

'''simple docstring'''



from typing import List

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


if is_torch_available():
  import torch







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   ["image_processor", "tokenizer"]
 lowerCAmelCase__						      =   "OneFormerImageProcessor"
 lowerCAmelCase__						      =   ("CLIPTokenizer", "CLIPTokenizerFast")
 def __init__(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : Tuple=None      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=None      ,							__SCREAMING_SNAKE_CASE      : int = 77      ,							__SCREAMING_SNAKE_CASE      : int = 77      ,							**__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Dict:


      """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						     =   max_seq_length
      __SCREAMING_SNAKE_CASE						     =   task_seq_length

      super().__init__(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=None      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=77    )						->				Optional[Any]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE						     =   self.tokenizer(__SCREAMING_SNAKE_CASE      ,							padding="""max_length"""      ,							max_length=__SCREAMING_SNAKE_CASE      ,							truncation=__SCREAMING_SNAKE_CASE    )

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

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

      __SCREAMING_SNAKE_CASE						     =   torch.cat(__SCREAMING_SNAKE_CASE      ,							dim=0    )
      return token_inputs
 def __call__(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Tuple=None      ,							__SCREAMING_SNAKE_CASE      : Tuple=None      ,							__SCREAMING_SNAKE_CASE      : int=None      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				Tuple:


      """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						     =   self.image_processor(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

      if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ):
           __SCREAMING_SNAKE_CASE						     =   [task_inputs]

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

      if hasattr(__SCREAMING_SNAKE_CASE      ,							"""text_inputs"""    ):
           __SCREAMING_SNAKE_CASE						     =   encoded_inputs.text_inputs

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

           __SCREAMING_SNAKE_CASE						     =   torch.cat(__SCREAMING_SNAKE_CASE      ,							dim=0    )

      return encoded_inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=None      ,							__SCREAMING_SNAKE_CASE      : str=None      ,							__SCREAMING_SNAKE_CASE      : int=None      ,							**__SCREAMING_SNAKE_CASE      : List[Any]    )						->				Optional[int]:


      """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						     =   self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

      if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ):
           __SCREAMING_SNAKE_CASE						     =   [task_inputs]

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

      if hasattr(__SCREAMING_SNAKE_CASE      ,							"""text_inputs"""    ):
           __SCREAMING_SNAKE_CASE						     =   encoded_inputs.text_inputs

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

           __SCREAMING_SNAKE_CASE						     =   torch.cat(__SCREAMING_SNAKE_CASE      ,							dim=0    )

      return encoded_inputs
 def    UpperCAmelCase__	(	self      : Any      ,							*__SCREAMING_SNAKE_CASE      : Dict      ,							**__SCREAMING_SNAKE_CASE      : int    )						->				List[str]:


      """simple docstring"""




      return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							*__SCREAMING_SNAKE_CASE      : Any      ,							**__SCREAMING_SNAKE_CASE      : Any    )						->				Optional[Any]:


      """simple docstring"""




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


 def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							*__SCREAMING_SNAKE_CASE      : Dict      ,							**__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				Dict:


      """simple docstring"""




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









'''simple docstring'''



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


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

UpperCAmelCase				: Optional[int]         =  {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "qdqbert"
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=30_522      ,							__SCREAMING_SNAKE_CASE      : str=768      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=12      ,							__SCREAMING_SNAKE_CASE      : Any=12      ,							__SCREAMING_SNAKE_CASE      : Any=3_072      ,							__SCREAMING_SNAKE_CASE      : List[str]="gelu"      ,							__SCREAMING_SNAKE_CASE      : Any=0.1      ,							__SCREAMING_SNAKE_CASE      : Any=0.1      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=512      ,							__SCREAMING_SNAKE_CASE      : List[str]=2      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : List[str]=1E-12      ,							__SCREAMING_SNAKE_CASE      : str=True      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=0      ,							__SCREAMING_SNAKE_CASE      : Dict=2      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							)						->				str:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE      ,							bos_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   max_position_embeddings
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   num_hidden_layers
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads
      __SCREAMING_SNAKE_CASE						     =   intermediate_size
      __SCREAMING_SNAKE_CASE						     =   hidden_act
      __SCREAMING_SNAKE_CASE						     =   hidden_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   initializer_range
      __SCREAMING_SNAKE_CASE						     =   type_vocab_size
      __SCREAMING_SNAKE_CASE						     =   layer_norm_eps
      __SCREAMING_SNAKE_CASE						     =   use_cache






