



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_					( a):
							lowerCamelCase__         =	['image_processor', 'tokenizer']
							lowerCamelCase__         =	'BridgeTowerImageProcessor'
							lowerCamelCase__         =	('RobertaTokenizer', 'RobertaTokenizerFast')







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														super().__init__(__a,    __a)







							def __call__(     self,    __a,    __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,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.tokenizer(
														    text=__a,    add_special_tokens=__a,    padding=__a,    truncation=__a,    max_length=__a,    stride=__a,    pad_to_multiple_of=__a,    return_token_type_ids=__a,    return_attention_mask=__a,    return_overflowing_tokens=__a,    return_special_tokens_mask=__a,    return_offsets_mapping=__a,    return_length=__a,    verbose=__a,    return_tensors=__a,    **__a,    )
														# add pixel_values + pixel_mask
														_lowerCAmelCase  :							Optional[Any]				     =					self.image_processor(
														    __a,    return_tensors=__a,    do_normalize=__a,    do_center_crop=__a,    **__a)
														encoding.update(__a)

														return encoding







							def      snake_case__   (     self,    *__a,    **__a):

														'''simple docstring'''

														return self.tokenizer.batch_decode(*__a,    **__a)







							def      snake_case__   (     self,    *__a,    **__a):

														'''simple docstring'''

														return self.tokenizer.decode(*__a,    **__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					self.tokenizer.model_input_names
														_lowerCAmelCase  :							Dict				     =					self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





import copy

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


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'upernet'







							def __init__(     self,    __a=None,    __a=512,    __a=0.02,    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=384,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														if backbone_config is None:
																					logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
																					_lowerCAmelCase  :							List[str]				     =					CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
														elif isinstance(__a,    __a):
																					_lowerCAmelCase  :							List[Any]				     =					backbone_config.get("model_type")
																					_lowerCAmelCase  :							Dict				     =					CONFIG_MAPPING[backbone_model_type]
																					_lowerCAmelCase  :							Optional[Any]				     =					config_class.from_dict(__a)

														_lowerCAmelCase  :							Tuple				     =					backbone_config
														_lowerCAmelCase  :							List[Any]				     =					hidden_size
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							str				     =					pool_scales
														_lowerCAmelCase  :							List[str]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Dict				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							Tuple				     =					auxiliary_in_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_channels
														_lowerCAmelCase  :							str				     =					auxiliary_num_convs
														_lowerCAmelCase  :							Union[str, Any]				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					loss_ignore_index







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							List[Any]				     =					self.backbone_config.to_dict()
														_lowerCAmelCase  :							Optional[Any]				     =					self.__class__.model_type
														return output
