from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_a  :  int=				Lock()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       Any						)							->    Optional[int]:

	'''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(UpperCAmelCase_						)
			process_lock.release()

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

			# take the lower value since you are on the left
			__snake_case    :							Any   	=				min(UpperCAmelCase_    ,							UpperCAmelCase_						)
		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(UpperCAmelCase_						)
			process_lock.release()

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

			# take the higher value since you are on the right
			__snake_case    :							Optional[Any]   	=				max(UpperCAmelCase_    ,							UpperCAmelCase_						)
    # after all swaps are performed, send the values back to main
	result_pipe[1].send(UpperCAmelCase_						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    str:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				[]
	__snake_case    :							List[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
	__snake_case    :							Tuple   	=				Pipe()
	__snake_case    :							List[Any]   	=				Pipe()
	process_array_.append(
	    Process(
	        target=UpperCAmelCase_    ,							args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])    ,							)						)
	__snake_case    :							str   	=				temp_rs
	__snake_case    :							List[str]   	=				temp_rr

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

	process_array_.append(
	    Process(
	        target=UpperCAmelCase_    ,							args=(
	            len(UpperCAmelCase_						) - 1,
	            arr[len(UpperCAmelCase_						) - 1],
	            temp_ls,
	            None,
	            temp_lr,
	            None,
	            result_pipe[len(UpperCAmelCase_						) - 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(UpperCAmelCase_						)						):
		__snake_case    :							Union[str, Any]   	=				result_pipe[p][0].recv()
		process_array_[p].join()
	return arr
def 						__UpperCAmelCase       (					)							->    Optional[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				list(range(10    ,							0    ,							-1						)						)
	print('Initial List'						)
	print(*UpperCAmelCase_						)
	__snake_case    :							Tuple   	=				odd_even_transposition(UpperCAmelCase_						)
	print('Sorted List\n'						)
	print(*UpperCAmelCase_						)


if __name__ == "__main__":
	main()






"""simple docstring"""







import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers import AutoModelForCausalLM

@require_torch
class 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							Optional[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Any   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							str   	=				tokenizer.decode(greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							List[str]   	=				TextStreamer(_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							Optional[Any]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Any   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							List[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Optional[int]   	=				-1

					__snake_case    :							str   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							int   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							int   	=				tokenizer.decode(greedy_ids[0])

					__snake_case    :							int   	=				TextIteratorStreamer(_A)
					__snake_case    :							List[Any]   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							Any   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()
					__snake_case    :							Dict   	=				''
					for new_text in streamer:
						streamer_text += new_text

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Any   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Optional[int]   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							Union[str, Any]   	=				greedy_ids[:, input_ids.shape[1] :]
					__snake_case    :							Optional[Any]   	=				tokenizer.decode(new_greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							str   	=				TextStreamer(_A			,						skip_prompt=_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							List[str]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   List[str])					->			Dict:
					# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
					# with actual models -- the dummy models' tokenizers are not aligned with their models, and
					# `skip_special_tokens=True` has no effect on them
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('distilgpt2')
					__snake_case    :							Dict   	=				AutoModelForCausalLM.from_pretrained('distilgpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[str]   	=				torch.ones((1, 5)			,						device=_A).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
						__snake_case    :							Tuple   	=				TextStreamer(_A			,						skip_special_tokens=_A)
						model.generate(_A			,						max_new_tokens=1			,						do_sample=_A			,						streamer=_A)

					# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
					# re-tokenized, must only contain one token
					__snake_case    :							Tuple   	=				cs.out[:-1]  # Remove the final "\n"
					__snake_case    :							int   	=				tokenizer(_A			,						return_tensors='pt')
					self.assertEqual(streamer_text_tokenized.input_ids.shape			,						(1, 1))






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[int]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[int]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							int   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							List[Any]   	=				TextIteratorStreamer(_A			,						timeout=0.001)
					__snake_case    :							str   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							str   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()

					# The streamer will timeout after 0.001 seconds, so an exception will be raised
					with self.assertRaises(_A):
						__snake_case    :							Any   	=				''
						for new_text in streamer:
							streamer_text += new_text





