



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
       __lowerCamelCase		:		Tuple      =['image_processor', 'tokenizer']
       __lowerCamelCase		:		int      ='AutoImageProcessor'
       __lowerCamelCase		:		List[str]      ='AutoTokenizer'





       def __init__(				self						:						Tuple ,			__lowercase						:						Optional[int]=None ,			__lowercase						:						int=None ,			**__lowercase						:						List[Any]		):

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

           __a					       =						image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError("""You have to specify an image_processor."""		)
           if tokenizer is None:
               raise ValueError("""You have to specify a tokenizer."""		)

           super().__init__(__lowercase ,			__lowercase		)
           __a					       =						self.image_processor





       def __call__(				self						:						List[Any] ,			__lowercase						:						str=None ,			__lowercase						:						Union[str, Any]=None ,			__lowercase						:						List[Any]=None ,			**__lowercase						:						str		):

           '''simple docstring'''

           if text is None and images is None:
               raise ValueError("""You have to specify either text or images. Both cannot be none."""		)

           if text is not None:
               __a					       =						self.tokenizer(__lowercase ,			return_tensors=__lowercase ,			**__lowercase		)

           if images is not None:
               __a					       =						self.image_processor(__lowercase ,			return_tensors=__lowercase ,			**__lowercase		)

           if text is not None and images is not None:
               __a					       =						image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**__lowercase		) ,			tensor_type=__lowercase		)





       def   UpperCamelCase_   (				self						:						Union[str, Any] ,			*__lowercase						:						List[str] ,			**__lowercase						:						str		):

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





       def   UpperCamelCase_   (				self						:						int ,			*__lowercase						:						int ,			**__lowercase						:						Optional[int]		):

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





       @property
       def   UpperCamelCase_   (				self						:						Any		):

           '''simple docstring'''
           __a					       =						self.tokenizer.model_input_names
           __a					       =						self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





       @property
       def   UpperCamelCase_   (				self						:						str		):

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







       @property
       def   UpperCamelCase_   (				self						:						Any		):

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





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__     =	logging.get_logger(__name__)

lowerCamelCase__     =	{
    """facebook/sam-vit-huge""": """https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json""",
    """facebook/sam-vit-large""": """https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json""",
    """facebook/sam-vit-base""": """https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json""",
}






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						List[Any] ,			__lowercase						:						Optional[Any]=256 ,			__lowercase						:						Optional[int]=1024 ,			__lowercase						:						Any=16 ,			__lowercase						:						Optional[Any]=16 ,			__lowercase						:						Optional[Any]=4 ,			__lowercase						:						Optional[int]="gelu" ,			__lowercase						:						List[Any]=1E-6 ,			**__lowercase						:						str ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						hidden_size
											__a					       =						image_size
											__a					       =						patch_size
											__a					       =						image_size // patch_size
											__a					       =						mask_input_channels
											__a					       =						num_point_embeddings
											__a					       =						hidden_act
											__a					       =						layer_norm_eps






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						int ,			__lowercase						:						Tuple=256 ,			__lowercase						:						List[Any]="relu" ,			__lowercase						:						Any=2048 ,			__lowercase						:						List[Any]=2 ,			__lowercase						:						List[str]=8 ,			__lowercase						:						Any=2 ,			__lowercase						:						List[Any]=3 ,			__lowercase						:						str=3 ,			__lowercase						:						str=256 ,			__lowercase						:						Optional[Any]=1E-6 ,			**__lowercase						:						Tuple ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						hidden_size
											__a					       =						hidden_act
											__a					       =						mlp_dim
											__a					       =						num_hidden_layers
											__a					       =						num_attention_heads
											__a					       =						attention_downsample_rate
											__a					       =						num_multimask_outputs
											__a					       =						iou_head_depth
											__a					       =						iou_head_hidden_dim
											__a					       =						layer_norm_eps






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						List[Any] ,			__lowercase						:						List[Any]=768 ,			__lowercase						:						List[Any]=256 ,			__lowercase						:						Any=12 ,			__lowercase						:						Tuple=12 ,			__lowercase						:						Union[str, Any]=3 ,			__lowercase						:						Tuple=1024 ,			__lowercase						:						Any=16 ,			__lowercase						:						Dict="gelu" ,			__lowercase						:						List[Any]=1E-06 ,			__lowercase						:						List[str]=0.0 ,			__lowercase						:						Optional[Any]=1E-10 ,			__lowercase						:						Optional[Any]=True ,			__lowercase						:						str=4.0 ,			__lowercase						:						List[Any]=True ,			__lowercase						:						Dict=True ,			__lowercase						:						Optional[Any]=14 ,			__lowercase						:						Optional[int]=[2, 5, 8, 11] ,			__lowercase						:						List[str]=128 ,			__lowercase						:						Optional[int]=None ,			**__lowercase						:						List[Any] ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)

											__a					       =						hidden_size
											__a					       =						output_channels
											__a					       =						num_hidden_layers
											__a					       =						num_attention_heads
											__a					       =						num_channels
											__a					       =						image_size
											__a					       =						patch_size
											__a					       =						hidden_act
											__a					       =						layer_norm_eps
											__a					       =						attention_dropout
											__a					       =						initializer_range
											__a					       =						qkv_bias
											__a					       =						mlp_ratio
											__a					       =						use_abs_pos
											__a					       =						use_rel_pos
											__a					       =						window_size
											__a					       =						global_attn_indexes
											__a					       =						num_pos_feats
											__a					       =						int(hidden_size * mlp_ratio		) if mlp_dim is None else mlp_dim






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
							__lowerCamelCase		:		Dict      ='sam'
							__lowerCamelCase		:		Any      =True





							def __init__(				self						:						List[str] ,			__lowercase						:						List[str]=None ,			__lowercase						:						Optional[int]=None ,			__lowercase						:						Dict=None ,			__lowercase						:						Optional[int]=0.02 ,			**__lowercase						:						List[str] ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						vision_config if vision_config is not None else {}
											__a					       =						prompt_encoder_config if prompt_encoder_config is not None else {}
											__a					       =						mask_decoder_config if mask_decoder_config is not None else {}

											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						vision_config.to_dict()
											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						prompt_encoder_config.to_dict()
											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						mask_decoder_config.to_dict()

											__a					       =						SamVisionConfig(**__lowercase		)
											__a					       =						SamPromptEncoderConfig(**__lowercase		)
											__a					       =						SamMaskDecoderConfig(**__lowercase		)
											__a					       =						initializer_range







							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											__a					       =						copy.deepcopy(self.__dict__		)
											__a					       =						self.vision_config.to_dict()
											__a					       =						self.prompt_encoder_config.to_dict()
											__a					       =						self.mask_decoder_config.to_dict()
											__a					       =						self.__class__.model_type
											return output
