelCase__    :			Optional[int]      				=      mask_loss_coefficient
      UpperCamelCase__    :			Tuple      				=      dice_loss_coefficient
      UpperCamelCase__    :			List[Any]      				=      bbox_loss_coefficient
      UpperCamelCase__    :			Optional[int]      				=      giou_loss_coefficient
      UpperCamelCase__    :			Union[str, Any]      				=      eos_coefficient
      super().__init__(is_encoder_decoder=UpperCAmelCase__							,		**UpperCAmelCase__	)


     @property
     def    UpperCAmelCase__		(					self							:   Any	)     ->					int:





      '''simple docstring'''






      return self.encoder_attention_heads


     @property
     def    UpperCAmelCase__		(					self							:   Any	)     ->					int:





      '''simple docstring'''






      return self.d_model


     @classmethod
     def    UpperCAmelCase__		(					cls							:   int							,		lowerCamelCase__							:   PretrainedConfig							,		**lowerCamelCase__							:   Optional[Any]	)     ->					str:





      '''simple docstring'''






      return cls(backbone_config=UpperCAmelCase__							,		**UpperCAmelCase__	)


     def    UpperCAmelCase__		(					self							:   str	)     ->					Dict[str, any]:





      '''simple docstring'''






      UpperCamelCase__    :			List[Any]      				=      copy.deepcopy(self.__dict__	)
      if output["backbone_config"] is not None:
       UpperCamelCase__    :			Tuple      				=      self.backbone_config.to_dict()
      UpperCamelCase__    :			Optional[int]      				=      self.__class__.model_type
      return output



class 					__magic_name__		(   UpperCAmelCase__):
     A:						Union[str, Any]								=					version.parse("1.11")


     @property
     def    UpperCAmelCase__		(					self							:   str	)     ->					Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






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


     @property
     def    UpperCAmelCase__		(					self							:   Union[str, Any]	)     ->					float:





      '''simple docstring'''






      return 1E-5


     @property
     def    UpperCAmelCase__		(					self							:   str	)     ->					int:





      '''simple docstring'''






      return 12








import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:		Tuple							       =   logging.get_logger(__name__)

_lowerCamelCase	:		str							       =   {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git_vision_model'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Any=768					, UpperCAmelCase__      :	int=3_072					, UpperCAmelCase__      :	List[str]=12					, UpperCAmelCase__      :	Dict=12					, UpperCAmelCase__      :	Optional[int]=3					, UpperCAmelCase__      :	List[Any]=224					, UpperCAmelCase__      :	Union[str, Any]=16					, UpperCAmelCase__      :	Union[str, Any]="quick_gelu"					, UpperCAmelCase__      :	Dict=1e-5					, UpperCAmelCase__      :	Union[str, Any]=0.0					, UpperCAmelCase__      :	Any=0.02					, **UpperCAmelCase__      :	Any					, )						->Optional[int]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__       =    hidden_size
           A__       =    intermediate_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    num_channels
           A__       =    patch_size
           A__       =    image_size
           A__       =    initializer_range
           A__       =    attention_dropout
           A__       =    layer_norm_eps
           A__       =    hidden_act





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Any					, UpperCAmelCase__      :	Union[str, os.PathLike]					, **UpperCAmelCase__      :	int)						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__ ,							A__       =    cls.get_config_dict(UpperCAmelCase__					, **UpperCAmelCase__)

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get('''model_type''') == "git":
                  A__       =    config_dict['''vision_config''']

           if "model_type" in config_dict and hasattr(cls					, '''model_type''') and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                      f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

           return cls.from_dict(UpperCAmelCase__					, **UpperCAmelCase__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git'''


    def __init__(			self      :	Dict					, UpperCAmelCase__      :	Dict=None					, UpperCAmelCase__      :	int=30_522					, UpperCAmelCase__      :	Optional[int]=768					, UpperCAmelCase__      :	Dict=6					, UpperCAmelCase__      :	int=12					, UpperCAmelCase__      :	List[str]=3_072					, UpperCAmelCase__      :	str="gelu"					, UpperCAmelCase__      :	int=0.1					, UpperCAmelCase__      :	Union[str, Any]=0.1					, UpperCAmelCase__      :	List[Any]=1_024					, UpperCAmelCase__      :	List[str]=0.02					, UpperCAmelCase__      :	Any=1e-12					, UpperCAmelCase__      :	Union[str, Any]=0					, UpperCAmelCase__      :	List[Any]="absolute"					, UpperCAmelCase__      :	int=True					, UpperCAmelCase__      :	Any=False					, UpperCAmelCase__      :	int=101					, UpperCAmelCase__      :	Tuple=102					, UpperCAmelCase__      :	Dict=None					, **UpperCAmelCase__      :	List[str]					, )						->Any:





           '''simple docstring'''

           super().__init__(bos_token_id=UpperCAmelCase__					, eos_token_id=UpperCAmelCase__					, pad_token_id=UpperCAmelCase__					, **UpperCAmelCase__)

           if vision_config is None:
                  A__       =    {}
                  logger.info('''vision_config is None. initializing the GitVisionConfig with default values.''')

           A__       =    GitVisionConfig(**UpperCAmelCase__)
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    hidden_act
           A__       =    intermediate_size
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    initializer_range
           A__       =    layer_norm_eps
           A__       =    position_embedding_type
           A__       =    use_cache
           A__       =    tie_word_embeddings
           A__       =    num_image_with_embedding

           A__       =    bos_token_id
           A__       =    eos_token_id





    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.vision_config.to_dict()
           A__       =    self.__class__.model_type
           return output


