



"""simple docstring"""





class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				pass




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				pass




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    [
											    [],
											    [],
											    [],
											]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											try:
																		if len(self.queues[priority]) >= 1_0_0:
																									raise OverflowError("""Maximum queue size is 100""")
																		self.queues[priority].append(_A)
											except IndexError:
																		raise ValueError("""Valid priorities are 0, 1, and 2""")





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											for queue in self.queues:
																		if queue:
																									return queue.pop(0)
											raise UnderFlowError("""All queues are empty""")





				def __str__(      self							:							Union[str, Any]):



											"""simple docstring"""







											return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues))




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    []





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							int):



											"""simple docstring"""







											if len(self.queue) == 1_0_0:
																		raise OverFlowError("""Maximum queue size is 100""")
											self.queue.append(_A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											if not self.queue:
																		raise UnderFlowError("""The queue is empty""")
											else:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    min(self.queue)
																		self.queue.remove(_A)
																		return data





				def __str__(      self							:							int):



											"""simple docstring"""







											return str(self.queue)



def      lowerCamelCase_()->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Any			  =    FixedPriorityQueue()
							fpq.enqueue(0     ,	10  )
							fpq.enqueue(1     ,	70  )
							fpq.enqueue(0     ,	100  )
							fpq.enqueue(2     ,	1  )
							fpq.enqueue(2     ,	5  )
							fpq.enqueue(1     ,	7  )
							fpq.enqueue(2     ,	4  )
							fpq.enqueue(1     ,	64  )
							fpq.enqueue(0     ,	128  )
							print(__SCREAMING_SNAKE_CASE  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )
							print(__SCREAMING_SNAKE_CASE  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )
							print(fpq.dequeue()  )



def      lowerCamelCase_()->    str:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    ElementPriorityQueue()
							epq.enqueue(10  )
							epq.enqueue(70  )
							epq.enqueue(100  )
							epq.enqueue(1  )
							epq.enqueue(5  )
							epq.enqueue(7  )
							epq.enqueue(4  )
							epq.enqueue(64  )
							epq.enqueue(128  )
							print(__SCREAMING_SNAKE_CASE  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )
							print(__SCREAMING_SNAKE_CASE  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )
							print(epq.dequeue()  )


if __name__ == "__main__":
			fixed_priority_queue()
			element_priority_queue()



"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
			import torch

			from transformers import AutoModelForImageClassification

if is_vision_available():
			from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											_SCREAMING_SNAKE_CASE		:						str			  =    AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											model.to(_A)

											from datasets import load_dataset

											_SCREAMING_SNAKE_CASE		:						Any			  =    load_dataset("""nielsr/rvlcdip-demo""")

											_SCREAMING_SNAKE_CASE		:						Any			  =    dataset["""train"""][0]["""image"""].convert("""RGB""")

											_SCREAMING_SNAKE_CASE		:						str			  =    image_processor(_A      ,    return_tensors="""pt""").to(_A)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Any			  =    model(**_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.Size((1, 1_6))
											self.assertEqual(logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor(
											    [-0.4_158, -0.4_092, -0.4_347]      ,    device=_A      ,    dtype=torch.float      ,    )
											self.assertTrue(torch.allclose(logits[0, :3]      ,    _A      ,    atol=1e-4))


