
"""simple docstring"""





import warnings
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    _UpperCAmelCase(   lowerCamelCase     ):
				lowercase__      =				['image_processor', 'tokenizer']
				lowercase__      =				'LayoutLMv3ImageProcessor'
				lowercase__      =				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')



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






									'''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.'''    ,		__UpperCAmelCase    ,		)
														_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__(__UpperCAmelCase    ,		__UpperCAmelCase)



				def __call__( self    ,		__a    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = True    ,		__a = False    ,		__a = None    ,		__a = None    ,		__a = 0    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = False    ,		__a = False    ,		__a = False    ,		__a = False    ,		__a = True    ,		__a = None    ,		**__a    ,		)	->	BatchEncoding:






									'''simple docstring'''


									if self.image_processor.apply_ocr and (boxes is not None):
														raise ValueError(
														    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.''')

									if self.image_processor.apply_ocr and (word_labels is not None):
														raise ValueError(
														    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''')

									# first, apply the image processor
									_UpperCamelCase										=  self.image_processor(images=__UpperCAmelCase    ,		return_tensors=__UpperCAmelCase)

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
														if isinstance(__UpperCAmelCase    ,		__UpperCAmelCase):
																			_UpperCamelCase										=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
														_UpperCamelCase										=  features['''words''']

									_UpperCamelCase										=  self.tokenizer(
									    text=text if text is not None else features['''words''']    ,		text_pair=text_pair if text_pair is not None else None    ,		boxes=boxes if boxes is not None else features['''boxes''']    ,		word_labels=__UpperCAmelCase    ,		add_special_tokens=__UpperCAmelCase    ,		padding=__UpperCAmelCase    ,		truncation=__UpperCAmelCase    ,		max_length=__UpperCAmelCase    ,		stride=__UpperCAmelCase    ,		pad_to_multiple_of=__UpperCAmelCase    ,		return_token_type_ids=__UpperCAmelCase    ,		return_attention_mask=__UpperCAmelCase    ,		return_overflowing_tokens=__UpperCAmelCase    ,		return_special_tokens_mask=__UpperCAmelCase    ,		return_offsets_mapping=__UpperCAmelCase    ,		return_length=__UpperCAmelCase    ,		verbose=__UpperCAmelCase    ,		return_tensors=__UpperCAmelCase    ,		**__UpperCAmelCase    ,		)

									# add pixel values
									_UpperCamelCase										=  features.pop('''pixel_values''')
									if return_overflowing_tokens is True:
														_UpperCamelCase										=  self.get_overflowing_images(__UpperCAmelCase    ,		encoded_inputs['''overflow_to_sample_mapping'''])
									_UpperCamelCase										=  images

									return encoded_inputs



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






									'''simple docstring'''
									_UpperCamelCase										=  []
									for sample_idx in overflow_to_sample_mapping:
														images_with_overflow.append(images[sample_idx])

									if len(__UpperCAmelCase) != len(__UpperCAmelCase):
														raise ValueError(
														    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
														    F''' {len(__UpperCAmelCase)} and {len(__UpperCAmelCase)}''')

									return images_with_overflow



				def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Any:






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



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






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



				@property
				def 						UpperCAmelCase							( self)	->	Optional[int]:






									'''simple docstring'''
									return ["input_ids", "bbox", "attention_mask", "pixel_values"]



				@property
				def 						UpperCAmelCase							( self)	->	Dict:






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




				@property
				def 						UpperCAmelCase							( self)	->	Optional[int]:






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






