"""simple docstring"""


from typing import List

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


if is_torch_available():
 import torch






class  a__     ( _a						):
   snake_case_				=   ["image_processor", "tokenizer"]
   snake_case_				=   "OneFormerImageProcessor"
   snake_case_				=   ("CLIPTokenizer", "CLIPTokenizerFast")


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					_UpperCAmelCase = 77,					_UpperCAmelCase = 77,					**_UpperCAmelCase	):
        '''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`."	)

        lowercase__								=				max_seq_length
        lowercase__								=				task_seq_length

        super().__init__(_UpperCAmelCase,					_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=77	):
        '''simple docstring'''



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

        lowercase__								=				self.tokenizer(_UpperCAmelCase,					padding="max_length",					max_length=_UpperCAmelCase,					truncation=_UpperCAmelCase	)

        lowercase__			,    lowercase__								=				tokens["attention_mask"], tokens["input_ids"]

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

        lowercase__								=				torch.cat(_UpperCAmelCase,					dim=0	)
        return token_inputs


   def __call__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					**_UpperCAmelCase	):
        '''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."	)

        lowercase__								=				self.image_processor(_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					**_UpperCAmelCase	)

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             lowercase__								=				[task_inputs]

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) and all(isinstance(_UpperCAmelCase,					_UpperCAmelCase	) for task_input in task_inputs	):
             lowercase__								=				[]
             for task in task_inputs:
                  lowercase__								=				F'''the task is {task}'''
                  task_token_inputs.append(_UpperCAmelCase	)
             lowercase__								=				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"	):
             lowercase__								=				encoded_inputs.text_inputs

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

             lowercase__								=				torch.cat(_UpperCAmelCase,					dim=0	)

        return encoded_inputs


   def 						snake_case__						(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					**_UpperCAmelCase	):
        '''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."	)

        lowercase__								=				self.image_processor.encode_inputs(_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					**_UpperCAmelCase	)

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             lowercase__								=				[task_inputs]

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) and all(isinstance(_UpperCAmelCase,					_UpperCAmelCase	) for task_input in task_inputs	):
             lowercase__								=				[]
             for task in task_inputs:
                  lowercase__								=				F'''the task is {task}'''
                  task_token_inputs.append(_UpperCAmelCase	)
             lowercase__								=				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"	):
             lowercase__								=				encoded_inputs.text_inputs

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

             lowercase__								=				torch.cat(_UpperCAmelCase,					dim=0	)

        return encoded_inputs


   def 						snake_case__						(  self,					*_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					*_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					*_UpperCAmelCase,					**_UpperCAmelCase	):
        '''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




