






'''simple docstring'''





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


_UpperCAmelCase			:		int   				=    logging.get_logger(__name__)

_UpperCAmelCase			:		Optional[int]   				=    {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}
class  lowerCAmelCase_       (				__A					):
							UpperCamelCase_		:Dict		      =    'table-transformer'
							UpperCamelCase_		:str		      =    ['past_key_values']
							UpperCamelCase_		:List[str]		      =    {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}




							def __init__(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Dict=True ,							SCREAMING_SNAKE_CASE_     :   int=None ,							SCREAMING_SNAKE_CASE_     :   List[Any]=3 ,							SCREAMING_SNAKE_CASE_     :   Dict=100 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=6 ,							SCREAMING_SNAKE_CASE_     :   int=2_048 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=8 ,							SCREAMING_SNAKE_CASE_     :   Any=6 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=2_048 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=8 ,							SCREAMING_SNAKE_CASE_     :   Dict=0.0 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=0.0 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]="relu" ,							SCREAMING_SNAKE_CASE_     :   List[str]=256 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.1 ,							SCREAMING_SNAKE_CASE_     :   int=0.0 ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.0 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.02 ,							SCREAMING_SNAKE_CASE_     :   str=1.0 ,							SCREAMING_SNAKE_CASE_     :   int=False ,							SCREAMING_SNAKE_CASE_     :   List[str]="sine" ,							SCREAMING_SNAKE_CASE_     :   Tuple="resnet50" ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=False ,							SCREAMING_SNAKE_CASE_     :   Dict=1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=5 ,							SCREAMING_SNAKE_CASE_     :   Dict=2 ,							SCREAMING_SNAKE_CASE_     :   int=1 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=1 ,							SCREAMING_SNAKE_CASE_     :   int=5 ,							SCREAMING_SNAKE_CASE_     :   str=2 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=0.1 ,							**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__      			=						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']	)
															elif isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																			lowerCAmelCase__      			=						backbone_config.get('''model_type'''	)
																			lowerCAmelCase__      			=						CONFIG_MAPPING[backbone_model_type]
																			lowerCAmelCase__      			=						config_class.from_dict(SCREAMING_SNAKE_CASE_	)
															# set timm attributes to None
															lowerCAmelCase__      			=						None, None, None

											lowerCAmelCase__      			=						use_timm_backbone
											lowerCAmelCase__      			=						backbone_config
											lowerCAmelCase__      			=						num_channels
											lowerCAmelCase__      			=						num_queries
											lowerCAmelCase__      			=						d_model
											lowerCAmelCase__      			=						encoder_ffn_dim
											lowerCAmelCase__      			=						encoder_layers
											lowerCAmelCase__      			=						encoder_attention_heads
											lowerCAmelCase__      			=						decoder_ffn_dim
											lowerCAmelCase__      			=						decoder_layers
											lowerCAmelCase__      			=						decoder_attention_heads
											lowerCAmelCase__      			=						dropout
											lowerCAmelCase__      			=						attention_dropout
											lowerCAmelCase__      			=						activation_dropout
											lowerCAmelCase__      			=						activation_function
											lowerCAmelCase__      			=						init_std
											lowerCAmelCase__      			=						init_xavier_std
											lowerCAmelCase__      			=						encoder_layerdrop
											lowerCAmelCase__      			=						decoder_layerdrop
											lowerCAmelCase__      			=						encoder_layers
											lowerCAmelCase__      			=						auxiliary_loss
											lowerCAmelCase__      			=						position_embedding_type
											lowerCAmelCase__      			=						backbone
											lowerCAmelCase__      			=						use_pretrained_backbone
											lowerCAmelCase__      			=						dilation
											# Hungarian matcher
											lowerCAmelCase__      			=						class_cost
											lowerCAmelCase__      			=						bbox_cost
											lowerCAmelCase__      			=						giou_cost
											# Loss coefficients
											lowerCAmelCase__      			=						mask_loss_coefficient
											lowerCAmelCase__      			=						dice_loss_coefficient
											lowerCAmelCase__      			=						bbox_loss_coefficient
											lowerCAmelCase__      			=						giou_loss_coefficient
											lowerCAmelCase__      			=						eos_coefficient
											super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   Tuple	):
											return self.encoder_attention_heads




							@property
							def 						__snake_case					(   self     :   List[str]	):
											return self.d_model



class  lowerCAmelCase_       (				__A					):
							UpperCamelCase_		:Optional[Any]		      =    version.parse('1.11'					)




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




							@property
							def 						__snake_case					(   self     :   str	):
											return 1e-5




							@property
							def 						__snake_case					(   self     :   List[str]	):
											return 12













import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    ['image_processor', 'tokenizer']
							UpperCamelCase_		:Tuple		      =    'ViTImageProcessor'
							UpperCamelCase_		:Dict		      =    ('CLIPTokenizer', 'CLIPTokenizerFast')




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						kwargs.pop('''feature_extractor'''	)

											lowerCAmelCase__      			=						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__(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def __call__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											if text is None and visual_prompt is None and images is None:
															raise ValueError('''You have to specify either text, visual prompt or images.'''	)

											if text is not None and visual_prompt is not None:
															raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''	)

											if text is not None:
															lowerCAmelCase__      			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if images is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None and images is not None:
															lowerCAmelCase__      			=						{
															    '''pixel_values''': image_features.pixel_values,
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											elif text is not None and images is not None:
															lowerCAmelCase__      			=						image_features.pixel_values
															return encoding
											elif text is not None:
															return encoding
											elif visual_prompt is not None:
															lowerCAmelCase__      			=						{
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											else:
															return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_	) ,							tensor_type=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   str	):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   str	):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor_class




							@property
							def 						__snake_case					(   self     :   Tuple	):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor





