




from multiprocessing import Lock, Pipe, Process

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





def 				A__ (snake_case   :		int		,	snake_case   :		Tuple		,	snake_case   :		str		,	snake_case   :		Dict		,	snake_case   :		List[Any]		,	snake_case   :		List[Any]		,	snake_case   :		str				)     ->						Optional[Any]:
					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(snake_case				)
															process_lock.release()

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

															# take the lower value since you are on the left
															__UpperCamelCase					:	Union[str, Any]             =      min(snake_case		,	snake_case				)
										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(snake_case				)
															process_lock.release()

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

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





def 				A__ (snake_case   :		Optional[int]				)     ->						List[str]:
					__UpperCamelCase					:	Any             =      []
					__UpperCamelCase					:	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
					__UpperCamelCase					:	int             =      Pipe()
					__UpperCamelCase					:	Dict             =      Pipe()
					process_array_.append(
					    Process(
					        target=snake_case		,	args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])		,	)				)
					__UpperCamelCase					:	Optional[Any]             =      temp_rs
					__UpperCamelCase					:	Dict             =      temp_rr

					for i in range(1		,	len(snake_case				) - 1				):
										__UpperCamelCase					:	Optional[int]             =      Pipe()
										__UpperCamelCase					:	List[Any]             =      Pipe()
										process_array_.append(
										    Process(
										        target=snake_case		,	args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])		,	)				)
										__UpperCamelCase					:	Any             =      temp_rs
										__UpperCamelCase					:	Optional[int]             =      temp_rr

					process_array_.append(
					    Process(
					        target=snake_case		,	args=(
					            len(snake_case				) - 1,
					            arr[len(snake_case				) - 1],
					            temp_ls,
					            None,
					            temp_lr,
					            None,
					            result_pipe[len(snake_case				) - 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(snake_case				)				):
										__UpperCamelCase					:	Union[str, Any]             =      result_pipe[p][0].recv()
										process_array_[p].join()
					return arr





def 				A__ ()     ->						Optional[Any]:
					__UpperCamelCase					:	int             =      list(range(10		,	0		,	-1				)				)
					print("""Initial List"""				)
					print(*snake_case				)
					__UpperCamelCase					:	str             =      odd_even_transposition(snake_case				)
					print("""Sorted List\n"""				)
					print(*snake_case				)


if __name__ == "__main__":
	main()












from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      SMALL_MODEL_IDENTIFIER
						__UpperCamelCase					:	List[str]             =      """pt"""
						__UpperCamelCase					:	List[Any]             =      """tf"""






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						Any	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      TFAutoModel.from_pretrained(self.test_model					,     from_pt=lowerCAmelCase	)
						model_tf.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      """mock_framework"""

						# Framework provided - return whatever the user provides
						__UpperCamelCase					:	List[Any]             =      FeaturesManager.determine_framework(self.test_model					,     lowerCAmelCase	)
						self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Optional[Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						__UpperCamelCase					:	Optional[int]             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Both in environment -> use PyTorch
						__UpperCamelCase					:	Dict             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# Both not in environment -> raise error
						__UpperCamelCase					:	Union[str, Any]             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	str             =      FeaturesManager.determine_framework(self.test_model	)






