

from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
  import torch







class 							a__							( _a							):







  """simple docstring"""




  UpperCAmelCase__   :			Tuple      							=["""image_processor""", """tokenizer"""]
  UpperCAmelCase__   :			str      							="""OneFormerImageProcessor"""
  UpperCAmelCase__   :			Any      							=("""CLIPTokenizer""", """CLIPTokenizerFast""")


  def __init__(      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Any=None					,					UpperCAmelCase__     :			Optional[int]=None					,					UpperCAmelCase__     :			Optional[int] = 7_7					,					UpperCAmelCase__     :			Any = 7_7					,					**UpperCAmelCase__     :			int		)       ->str:
        """simple docstring"""







        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`."""		)

        SCREAMING_SNAKE_CASE			:   Dict	  = max_seq_length
        SCREAMING_SNAKE_CASE			:   List[str]	  = task_seq_length

        super().__init__(_UpperCAmelCase					,					_UpperCAmelCase		)


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			Dict=None					,					UpperCAmelCase__     :			Union[str, Any]=7_7		)       ->str:
        """simple docstring"""







        if text_list is None:
              raise ValueError("""tokens cannot be None."""		)

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.tokenizer(_UpperCAmelCase					,					padding="""max_length"""					,					max_length=_UpperCAmelCase					,					truncation=_UpperCAmelCase		)

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   str	  = tokens["""attention_mask"""], tokens["""input_ids"""]

        SCREAMING_SNAKE_CASE			:   List[Any]	  = []
        for attn_mask, input_id in zip(_UpperCAmelCase					,					_UpperCAmelCase		):
              SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
              token_inputs.append(token.unsqueeze(0		)		)

        SCREAMING_SNAKE_CASE			:   int	  = torch.cat(_UpperCAmelCase					,					dim=0		)
        return token_inputs


  def __call__(      self     :			Dict					,					UpperCAmelCase__     :			Tuple=None					,					UpperCAmelCase__     :			str=None					,					UpperCAmelCase__     :			List[Any]=None					,					**UpperCAmelCase__     :			List[Any]		)       ->Union[str, Any]:
        """simple docstring"""







        if task_inputs is None:
              raise ValueError("""You have to specify the task_input. Found None."""		)
        elif images is None:
              raise ValueError("""You have to specify the image. Found None."""		)

        if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
              raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.image_processor(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					**_UpperCAmelCase		)

        if isinstance(_UpperCAmelCase					,					_UpperCAmelCase		):
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = [task_inputs]

        if isinstance(_UpperCAmelCase					,					_UpperCAmelCase		) and all(isinstance(_UpperCAmelCase					,					_UpperCAmelCase		) for task_input in task_inputs		):
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = []
              for task in task_inputs:
                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = f"the task is {task}"
                    task_token_inputs.append(_UpperCAmelCase		)
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self._preprocess_text(_UpperCAmelCase					,					max_length=self.task_seq_length		)
        else:
              raise TypeError("""Task Inputs should be a string or a list of strings."""		)

        if hasattr(_UpperCAmelCase					,					"""text_inputs"""		):
              SCREAMING_SNAKE_CASE			:   List[Any]	  = encoded_inputs.text_inputs

              SCREAMING_SNAKE_CASE			:   Dict	  = []
              for texts in texts_list:
                    SCREAMING_SNAKE_CASE			:   int	  = self._preprocess_text(_UpperCAmelCase					,					max_length=self.max_seq_length		)
                    text_inputs.append(text_input_list.unsqueeze(0		)		)

              SCREAMING_SNAKE_CASE			:   Dict	  = torch.cat(_UpperCAmelCase					,					dim=0		)

        return encoded_inputs


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			List[str]=None					,					UpperCAmelCase__     :			Union[str, Any]=None					,					UpperCAmelCase__     :			Tuple=None					,					**UpperCAmelCase__     :			int		)       ->List[Any]:
        """simple docstring"""







        if task_inputs is None:
              raise ValueError("""You have to specify the task_input. Found None."""		)
        elif images is None:
              raise ValueError("""You have to specify the image. Found None."""		)

        if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
              raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

        SCREAMING_SNAKE_CASE			:   int	  = self.image_processor.encode_inputs(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					**_UpperCAmelCase		)

        if isinstance(_UpperCAmelCase					,					_UpperCAmelCase		):
              SCREAMING_SNAKE_CASE			:   List[Any]	  = [task_inputs]

        if isinstance(_UpperCAmelCase					,					_UpperCAmelCase		) and all(isinstance(_UpperCAmelCase					,					_UpperCAmelCase		) for task_input in task_inputs		):
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = []
              for task in task_inputs:
                    SCREAMING_SNAKE_CASE			:   Any	  = f"the task is {task}"
                    task_token_inputs.append(_UpperCAmelCase		)
              SCREAMING_SNAKE_CASE			:   Tuple	  = self._preprocess_text(_UpperCAmelCase					,					max_length=self.task_seq_length		)
        else:
              raise TypeError("""Task Inputs should be a string or a list of strings."""		)

        if hasattr(_UpperCAmelCase					,					"""text_inputs"""		):
              SCREAMING_SNAKE_CASE			:   Tuple	  = encoded_inputs.text_inputs

              SCREAMING_SNAKE_CASE			:   Dict	  = []
              for texts in texts_list:
                    SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self._preprocess_text(_UpperCAmelCase					,					max_length=self.max_seq_length		)
                    text_inputs.append(text_input_list.unsqueeze(0		)		)

              SCREAMING_SNAKE_CASE			:   Dict	  = torch.cat(_UpperCAmelCase					,					dim=0		)

        return encoded_inputs


  def 			_lowercase   (      self     :			List[str]					,					*UpperCAmelCase__     :			Optional[int]					,					**UpperCAmelCase__     :			List[Any]		)       ->List[Any]:
        """simple docstring"""







        return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase					,					**_UpperCAmelCase		)


  def 			_lowercase   (      self     :			Dict					,					*UpperCAmelCase__     :			List[str]					,					**UpperCAmelCase__     :			Optional[int]		)       ->Optional[int]:
        """simple docstring"""







        return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase					,					**_UpperCAmelCase		)




  def 			_lowercase   (      self     :			int					,					*UpperCAmelCase__     :			Optional[Any]					,					**UpperCAmelCase__     :			str		)       ->Optional[int]:
        """simple docstring"""







        return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase					,					**_UpperCAmelCase		)





"""simple docstring"""


import copy
from typing import Dict

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


lowerCAmelCase_:       Union[str, Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Dict			     =       {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class  a__     ( _a						):
   snake_case_				=   "vit-hybrid"


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=224,					_UpperCAmelCase=1,					_UpperCAmelCase=3,					_UpperCAmelCase=[1, 1024, 24, 24],					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)

        if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
             lowercase__								=				{
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             if "model_type" in backbone_config:
                  lowercase__								=				CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  lowercase__								=				BitConfig
             lowercase__								=				backbone_config_class(**_UpperCAmelCase	)

        lowercase__								=				backbone_featmap_shape
        lowercase__								=				backbone_config
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				image_size
        lowercase__								=				patch_size
        lowercase__								=				num_channels
        lowercase__								=				qkv_bias


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.backbone_config.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




