




"""simple docstring"""







import copy

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


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''deformable_detr'''
   A_				      =       {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(		self       ,					lowerCamelCase_=True       ,					lowerCamelCase_=None       ,					lowerCamelCase_=3       ,					lowerCamelCase_=3_0_0       ,					lowerCamelCase_=1_0_2_4       ,					lowerCamelCase_=6       ,					lowerCamelCase_=1_0_2_4       ,					lowerCamelCase_=8       ,					lowerCamelCase_=6       ,					lowerCamelCase_=1_0_2_4       ,					lowerCamelCase_=8       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=True       ,					lowerCamelCase_="relu"       ,					lowerCamelCase_=2_5_6       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1.0       ,					lowerCamelCase_=True       ,					lowerCamelCase_=False       ,					lowerCamelCase_="sine"       ,					lowerCamelCase_="resnet50"       ,					lowerCamelCase_=True       ,					lowerCamelCase_=False       ,					lowerCamelCase_=4       ,					lowerCamelCase_=4       ,					lowerCamelCase_=4       ,					lowerCamelCase_=False       ,					lowerCamelCase_=3_0_0       ,					lowerCamelCase_=False       ,					lowerCamelCase_=1       ,					lowerCamelCase_=5       ,					lowerCamelCase_=2       ,					lowerCamelCase_=1       ,					lowerCamelCase_=1       ,					lowerCamelCase_=5       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.25       ,					lowerCamelCase_=False       ,					**lowerCamelCase_       ,					)				->   int:
       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              =	CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
           elif isinstance(lowerCamelCase_       ,					lowerCamelCase_):
               UpperCamelCase              =	backbone_config.get('''model_type''')
               UpperCamelCase              =	CONFIG_MAPPING[backbone_model_type]
               UpperCamelCase              =	config_class.from_dict(lowerCamelCase_)
       UpperCamelCase              =	use_timm_backbone
       UpperCamelCase              =	backbone_config
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	num_queries
       UpperCamelCase              =	max_position_embeddings
       UpperCamelCase              =	d_model
       UpperCamelCase              =	encoder_ffn_dim
       UpperCamelCase              =	encoder_layers
       UpperCamelCase              =	encoder_attention_heads
       UpperCamelCase              =	decoder_ffn_dim
       UpperCamelCase              =	decoder_layers
       UpperCamelCase              =	decoder_attention_heads
       UpperCamelCase              =	dropout
       UpperCamelCase              =	attention_dropout
       UpperCamelCase              =	activation_dropout
       UpperCamelCase              =	activation_function
       UpperCamelCase              =	init_std
       UpperCamelCase              =	init_xavier_std
       UpperCamelCase              =	encoder_layerdrop
       UpperCamelCase              =	auxiliary_loss
       UpperCamelCase              =	position_embedding_type
       UpperCamelCase              =	backbone
       UpperCamelCase              =	use_pretrained_backbone
       UpperCamelCase              =	dilation
       # deformable attributes
       UpperCamelCase              =	num_feature_levels
       UpperCamelCase              =	encoder_n_points
       UpperCamelCase              =	decoder_n_points
       UpperCamelCase              =	two_stage
       UpperCamelCase              =	two_stage_num_proposals
       UpperCamelCase              =	with_box_refine
       if two_stage is True and with_box_refine is False:
           raise ValueError('''If two_stage is True, with_box_refine must be True.''')
       # Hungarian matcher
       UpperCamelCase              =	class_cost
       UpperCamelCase              =	bbox_cost
       UpperCamelCase              =	giou_cost
       # Loss coefficients
       UpperCamelCase              =	mask_loss_coefficient
       UpperCamelCase              =	dice_loss_coefficient
       UpperCamelCase              =	bbox_loss_coefficient
       UpperCamelCase              =	giou_loss_coefficient
       UpperCamelCase              =	eos_coefficient
       UpperCamelCase              =	focal_alpha
       UpperCamelCase              =	disable_custom_kernels
       super().__init__(is_encoder_decoder=lowerCamelCase_       ,					**lowerCamelCase_)







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.encoder_attention_heads







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.d_model







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	copy.deepcopy(self.__dict__)
       if self.backbone_config is not None:
           UpperCamelCase              =	self.backbone_config.to_dict()
       UpperCamelCase              =	self.__class__.model_type
       return output



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__  			=				{
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TrOCRForCausalLM''',
					    '''TrOCRPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



