

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


a__				=		logging.get_logger(__name__)

a__				=		{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 				UpperCAmelCase_		(			__a   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    '''conditional_detr'''
							UpperCAmelCase__       :						str	    =    ['''past_key_values''']
							UpperCAmelCase__       :						Union[str, Any]	    =    {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}


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

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


							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												return self.encoder_attention_heads


							@property
							def 				__lowercase				(			self       )    ->    List[str]:
												return self.d_model




							def 				__lowercase				(			self       )    ->    int:
												_a	:    Tuple         =		copy.deepcopy(self.__dict__       )
												if self.backbone_config is not None:
																	_a	:    Dict         =		self.backbone_config.to_dict()
												_a	:    Optional[int]         =		self.__class__.model_type
												return output



class 				UpperCAmelCase_		(			__a   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[Any]	    =    version.parse("1.11"   )


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


							@property
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												return 1e-5




							@property
							def 				__lowercase				(			self       )    ->    Tuple:
												return 1_2





"""simple docstring"""





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  List[Any]						   =		'''BridgeTowerImageProcessor'''
		__lowercase							:  Tuple						   =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				super().__init__(lowerCAmelCase__							,  lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = self.tokenizer(
				    text=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  max_length=lowerCAmelCase__							,  stride=lowerCAmelCase__							,  pad_to_multiple_of=lowerCAmelCase__							,  return_token_type_ids=lowerCAmelCase__							,  return_attention_mask=lowerCAmelCase__							,  return_overflowing_tokens=lowerCAmelCase__							,  return_special_tokens_mask=lowerCAmelCase__							,  return_offsets_mapping=lowerCAmelCase__							,  return_length=lowerCAmelCase__							,  verbose=lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__							,  )
				# add pixel_values + pixel_mask
				__SCREAMING_SNAKE_CASE            = self.image_processor(
				    lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  do_normalize=lowerCAmelCase__							,  do_center_crop=lowerCAmelCase__							,  **lowerCAmelCase__)
				encoding.update(lowerCAmelCase__)

				return encoding




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.tokenizer.model_input_names
				__SCREAMING_SNAKE_CASE            = self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




