code: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowercase:		Tuple							=     logging.get_logger(__name__)
  
  __lowercase:		Union[str, Any]							=     {
      "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
      "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
      # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
  }
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					List[str]    					=					'pegasus_x'
   _lowerCamelCase :					Dict    					=					['past_key_values']
   _lowerCamelCase :					List[str]    					=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
   def __init__( self :	Optional[int],					a_ :	Tuple=9_6103,					a_ :	Any=1_6384,					a_ :	Optional[int]=16,					a_ :	List[str]=4096,					a_ :	int=16,					a_ :	Tuple=16,					a_ :	Union[str, Any]=4096,					a_ :	Optional[Any]=16,					a_ :	int=0.0,					a_ :	Dict=0.0,					a_ :	Any=True,					a_ :	Optional[int]=True,					a_ :	Tuple="gelu",					a_ :	Union[str, Any]=1024,					a_ :	Optional[Any]=0.1,					a_ :	int=0.0,					a_ :	int=0.0,					a_ :	Optional[int]=0.02,					a_ :	Union[str, Any]=0,					a_ :	Optional[int]=True,					a_ :	str=0,					a_ :	Tuple=1,					a_ :	Optional[Any]=1,					a_ :	Tuple=32,					a_ :	Dict=512,					a_ :	Tuple=True,					**a_ :	Tuple,					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						vocab_size
      UpperCamelCase__ 						=						max_position_embeddings
      UpperCamelCase__ 						=						d_model
      UpperCamelCase__ 						=						encoder_ffn_dim
      UpperCamelCase__ 						=						encoder_layers
      UpperCamelCase__ 						=						encoder_attention_heads
      UpperCamelCase__ 						=						decoder_ffn_dim
      UpperCamelCase__ 						=						decoder_layers
      UpperCamelCase__ 						=						decoder_attention_heads
      UpperCamelCase__ 						=						dropout
      UpperCamelCase__ 						=						attention_dropout
      UpperCamelCase__ 						=						activation_dropout
      UpperCamelCase__ 						=						activation_function
      UpperCamelCase__ 						=						init_std
      UpperCamelCase__ 						=						encoder_layerdrop
      UpperCamelCase__ 						=						decoder_layerdrop
      UpperCamelCase__ 						=						use_cache
      UpperCamelCase__ 						=						encoder_layers
      UpperCamelCase__ 						=						scale_embedding  # scale factor will be sqrt(d_model) if True
  
      UpperCamelCase__ 						=						num_global_tokens
      UpperCamelCase__ 						=						block_size
      UpperCamelCase__ 						=						stagger_local_blocks
  
      super().__init__(
          pad_token_id=a_,					eos_token_id=a_,					is_encoder_decoder=a_,					decoder_start_token_id=a_,					forced_eos_token_id=a_,					**a_,					)
  
   @property
   def 		lowercase_						( self :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.encoder_attention_heads
  
   @property
   def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.d_model
style_context: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
   _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
   _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')
  
   def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):
  
  
  
  
  
      """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`."					)
  
      UpperCamelCase__ 						=						max_seq_length
      UpperCamelCase__ 						=						task_seq_length
  
      super().__init__(a_,					a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if text_list is None:
         raise ValueError("tokens cannot be None."					)
  
      UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]
  
      UpperCamelCase__ 						=						[]
      for attn_mask, input_id in zip(a_,					a_					):
         UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
         token_inputs.append(token.unsqueeze(0					)					)
  
      UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
      return token_inputs
  
   def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	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."					)
  
      UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	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."					)
  
      UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)