



"""simple docstring"""






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase			:			str										=						get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							XLMRobertaTokenizer
					_a             =							XLMRobertaTokenizerFast
					_a             =							True
					_a             =							True



					def 		snake_case	(	self							:  Union[str, Any]					)-> Any:
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__		:		Union[str, Any]		     =XLMRobertaTokenizer(lowerCamelCase,  keep_accents=lowerCamelCase					)
								tokenizer.save_pretrained(self.tmpdirname					)



					def 		snake_case	(	self							:  Any					)-> Tuple:
								lowerCamelCase__		:		Any		     ='''<pad>'''
								lowerCamelCase__		:		str		     =1

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



					def 		snake_case	(	self							:  int					)-> int:
								lowerCamelCase__		:		Optional[int]		     =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(lowerCamelCase					),  1002					)



					def 		snake_case	(	self							:  Dict					)-> List[str]:
								self.assertEqual(self.get_tokenizer().vocab_size,  1002					)



					def 		snake_case	(	self							:  Union[str, Any]					)-> Optional[Any]:
								lowerCamelCase__		:		Dict		     =XLMRobertaTokenizer(lowerCamelCase,  keep_accents=lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =tokenizer.tokenize('''This is a test'''					)
								self.assertListEqual(lowerCamelCase,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']					)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(lowerCamelCase					),  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],  )

								lowerCamelCase__		:		Any		     =tokenizer.tokenize('''I was born in 92000, and this is falsé.'''					)
								self.assertListEqual(
								    lowerCamelCase,  [
								        SPIECE_UNDERLINE + '''I''',
								        SPIECE_UNDERLINE + '''was''',
								        SPIECE_UNDERLINE + '''b''',
								        '''or''',
								        '''n''',
								        SPIECE_UNDERLINE + '''in''',
								        SPIECE_UNDERLINE + '''''',
								        '''9''',
								        '''2''',
								        '''0''',
								        '''0''',
								        '''0''',
								        ''',''',
								        SPIECE_UNDERLINE + '''and''',
								        SPIECE_UNDERLINE + '''this''',
								        SPIECE_UNDERLINE + '''is''',
								        SPIECE_UNDERLINE + '''f''',
								        '''al''',
								        '''s''',
								        '''é''',
								        '''.''',
								    ],  )
								lowerCamelCase__		:		Tuple		     =tokenizer.convert_tokens_to_ids(lowerCamelCase					)
								self.assertListEqual(
								    lowerCamelCase,  [
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ],  )

								lowerCamelCase__		:		Optional[Any]		     =tokenizer.convert_ids_to_tokens(lowerCamelCase					)
								self.assertListEqual(
								    lowerCamelCase,  [
								        SPIECE_UNDERLINE + '''I''',
								        SPIECE_UNDERLINE + '''was''',
								        SPIECE_UNDERLINE + '''b''',
								        '''or''',
								        '''n''',
								        SPIECE_UNDERLINE + '''in''',
								        SPIECE_UNDERLINE + '''''',
								        '''<unk>''',
								        '''2''',
								        '''0''',
								        '''0''',
								        '''0''',
								        ''',''',
								        SPIECE_UNDERLINE + '''and''',
								        SPIECE_UNDERLINE + '''this''',
								        SPIECE_UNDERLINE + '''is''',
								        SPIECE_UNDERLINE + '''f''',
								        '''al''',
								        '''s''',
								        '''<unk>''',
								        '''.''',
								    ],  )



					def 		snake_case	(	self							:  Dict					)-> int:
								if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

								lowerCamelCase__		:		Optional[Any]		     =(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														lowerCamelCase__		:		Tuple		     =self.rust_tokenizer_class.from_pretrained(lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		Union[str, Any]		     =self.tokenizer_class.from_pretrained(lowerCamelCase,  **lowerCamelCase					)

														lowerCamelCase__		:		Any		     =tempfile.mkdtemp()

														lowerCamelCase__		:		Any		     =tokenizer_r.save_pretrained(lowerCamelCase					)
														lowerCamelCase__		:		Any		     =tokenizer_p.save_pretrained(lowerCamelCase					)

														# Checks it save with the same files + the tokenizer.json file for the fast one
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files					)					)
														lowerCamelCase__		:		List[Any]		     =tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f					)
														self.assertSequenceEqual(lowerCamelCase,  lowerCamelCase					)

														# Checks everything loads correctly in the same way
														lowerCamelCase__		:		Optional[int]		     =tokenizer_r.from_pretrained(lowerCamelCase					)
														lowerCamelCase__		:		Union[str, Any]		     =tokenizer_p.from_pretrained(lowerCamelCase					)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,  lowerCamelCase					)					)
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(lowerCamelCase					)

														# Save tokenizer rust, legacy_format=True
														lowerCamelCase__		:		str		     =tempfile.mkdtemp()

														lowerCamelCase__		:		Tuple		     =tokenizer_r.save_pretrained(lowerCamelCase,  legacy_format=lowerCamelCase					)
														lowerCamelCase__		:		Optional[Any]		     =tokenizer_p.save_pretrained(lowerCamelCase					)

														# Checks it save with the same files
														self.assertSequenceEqual(lowerCamelCase,  lowerCamelCase					)

														# Checks everything loads correctly in the same way
														lowerCamelCase__		:		Union[str, Any]		     =tokenizer_r.from_pretrained(lowerCamelCase					)
														lowerCamelCase__		:		Union[str, Any]		     =tokenizer_p.from_pretrained(lowerCamelCase					)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,  lowerCamelCase					)					)

														shutil.rmtree(lowerCamelCase					)

														# Save tokenizer rust, legacy_format=False
														lowerCamelCase__		:		Optional[int]		     =tempfile.mkdtemp()

														lowerCamelCase__		:		Tuple		     =tokenizer_r.save_pretrained(lowerCamelCase,  legacy_format=lowerCamelCase					)
														lowerCamelCase__		:		Union[str, Any]		     =tokenizer_p.save_pretrained(lowerCamelCase					)

														# Checks it saved the tokenizer.json file
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files					)					)

														# Checks everything loads correctly in the same way
														lowerCamelCase__		:		List[str]		     =tokenizer_r.from_pretrained(lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =tokenizer_p.from_pretrained(lowerCamelCase					)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,  lowerCamelCase					)					)

														shutil.rmtree(lowerCamelCase					)



					@cached_property
					def 		snake_case	(	self							:  str					)-> Any:
								return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''					)



					def 		snake_case	(	self							:  Dict					)-> Optional[int]:
								with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(lowerCamelCase,  f.name					)
											lowerCamelCase__		:		str		     =XLMRobertaTokenizer(f.name,  keep_accents=lowerCamelCase					)
											lowerCamelCase__		:		int		     =pickle.dumps(lowerCamelCase					)
								pickle.loads(lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								if not self.test_rust_tokenizer:
											return

								lowerCamelCase__		:		Any		     =self.get_tokenizer()
								lowerCamelCase__		:		Any		     =self.get_rust_tokenizer()

								lowerCamelCase__		:		str		     ='''I was born in 92000, and this is falsé.'''

								lowerCamelCase__		:		int		     =tokenizer.tokenize(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =rust_tokenizer.tokenize(lowerCamelCase					)
								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)

								lowerCamelCase__		:		int		     =tokenizer.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =rust_tokenizer.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase					)
								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =self.get_rust_tokenizer()
								lowerCamelCase__		:		Optional[Any]		     =tokenizer.encode(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =rust_tokenizer.encode(lowerCamelCase					)
								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)



					@slow
					def 		snake_case	(	self							:  str					)-> Tuple:
								lowerCamelCase__		:		Any		     ='''Hello World!'''
								lowerCamelCase__		:		int		     =[0, 3_5378, 6661, 38, 2]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(lowerCamelCase,  self.big_tokenizer.encode(lowerCamelCase					)					)



					@slow
					def 		snake_case	(	self							:  Optional[Any]					)-> List[str]:
								lowerCamelCase__		:		List[str]		     =(
								    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
								    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
								)
								lowerCamelCase__		:		Any		     =[
								    0,
								    3293,
								    83,
								    10,
								    4552,
								    4989,
								    7986,
								    678,
								    10,
								    5915,
								    111,
								    17_9459,
								    12_4850,
								    4,
								    6044,
								    237,
								    12,
								    6,
								    5,
								    6,
								    4,
								    6780,
								    705,
								    15,
								    1388,
								    44,
								    378,
								    1_0114,
								    711,
								    152,
								    20,
								    6,
								    5,
								    2_2376,
								    642,
								    1221,
								    1_5190,
								    3_4153,
								    450,
								    5608,
								    959,
								    1119,
								    5_7702,
								    136,
								    186,
								    47,
								    1098,
								    2_9367,
								    47,
								    # 4426, # What fairseq tokenizes from "<unk>": "_<"
								    # 3678, # What fairseq tokenizes from "<unk>": "unk"
								    # 2740, # What fairseq tokenizes from "<unk>": ">"
								    3,  # What we tokenize from "<unk>": "<unk>"
								    6,  # Residue from the tokenization: an extra sentencepiece underline
								    4,
								    6044,
								    237,
								    6284,
								    5_0901,
								    528,
								    31,
								    90,
								    34,
								    927,
								    2,
								]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(lowerCamelCase,  self.big_tokenizer.encode(lowerCamelCase					)					)




					@slow
					def 		snake_case	(	self							:  List[str]					)-> Optional[int]:
								# fmt: off
								lowerCamelCase__		:		Tuple		     ={'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 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, 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], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 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, 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, 1, 1, 1, 1]], '''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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=lowerCamelCase,  model_name='''xlm-roberta-base''',  revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''',  )









"""simple docstring"""






from collections import defaultdict

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		List[Any]		     =total  # total no of tasks (N)

								# DP table will have a dimension of (2^M)*N
								# initially all values are set to -1
								lowerCamelCase__		:		Optional[Any]		     =[
								    [-1 for i in range(total + 1					)] for j in range(2 ** len(lowerCamelCase					)					)
								]

								lowerCamelCase__		:		Any		     =defaultdict(lowerCamelCase					)  # stores the list of persons for each task

								# final_mask is used to check if all persons are included by setting all bits
								# to 1
								lowerCamelCase__		:		List[Any]		     =(1 << len(lowerCamelCase					)) - 1



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Any					)-> Any:
								# if mask == self.finalmask all persons are distributed tasks, return 1
								if mask == self.final_mask:
											return 1

								# if not everyone gets the task and no more tasks are available, return 0
								if task_no > self.total_tasks:
											return 0

								# if case already considered
								if self.dp[mask][task_no] != -1:
											return self.dp[mask][task_no]

								# Number of ways when we don't this task in the arrangement
								lowerCamelCase__		:		Optional[int]		     =self.count_ways_until(lowerCamelCase,  task_no + 1					)

								# now assign the tasks one by one to all possible persons and recursively
								# assign for the remaining tasks.
								if task_no in self.task:
											for p in self.task[task_no]:
														# if p is already given a task
														if mask & (1 << p):
																	continue

														# assign this task to p and change the mask value. And recursively
														# assign tasks with the new mask value.
														total_ways_util += self.count_ways_until(mask | (1 << p),  task_no + 1					)

        # save the value.
								lowerCamelCase__		:		int		     =total_ways_util

								return self.dp[mask][task_no]




					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Dict					)-> int:
								# Store the list of persons for each task
								for i in range(len(lowerCamelCase					)					):
											for j in task_performed[i]:
														self.task[j].append(lowerCamelCase					)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
								return self.count_ways_until(0,  1					)


if __name__ == "__main__":
		_lowercase			:			Tuple										=						5  # total no of tasks (the value of N)

		# the list of tasks that can be done by M persons.
		_lowercase			:			Dict										=						[[1, 3, 4], [1, 2, 5], [3, 4]]
		print(
		    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
		        task_performed
		    )
		)




