

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						BarthezTokenizer
			SCREAMING_SNAKE_CASE							   =						BarthezTokenizerFast
			SCREAMING_SNAKE_CASE							   =						True
			SCREAMING_SNAKE_CASE							   =						True





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						super().setUp()

						__a        =BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'					)
						tokenizer.save_pretrained(self.tmpdirname					)
						tokenizer.save_pretrained(self.tmpdirname					,  legacy_format=__snake_case					)
						__a        =tokenizer





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        ='<pad>'
						__a        =1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case					)					,  __snake_case					)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case					)					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =list(self.get_tokenizer().get_vocab().keys()					)

						self.assertEqual(vocab_keys[0]					,  '<s>'					)
						self.assertEqual(vocab_keys[1]					,  '<pad>'					)
						self.assertEqual(vocab_keys[-1]					,  '<mask>'					)
						self.assertEqual(len(__snake_case					)					,  10_1122					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						self.assertEqual(self.get_tokenizer().vocab_size					,  10_1122					)





			@require_torch
			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =['A long paragraph for summarization.', 'Another paragraph for summarization.']
						__a        =[0, 57, 3018, 7_0307, 91, 2]

						__a        =self.tokenizer(
						    __snake_case					,  max_length=len(__snake_case					)					,  padding=__snake_case					,  truncation=__snake_case					,  return_tensors='pt'					)
						self.assertIsInstance(__snake_case					,  __snake_case					)

						self.assertEqual((2, 6)					,  batch.input_ids.shape					)
						self.assertEqual((2, 6)					,  batch.attention_mask.shape					)
						__a        =batch.input_ids.tolist()[0]
						self.assertListEqual(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						if not self.test_rust_tokenizer:
									return

						__a        =self.get_tokenizer()
						__a        =self.get_rust_tokenizer()

						__a        ='I was born in 92000, and this is falsé.'

						__a        =tokenizer.tokenize(__snake_case					)
						__a        =rust_tokenizer.tokenize(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						__a        =tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case					)
						__a        =rust_tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						__a        =self.get_rust_tokenizer()
						__a        =tokenizer.encode(__snake_case					)
						__a        =rust_tokenizer.encode(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						# fmt: off
						__a        ={'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# moussaKam/mbarthez is a french model. So we also use french texts.
						__a        =[
						    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
						    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
						    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
						    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
						    'telles que la traduction et la synthèse de texte.',
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=__snake_case					,  model_name='moussaKam/mbarthez'					,  revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'					,  sequences=__snake_case					,  )









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()






