code: 
  
  
  
  
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
   import torch
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Dict     =['image_processor', 'tokenizer']
        UpperCamelCase_       :      Any     ='OneFormerImageProcessor'
        UpperCamelCase_       :      Any     =('CLIPTokenizer', 'CLIPTokenizerFast')
        def __init__(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_ = 77					,       SCREAMING_SNAKE_CASE_ = 77					,       **SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            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 :Union[str, Any]										=     max_seq_length
            UpperCamelCase :Dict										=     task_seq_length
  
            super().__init__(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=77				)       ->   Union[str, Any]:
            if text_list is None:
                raise ValueError('''tokens cannot be None.'''				)
  
            UpperCamelCase :List[Any]										=     self.tokenizer(SCREAMING_SNAKE_CASE_					,       padding='''max_length'''					,       max_length=SCREAMING_SNAKE_CASE_					,       truncation=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase							,     UpperCamelCase :List[Any]										=     tokens['''attention_mask'''], tokens['''input_ids''']
  
            UpperCamelCase :Union[str, Any]										=     []
            for attn_mask, input_id in zip(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :str										=     torch.tensor(SCREAMING_SNAKE_CASE_				) * torch.tensor(SCREAMING_SNAKE_CASE_				)
                token_inputs.append(token.unsqueeze(0				)				)
  
            UpperCamelCase :int										=     torch.cat(SCREAMING_SNAKE_CASE_					,       dim=0				)
            return token_inputs
        def __call__(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   Tuple:
  
            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 :int										=     self.image_processor(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Union[str, Any]										=     [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				):
                UpperCamelCase :Optional[Any]										=     []
                for task in task_inputs:
                    UpperCamelCase :Dict										=     F'''the task is {task}'''
                    task_token_inputs.append(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Optional[int]										=     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'''				):
                UpperCamelCase :str										=     encoded_inputs.text_inputs
  
                UpperCamelCase :Union[str, Any]										=     []
                for texts in texts_list:
                    UpperCamelCase :Dict										=     self._preprocess_text(SCREAMING_SNAKE_CASE_					,       max_length=self.max_seq_length				)
                    text_inputs.append(text_input_list.unsqueeze(0				)				)
  
                UpperCamelCase :Optional[int]										=     torch.cat(SCREAMING_SNAKE_CASE_					,       dim=0				)
  
            return encoded_inputs
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   Tuple:
  
            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 :int										=     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_				):
                UpperCamelCase :Tuple										=     [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				):
                UpperCamelCase :str										=     []
                for task in task_inputs:
                    UpperCamelCase :Optional[int]										=     F'''the task is {task}'''
                    task_token_inputs.append(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :List[str]										=     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'''				):
                UpperCamelCase :str										=     encoded_inputs.text_inputs
  
                UpperCamelCase :Any										=     []
                for texts in texts_list:
                    UpperCamelCase :Any										=     self._preprocess_text(SCREAMING_SNAKE_CASE_					,       max_length=self.max_seq_length				)
                    text_inputs.append(text_input_list.unsqueeze(0				)				)
  
                UpperCamelCase :Dict										=     torch.cat(SCREAMING_SNAKE_CASE_					,       dim=0				)
  
            return encoded_inputs
        def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   int:
            return self.image_processor.post_process_semantic_segmentation(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Any:
            return self.image_processor.post_process_instance_segmentation(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   List[str]:
            return self.image_processor.post_process_panoptic_segmentation(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  from typing import Generic, TypeVar
  
  __snake_case							      =						TypeVar("""T""")
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        pass
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        pass
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      Generic[T]				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_ = 10				)       ->   List[str]:
            UpperCamelCase :list[T]										=     []
            UpperCamelCase :Any										=     limit
        def __bool__(       self				)       ->   bool:
            return bool(self.stack				)
        def __str__(       self				)       ->   str:
            return str(self.stack				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if len(self.stack				) >= self.limit:
                raise StackOverflowError
            self.stack.append(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   T:
            if not self.stack:
                raise StackUnderflowError
            return self.stack.pop()
        def 					UpperCAmelCase		(       self				)       ->   T:
            if not self.stack:
                raise StackUnderflowError
            return self.stack[-1]
        def 					UpperCAmelCase		(       self				)       ->   bool:
            return not bool(self.stack				)
        def 					UpperCAmelCase		(       self				)       ->   bool:
            return self.size() == self.limit
        def 					UpperCAmelCase		(       self				)       ->   int:
            return len(self.stack				)
  
  
  
  
        def __contains__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   bool:
            return item in self.stack
  
  
  
  
  
  
  
  def 						_A     (       ):
      UpperCamelCase :Stack[int]										=     Stack(10 )
      assert bool(SCREAMING_SNAKE_CASE__ ) is False
      assert stack.is_empty() is True
      assert stack.is_full() is False
      assert str(SCREAMING_SNAKE_CASE__ ) == "[]"
  
      try:
          UpperCamelCase :Optional[int]										=     stack.pop()
          raise AssertionError  # This should not happen
      except StackUnderflowError:
          assert True  # This should happen
  
      try:
          UpperCamelCase :Union[str, Any]										=     stack.peek()
          raise AssertionError  # This should not happen
      except StackUnderflowError:
          assert True  # This should happen
  
      for i in range(10 ):
          assert stack.size() == i
          stack.push(SCREAMING_SNAKE_CASE__ )
  
      assert bool(SCREAMING_SNAKE_CASE__ )
      assert not stack.is_empty()
      assert stack.is_full()
      assert str(SCREAMING_SNAKE_CASE__ ) == str(list(range(10 ) ) )
      assert stack.pop() == 9
      assert stack.peek() == 8
  
      stack.push(100 )
      assert str(SCREAMING_SNAKE_CASE__ ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )
  
      try:
          stack.push(200 )
          raise AssertionError  # This should not happen
      except StackOverflowError:
          assert True  # This should happen
  
      assert not stack.is_empty()
      assert stack.size() == 10
  
      assert 5 in stack
      assert 55 not in stack
  
  
  if __name__ == "__main__":
   test_stack()
  
  
  
  
  
  