






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_				)













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()





