



"""simple docstring"""







import copy
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
from ..auto import CONFIG_MAPPING


lowercase__		:				Optional[int]          =	logging.get_logger(__name__)

lowercase__		:				Optional[int]          =	{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}







class       UpperCamelCase__   (	_UpperCAmelCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """conditional_detr"""
      _SCREAMING_SNAKE_CASE  				=     ["""past_key_values"""]
      _SCREAMING_SNAKE_CASE  				=     {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }



      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      str=3    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=3_0_0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=6    ,			SCREAMING_SNAKE_CASE_				:      Any=2_0_4_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=8    ,			SCREAMING_SNAKE_CASE_				:      Any=6    ,			SCREAMING_SNAKE_CASE_				:      str=2_0_4_8    ,			SCREAMING_SNAKE_CASE_				:      List[str]=8    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=0.0    ,			SCREAMING_SNAKE_CASE_				:      Any=0.0    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]="relu"    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=2_5_6    ,			SCREAMING_SNAKE_CASE_				:      str=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=0.0    ,			SCREAMING_SNAKE_CASE_				:      int=0.0    ,			SCREAMING_SNAKE_CASE_				:      List[str]=0.02    ,			SCREAMING_SNAKE_CASE_				:      Tuple=1.0    ,			SCREAMING_SNAKE_CASE_				:      str=False    ,			SCREAMING_SNAKE_CASE_				:      int="sine"    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="resnet50"    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      int=False    ,			SCREAMING_SNAKE_CASE_				:      int=2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=1    ,			SCREAMING_SNAKE_CASE_				:      Tuple=1    ,			SCREAMING_SNAKE_CASE_				:      Tuple=2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5    ,			SCREAMING_SNAKE_CASE_				:      Any=2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=0.25    ,			**SCREAMING_SNAKE_CASE_				:      List[str]    ,			):
        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.'       )
            lowerCAmelCase_   :				List[str]								= CONFIG_MAPPING['resnet'](out_features=['stage4']       )
          elif isinstance(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
            lowerCAmelCase_   :				Tuple								= backbone_config.get('model_type'       )
            lowerCAmelCase_   :				str								= CONFIG_MAPPING[backbone_model_type]
            lowerCAmelCase_   :				str								= config_class.from_dict(SCREAMING_SNAKE_CASE_       )

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



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return self.encoder_attention_heads



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return self.d_model





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[int]								= copy.deepcopy(self.__dict__       )
        if self.backbone_config is not None:
          lowerCAmelCase_   :				Tuple								= self.backbone_config.to_dict()
        lowerCAmelCase_   :				Optional[Any]								= self.__class__.model_type
        return output







class       UpperCamelCase__   (	_UpperCAmelCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     version.parse("""1.11"""  )



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



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        return 1E-5





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        return 1_2












"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'imagegpt'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(       self      ,						lowercase=512 + 1      ,						lowercase=32 * 32      ,						lowercase=512      ,						lowercase=24      ,						lowercase=8      ,						lowercase=None      ,						lowercase="quick_gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=1e-5      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						**lowercase      ,						)		->     Any:
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       n_positions
            lowerCAmelCase  						=       n_embd
            lowerCAmelCase  						=       n_layer
            lowerCAmelCase  						=       n_head
            lowerCAmelCase  						=       n_inner
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       resid_pdrop
            lowerCAmelCase  						=       embd_pdrop
            lowerCAmelCase  						=       attn_pdrop
            lowerCAmelCase  						=       layer_norm_epsilon
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       scale_attn_weights
            lowerCAmelCase  						=       use_cache
            lowerCAmelCase  						=       scale_attn_by_inverse_layer_idx
            lowerCAmelCase  						=       reorder_and_upcast_attn
            lowerCAmelCase  						=       tie_word_embeddings

            super().__init__(tie_word_embeddings=lowercase      ,						**lowercase )







class 		lowercase					(					_UpperCAmelCase					):





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ] )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = 1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 32      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       self._generate_dummy_images(lowercase      ,						lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       dict(preprocessor(images=lowercase      ,						return_tensors=lowercase ) )

            return inputs






