
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							pass







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							pass







class 				lowercase		:




							def __init__(				self):
												lowercase							=						[
												    [],
												    [],
												    [],
												]




							def  A__      (				self   ,A__   ,A__):
												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  A__      (				self):
												for queue in self.queues:
																	if queue:
																						return queue.pop(0)
												raise UnderFlowError('''All queues are empty''')





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







class 				lowercase		:




							def __init__(				self):
												lowercase							=						[]




							def  A__      (				self   ,A__):
												if len(self.queue) == 1_0_0:
																	raise OverFlowError('''Maximum queue size is 100''')
												self.queue.append(A__)




							def  A__      (				self):
												if not self.queue:
																	raise UnderFlowError('''The queue is empty''')
												else:
																	lowercase							=						min(self.queue)
																	self.queue.remove(A__)
																	return data





							def __str__(				self):
												return str(self.queue)



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						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(lowerCAmelCase__		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)
					print(lowerCAmelCase__		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)
					print(fpq.dequeue()		)



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						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(lowerCAmelCase__		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)
					print(lowerCAmelCase__		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)
					print(epq.dequeue()		)


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






import os
import sys

lowercase__		:Tuple   =    os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase__		:List[Any]   =    [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]



@add_start_docstrings(AutoConfig.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoConfig.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoTokenizer.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoTokenizer.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModel.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModel.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForCausalLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForMaskedLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForSequenceClassification.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForQuestionAnswering.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)




