



'''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 							lowercase__	(							unittest.TestCase   ):





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
							_UpperCamelCase					:		Any 				=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(lowerCamelCase__ )
							_UpperCamelCase					:		str 				=   -1

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

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

							self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
							_UpperCamelCase					:		Union[str, Any] 				=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   -1

							_UpperCamelCase					:		Optional[int] 				=   ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   model.generate(lowerCamelCase__ ,max_new_tokens=10 ,do_sample=lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   tokenizer.decode(greedy_ids[0] )

							_UpperCamelCase					:		List[str] 				=   TextIteratorStreamer(lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
							_UpperCamelCase					:		Optional[Any] 				=   Thread(target=model.generate ,kwargs=lowerCamelCase__ )
							thread.start()
							_UpperCamelCase					:		Tuple 				=   ''
							for new_text in streamer:
													streamer_text += new_text

							self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
							_UpperCamelCase					:		int 				=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   -1

							_UpperCamelCase					:		Dict 				=   ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(lowerCamelCase__ )
							_UpperCamelCase					:		str 				=   model.generate(lowerCamelCase__ ,max_new_tokens=10 ,do_sample=lowerCamelCase__ )
							_UpperCamelCase					:		Optional[Any] 				=   greedy_ids[:, input_ids.shape[1] :]
							_UpperCamelCase					:		str 				=   tokenizer.decode(new_greedy_ids[0] )

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

							self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							# 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
							_UpperCamelCase					:		Dict 				=   AutoTokenizer.from_pretrained('distilgpt2' )
							_UpperCamelCase					:		int 				=   AutoModelForCausalLM.from_pretrained('distilgpt2' ).to(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[Any] 				=   -1

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

							# 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
							_UpperCamelCase					:		Dict 				=   cs.out[:-1]  # Remove the final "\n"
							_UpperCamelCase					:		str 				=   tokenizer(lowerCamelCase__ ,return_tensors='pt' )
							self.assertEqual(streamer_text_tokenized.input_ids.shape ,(1, 1) )





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
							_UpperCamelCase					:		Optional[Any] 				=   AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   -1

							_UpperCamelCase					:		Dict 				=   ids_tensor((1, 5) ,vocab_size=model.config.vocab_size ).to(lowerCamelCase__ )
							_UpperCamelCase					:		str 				=   TextIteratorStreamer(lowerCamelCase__ ,timeout=0.0_0_1 )
							_UpperCamelCase					:		Any 				=   {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
							_UpperCamelCase					:		Optional[int] 				=   Thread(target=model.generate ,kwargs=lowerCamelCase__ )
							thread.start()

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





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_lowerCAmelCase					:			Optional[Any]  				=			Lock()






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  Dict       ,     _snake_case   :  Any       ,     _snake_case   :  List[str]       ,     _snake_case   :  Tuple       ,     _snake_case   :  List[str]	):




			"""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(_snake_case	)
									process_lock.release()

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

									# take the lower value since you are on the left
									__a        =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()
									__a        =lr_cv[0].recv()
									process_lock.release()

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






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			__a        =[]
			__a        =[]
			# 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
			__a        =Pipe()
			__a        =Pipe()
			process_array_.append(
			    Process(
			        target=_snake_case       ,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])       ,     )	)
			__a        =temp_rs
			__a        =temp_rr

			for i in range(1       ,     len(_snake_case	) - 1	):
						__a        =Pipe()
						__a        =Pipe()
						process_array_.append(
						    Process(
						        target=_snake_case       ,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])       ,     )	)
						__a        =temp_rs
						__a        =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	)	):
						__a        =result_pipe[p][0].recv()
						process_array_[p].join()
			return arr






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =list(range(10       ,     0       ,     -1	)	)
			print('Initial List'	)
			print(*_snake_case	)
			__a        =odd_even_transposition(_snake_case	)
			print('Sorted List\n'	)
			print(*_snake_case	)


if __name__ == "__main__":
						main()






