




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
					from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											for model_result in results.values():
																for batch_size, sequence_length in zip(model_result["bs"]		,			model_result["ss"]			):
																					__lowerCAmelCase				   =       model_result["result"][batch_size][sequence_length]
																					self.assertIsNotNone(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_inference_result			)
											self.check_results_dict_not_empty(results.memory_inference_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sgugger/tiny-distilbert-classification"
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			only_pretrain_model=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_inference_result			)
											self.check_results_dict_not_empty(results.memory_inference_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			torchscript=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_inference_result			)
											self.check_results_dict_not_empty(results.memory_inference_result			)






						@unittest.skipIf(torch_device == "cpu"		,			"Cant do half precision"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			fpaa=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_inference_result			)
											self.check_results_dict_not_empty(results.memory_inference_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       AutoConfig.from_pretrained(_A			)
											# set architectures equal to `None`
											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A		,			configs=[config]			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_inference_result			)
											self.check_results_dict_not_empty(results.memory_inference_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_train_result			)
											self.check_results_dict_not_empty(results.memory_train_result			)






						@unittest.skipIf(torch_device == "cpu"		,			"Can't do half precision"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			fpaa=_A		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_train_result			)
											self.check_results_dict_not_empty(results.memory_train_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       AutoConfig.from_pretrained(_A			)
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A		,			configs=[config]			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_inference_result			)
											self.check_results_dict_not_empty(results.memory_inference_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tinier_bart"
											__lowerCAmelCase				   =       AutoConfig.from_pretrained(_A			)
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A		,			configs=[config]			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_inference_result			)
											self.check_results_dict_not_empty(results.memory_inference_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											__lowerCAmelCase				   =       AutoConfig.from_pretrained(_A			)
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A		,			configs=[config]			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_train_result			)
											self.check_results_dict_not_empty(results.memory_train_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tinier_bart"
											__lowerCAmelCase				   =       AutoConfig.from_pretrained(_A			)
											__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
											    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=_A		,			)
											__lowerCAmelCase				   =       PyTorchBenchmark(_A		,			configs=[config]			)
											__lowerCAmelCase				   =       benchmark.run()
											self.check_results_dict_not_empty(results.time_train_result			)
											self.check_results_dict_not_empty(results.memory_train_result			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"
											with tempfile.TemporaryDirectory() as tmp_dir:
																__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
																    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			save_to_csv=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			inference_time_csv_file=os.path.join(_A		,			"inf_time.csv"			)		,			train_memory_csv_file=os.path.join(_A		,			"train_mem.csv"			)		,			inference_memory_csv_file=os.path.join(_A		,			"inf_mem.csv"			)		,			train_time_csv_file=os.path.join(_A		,			"train_time.csv"			)		,			env_info_csv_file=os.path.join(_A		,			"env.csv"			)		,			multi_process=_A		,			)
																__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
																benchmark.run()
																self.assertTrue(Path(os.path.join(_A		,			"inf_time.csv"			)			).exists()			)
																self.assertTrue(Path(os.path.join(_A		,			"train_time.csv"			)			).exists()			)
																self.assertTrue(Path(os.path.join(_A		,			"inf_mem.csv"			)			).exists()			)
																self.assertTrue(Path(os.path.join(_A		,			"train_mem.csv"			)			).exists()			)
																self.assertTrue(Path(os.path.join(_A		,			"env.csv"			)			).exists()			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "sshleifer/tiny-gpt2"

											def _check_summary_is_not_empty(_A			):
																self.assertTrue(hasattr(_A		,			"sequential"			)			)
																self.assertTrue(hasattr(_A		,			"cumulative"			)			)
																self.assertTrue(hasattr(_A		,			"current"			)			)
																self.assertTrue(hasattr(_A		,			"total"			)			)

											with tempfile.TemporaryDirectory() as tmp_dir:
																__lowerCAmelCase				   =       PyTorchBenchmarkArguments(
																    models=[MODEL_ID]		,			training=_A		,			inference=_A		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			log_filename=os.path.join(_A		,			"log.txt"			)		,			log_print=_A		,			trace_memory_line_by_line=_A		,			multi_process=_A		,			)
																__lowerCAmelCase				   =       PyTorchBenchmark(_A			)
																__lowerCAmelCase				   =       benchmark.run()
																_check_summary_is_not_empty(result.inference_summary			)
																_check_summary_is_not_empty(result.train_summary			)
																self.assertTrue(Path(os.path.join(_A		,			"log.txt"			)			).exists()			)










class 			a__    (					snake_case__      ):
						pass






class 			a__    (					snake_case__      ):
						pass






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [
											    [],
											    [],
											    [],
											]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def __str__(   self			):




											"""simple docstring"""



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






class 			a__    :






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def __str__(   self			):




											"""simple docstring"""



											return str(self.queue			)







def 	_a      (						):
					__lowerCAmelCase				   =       FixedPriorityQueue()
					fpq.enqueue(0      ,      10     )
					fpq.enqueue(1      ,      70     )
					fpq.enqueue(0      ,      1_00     )
					fpq.enqueue(2      ,      1     )
					fpq.enqueue(2      ,      5     )
					fpq.enqueue(1      ,      7     )
					fpq.enqueue(2      ,      4     )
					fpq.enqueue(1      ,      64     )
					fpq.enqueue(0      ,      1_28     )
					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 	_a      (						):
					__lowerCAmelCase				   =       ElementPriorityQueue()
					epq.enqueue(10     )
					epq.enqueue(70     )
					epq.enqueue(1_00     )
					epq.enqueue(1     )
					epq.enqueue(5     )
					epq.enqueue(7     )
					epq.enqueue(4     )
					epq.enqueue(64     )
					epq.enqueue(1_28     )
					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()




