
"""simple docstring"""





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				['image_processor', 'tokenizer']
		lowercase__      =				'AutoImageProcessor'
		lowercase__      =				'AutoTokenizer'



		def __init__( self    ,		__a=None    ,		__a=None    ,		**__a)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''    ,		__a    ,		)
												_UpperCamelCase										=  kwargs.pop('''feature_extractor''')

							_UpperCamelCase										=  image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.''')
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.''')

							super().__init__(__a    ,		__a)
							_UpperCamelCase										=  self.image_processor
							_UpperCamelCase										=  False



		def __call__( self    ,		*__a    ,		**__a)	->	Dict:






							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
												return self.current_processor(*__a    ,		**__a)

							_UpperCamelCase										=  kwargs.pop('''images'''    ,		__a)
							_UpperCamelCase										=  kwargs.pop('''text'''    ,		__a)
							if len(__a) > 0:
												_UpperCamelCase										=  args[0]
												_UpperCamelCase										=  args[1:]

							if images is None and text is None:
												raise ValueError('''You need to specify either an `images` or `text` input to process.''')

							if images is not None:
												_UpperCamelCase										=  self.image_processor(__a    ,		*__a    ,		**__a)
							if text is not None:
												_UpperCamelCase										=  self.tokenizer(__a    ,		**__a)

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												_UpperCamelCase										=  encodings['''input_ids''']
												return inputs



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Optional[int]:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Union[str, Any]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a    ,		**__a)



		@contextmanager
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							warnings.warn(
							    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
							    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
							    '''your images inputs, or in a separate call.''')
							_UpperCamelCase										=  True
							_UpperCamelCase										=  self.tokenizer
							yield
							_UpperCamelCase										=  self.image_processor
							_UpperCamelCase										=  False



		def 						UpperCAmelCase							( self    ,		__a    ,		__a=False    ,		__a=None)	->	Dict:






							'''simple docstring'''
							if added_vocab is None:
												_UpperCamelCase										=  self.tokenizer.get_added_vocab()

							_UpperCamelCase										=  {}

							while tokens:
												_UpperCamelCase										=  re.search(R'''<s_(.*?)>'''    ,		__a    ,		re.IGNORECASE)
												if start_token is None:
																	break
												_UpperCamelCase										=  start_token.group(1)
												_UpperCamelCase										=  re.search(RF'''</s_{key}>'''    ,		__a    ,		re.IGNORECASE)
												_UpperCamelCase										=  start_token.group()
												if end_token is None:
																	_UpperCamelCase										=  tokens.replace(__a    ,		'''''')
												else:
																	_UpperCamelCase										=  end_token.group()
																	_UpperCamelCase										=  re.escape(__a)
																	_UpperCamelCase										=  re.escape(__a)
																	_UpperCamelCase										=  re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''    ,		__a    ,		re.IGNORECASE)
																	if content is not None:
																						_UpperCamelCase										=  content.group(1).strip()
																						if r"<s_" in content and r"</s_" in content:  # non-leaf node
																											_UpperCamelCase										=  self.tokenajson(__a    ,		is_inner_value=__a    ,		added_vocab=__a)
																											if value:
																																if len(__a) == 1:
																																					_UpperCamelCase										=  value[0]
																																_UpperCamelCase										=  value
																						else:  # leaf nodes
																											_UpperCamelCase										=  []
																											for leaf in content.split(R'''<sep/>'''):
																																_UpperCamelCase										=  leaf.strip()
																																if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																					_UpperCamelCase										=  leaf[1:-2]  # for categorical special tokens
																																output[key].append(__a)
																											if len(output[key]) == 1:
																																_UpperCamelCase										=  output[key][0]

																	_UpperCamelCase										=  tokens[tokens.find(__a) + len(__a) :].strip()
																	if tokens[:6] == r"<sep/>":  # non-leaf nodes
																						return [output] + self.tokenajson(tokens[6:]    ,		is_inner_value=__a    ,		added_vocab=__a)

							if len(__a):
												return [output] if is_inner_value else output
							else:
												return [] if is_inner_value else {"text_sequence": tokens}



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






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,		__a    ,		)
							return self.image_processor_class




		@property
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,		__a    ,		)
							return self.image_processor








"""simple docstring"""





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


_a														=		logging.get_logger(__name__)

_a														=		{
    """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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'detr'
		lowercase__      =				['past_key_values']
		lowercase__      =				{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}



		def __init__( self    ,		__a=True    ,		__a=None    ,		__a=3    ,		__a=1_00    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=0.0    ,		__a=0.0    ,		__a=True    ,		__a="relu"    ,		__a=2_56    ,		__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=1    ,		__a=5    ,		__a=2    ,		__a=1    ,		__a=1    ,		__a=5    ,		__a=2    ,		__a=0.1    ,		**__a    ,		)	->	Tuple:






							'''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										=  CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
												elif isinstance(__a    ,		__a):
																	_UpperCamelCase										=  backbone_config.get('''model_type''')
																	_UpperCamelCase										=  CONFIG_MAPPING[backbone_model_type]
																	_UpperCamelCase										=  config_class.from_dict(__a)
												# set timm attributes to None
												_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  None, None, None

							_UpperCamelCase										=  use_timm_backbone
							_UpperCamelCase										=  backbone_config
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  num_queries
							_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										=  decoder_layerdrop
							_UpperCamelCase										=  encoder_layers
							_UpperCamelCase										=  auxiliary_loss
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  backbone
							_UpperCamelCase										=  use_pretrained_backbone
							_UpperCamelCase										=  dilation
							# 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
							super().__init__(is_encoder_decoder=__a    ,		**__a)



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.encoder_attention_heads



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.d_model



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	Any:






							'''simple docstring'''
							return cls(backbone_config=__a    ,		**__a)




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






							'''simple docstring'''
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							if output["backbone_config"] is not None:
												_UpperCamelCase										=  self.backbone_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



		@property
		def 						UpperCAmelCase							( self)	->	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)	->	float:






							'''simple docstring'''
							return 1e-5




		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return 12






