




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__       :      List[str]  =   '''▁'''

SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
class 		UpperCamelCase__	(snake_case__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      BertGenerationTokenizer
    lowerCamelCase_      :	str	     =      False
    lowerCamelCase_      :	Tuple	     =      True







    def     _lowercase     (    self     )     ->      Optional[Any]:
           super().setUp()

           lowerCamelCase   :      Tuple			 =					BertGenerationTokenizer(_A     ,  keep_accents=_A     )
           tokenizer.save_pretrained(self.tmpdirname     )







    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      str			 =					'<s>'
           lowerCamelCase   :      str			 =					1

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







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					list(self.get_tokenizer().get_vocab().keys()     )

           self.assertEqual(vocab_keys[0]     ,  "<unk>"     )
           self.assertEqual(vocab_keys[1]     ,  "<s>"     )
           self.assertEqual(vocab_keys[-1]     ,  "<pad>"     )
           self.assertEqual(len(_A     )     ,  1002     )







    def     _lowercase     (    self     )     ->      List[Any]:
           self.assertEqual(self.get_tokenizer().vocab_size     ,  1000     )







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      str			 =					BertGenerationTokenizer(_A     ,  keep_accents=_A     )

           lowerCamelCase   :      Dict			 =					tokenizer.tokenize("This is a test"     )
           self.assertListEqual(_A     ,  ["▁This", "▁is", "▁a", "▁t", "est"]     )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(_A     )     ,  [285, 46, 10, 170, 382]     ,  )

           lowerCamelCase   :      int			 =					tokenizer.tokenize("I was born in 92000, and this is falsé."     )
           self.assertListEqual(
               _A     ,  [
                   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   :      Dict			 =					tokenizer.convert_tokens_to_ids(_A     )
           self.assertListEqual(
               _A     ,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,  )

           lowerCamelCase   :      Optional[int]			 =					tokenizer.convert_ids_to_tokens(_A     )
           self.assertListEqual(
               _A     ,  [
                   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>",
                   ".",
               ]     ,  )







    @cached_property
    def     _lowercase     (    self     )     ->      List[str]:
           return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"     )







    @slow
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      List[Any]			 =					'Hello World!'
           lowerCamelCase   :      Optional[Any]			 =					[1_8536, 2260, 101]

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







    @slow
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Dict			 =					(
               '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   :      int			 =					[
               871,
               419,
               358,
               946,
               991,
               2521,
               452,
               358,
               1357,
               387,
               7751,
               3536,
               112,
               985,
               456,
               126,
               865,
               938,
               5400,
               5734,
               458,
               1368,
               467,
               786,
               2462,
               5246,
               1159,
               633,
               865,
               4519,
               457,
               582,
               852,
               2557,
               427,
               916,
               508,
               405,
               3_4324,
               497,
               391,
               408,
               1_1342,
               1244,
               385,
               100,
               938,
               985,
               456,
               574,
               362,
               1_2597,
               3200,
               3129,
               1172,
           ]

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







    @require_torch
    @slow
    def     _lowercase     (    self     )     ->      str:
           import torch

           from transformers import BertGenerationConfig, BertGenerationEncoder

           # Build sequence
           lowerCamelCase   :      Tuple			 =					list(self.big_tokenizer.get_vocab().keys()     )[:10]
           lowerCamelCase   :      List[Any]			 =					' '.join(_A     )
           lowerCamelCase   :      Union[str, Any]			 =					self.big_tokenizer.encode_plus(_A     ,  return_tensors="pt"     ,  return_token_type_ids=_A     )
           lowerCamelCase   :      Optional[Any]			 =					self.big_tokenizer.batch_encode_plus(
               [sequence + " " + sequence]     ,  return_tensors="pt"     ,  return_token_type_ids=_A     )

           lowerCamelCase   :      int			 =					BertGenerationConfig()
           lowerCamelCase   :      List[str]			 =					BertGenerationEncoder(_A     )

           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                  model(**_A     )
                  model(**_A     )







    @slow
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           # fmt: off
           lowerCamelCase   :      str			 =					{'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 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], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 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]], '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, 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, 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=_A     ,  model_name="google/bert_for_seq_generation_L-24_bbc_encoder"     ,  revision="c817d1fd1be2ffa69431227a1fe320544943d4db"     ,  )













import math







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      list[int]:
					__A	:						List[str]       =				[]
					__A	:						Any       =				2
					__A	:						Union[str, Any]       =				int(math.sqrt(a  )  )  # Size of every segment
					__A	:						Any       =				[True] * (end + 1)
					__A	:						List[Any]       =				[]

					while start <= end:
										if temp[start] is True:
															in_prime.append(a  )
															for i in range(start * start					,						end + 1					,						a  ):
																				__A	:						Optional[int]       =				False
										start += 1
					prime += in_prime

					__A	:						Any       =				end + 1
					__A	:						Any       =				min(2 * end					,						a  )

					while low <= n:
										__A	:						List[Any]       =				[True] * (high - low + 1)
										for each in in_prime:
															__A	:						List[str]       =				math.floor(low / each  ) * each
															if t < low:
																				t += each

															for j in range(a					,						high + 1					,						a  ):
																				__A	:						Optional[int]       =				False

										for j in range(len(a  )  ):
															if temp[j] is True:
																				prime.append(j + low  )

										__A	:						Optional[int]       =				high + 1
										__A	:						Tuple       =				min(high + end					,						a  )

					return prime




print(sieve(10**6))




