
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     __lowerCAmelCase						(						unittest.TestCase   ):



      """simple docstring"""







      def 		snake_case_      (   self		:							int					,							_snake_case		:							List[Any]					):
          for model_result in results.values():
              for batch_size, sequence_length in zip(model_result['''bs''']					,							model_result['''ss''']					):
                  __lowercase		:      Any						     =							model_result["result"][batch_size][sequence_length]
                  self.assertIsNotNone(_snake_case					)




      def 		snake_case_      (   self		:							Any					):
          __lowercase		:      str						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      Optional[int]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      Union[str, Any]						     =							PyTorchBenchmark(_snake_case					)
          __lowercase		:      int						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result					)
          self.check_results_dict_not_empty(results.memory_inference_result					)




      def 		snake_case_      (   self		:							Tuple					):
          __lowercase		:      Optional[int]						     =							"sgugger/tiny-distilbert-classification"
          __lowercase		:      List[Any]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							only_pretrain_model=_snake_case					,							)
          __lowercase		:      Optional[Any]						     =							PyTorchBenchmark(_snake_case					)
          __lowercase		:      Any						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result					)
          self.check_results_dict_not_empty(results.memory_inference_result					)




      def 		snake_case_      (   self		:							Dict					):
          __lowercase		:      Optional[int]						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      List[Any]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							torchscript=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      Tuple						     =							PyTorchBenchmark(_snake_case					)
          __lowercase		:      Optional[Any]						     =							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 		snake_case_      (   self		:							Any					):
          __lowercase		:      int						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      List[str]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							fpaa=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      Optional[int]						     =							PyTorchBenchmark(_snake_case					)
          __lowercase		:      str						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result					)
          self.check_results_dict_not_empty(results.memory_inference_result					)




      def 		snake_case_      (   self		:							int					):
          __lowercase		:      int						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      Optional[Any]						     =							AutoConfig.from_pretrained(_snake_case					)
          # set architectures equal to `None`
          __lowercase		:      List[Any]						     =							None
          __lowercase		:      str						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      str						     =							PyTorchBenchmark(_snake_case					,							configs=[config]					)
          __lowercase		:      Union[str, Any]						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result					)
          self.check_results_dict_not_empty(results.memory_inference_result					)




      def 		snake_case_      (   self		:							str					):
          __lowercase		:      Tuple						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      Optional[Any]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      Dict						     =							PyTorchBenchmark(_snake_case					)
          __lowercase		:      int						     =							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 		snake_case_      (   self		:							List[Any]					):
          __lowercase		:      List[Any]						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      Tuple						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							fpaa=_snake_case					,							multi_process=_snake_case					,							)
          __lowercase		:      Any						     =							PyTorchBenchmark(_snake_case					)
          __lowercase		:      Optional[int]						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result					)
          self.check_results_dict_not_empty(results.memory_train_result					)




      def 		snake_case_      (   self		:							Optional[int]					):
          __lowercase		:      Union[str, Any]						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      Union[str, Any]						     =							AutoConfig.from_pretrained(_snake_case					)
          __lowercase		:      Optional[int]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      int						     =							PyTorchBenchmark(_snake_case					,							configs=[config]					)
          __lowercase		:      List[str]						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result					)
          self.check_results_dict_not_empty(results.memory_inference_result					)




      def 		snake_case_      (   self		:							Tuple					):
          __lowercase		:      str						     =							"sshleifer/tinier_bart"
          __lowercase		:      List[Any]						     =							AutoConfig.from_pretrained(_snake_case					)
          __lowercase		:      List[Any]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      Dict						     =							PyTorchBenchmark(_snake_case					,							configs=[config]					)
          __lowercase		:      Optional[Any]						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result					)
          self.check_results_dict_not_empty(results.memory_inference_result					)




      def 		snake_case_      (   self		:							List[str]					):
          __lowercase		:      Tuple						     =							"sshleifer/tiny-gpt2"
          __lowercase		:      Dict						     =							AutoConfig.from_pretrained(_snake_case					)
          __lowercase		:      Union[str, Any]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      Union[str, Any]						     =							PyTorchBenchmark(_snake_case					,							configs=[config]					)
          __lowercase		:      int						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result					)
          self.check_results_dict_not_empty(results.memory_train_result					)




      def 		snake_case_      (   self		:							Any					):
          __lowercase		:      int						     =							"sshleifer/tinier_bart"
          __lowercase		:      Any						     =							AutoConfig.from_pretrained(_snake_case					)
          __lowercase		:      Union[str, Any]						     =							PyTorchBenchmarkArguments(
              models=[MODEL_ID]					,							training=_snake_case					,							inference=_snake_case					,							sequence_lengths=[8]					,							batch_sizes=[1]					,							multi_process=_snake_case					,							)
          __lowercase		:      Tuple						     =							PyTorchBenchmark(_snake_case					,							configs=[config]					)
          __lowercase		:      List[str]						     =							benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result					)
          self.check_results_dict_not_empty(results.memory_train_result					)




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




      def 		snake_case_      (   self		:							Optional[int]					):
          __lowercase		:      Dict						     =							"sshleifer/tiny-gpt2"

          def _check_summary_is_not_empty(_snake_case		:							Dict					):
              self.assertTrue(hasattr(_snake_case					,							'''sequential'''					)					)
              self.assertTrue(hasattr(_snake_case					,							'''cumulative'''					)					)
              self.assertTrue(hasattr(_snake_case					,							'''current'''					)					)
              self.assertTrue(hasattr(_snake_case					,							'''total'''					)					)

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






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__     					=			Lock()


def     lowercase	(    a     , a     , a     , a     , a     , a     , a ):



		'''simple docstring'''




		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0     , 10 ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(a )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:str				     =						rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						SCREAMING_SNAKE_CASE_				:int				     =						min(a     , a )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(a )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:int				     =						lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						SCREAMING_SNAKE_CASE_				:Dict				     =						max(a     , a )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(a )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe() )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		SCREAMING_SNAKE_CASE_				:str				     =						Pipe()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Pipe()
		process_array_.append(
		    Process(
		        target=a     , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])     , ) )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						temp_rs
		SCREAMING_SNAKE_CASE_				:Any				     =						temp_rr

		for i in range(1     , len(a ) - 1 ):
				SCREAMING_SNAKE_CASE_				:int				     =						Pipe()
				SCREAMING_SNAKE_CASE_				:Dict				     =						Pipe()
				process_array_.append(
				    Process(
				        target=a     , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])     , ) )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp_rs
				SCREAMING_SNAKE_CASE_				:int				     =						temp_rr

		process_array_.append(
		    Process(
		        target=a     , args=(
		            len(a ) - 1,
		            arr[len(a ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(a ) - 1],
		        )     , ) )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0     , len(a ) ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						result_pipe[p][0].recv()
				process_array_[p].join()
		return arr


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						list(range(10     , 0     , -1 ) )
		print("Initial List" )
		print(*a )
		SCREAMING_SNAKE_CASE_				:int				     =						odd_even_transposition(a )
		print("Sorted List\n" )
		print(*a )


if __name__ == "__main__":
		main()






