


'''simple docstring'''





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  MvpTokenizer
  A__=  MvpTokenizerFast
  A__=  True
  A__=  filter_roberta_detectors






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




         """simple docstring"""







         super().setUp()
         UpperCAmelCase__										=     [
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "<unk>",
         ]
         UpperCAmelCase__										=     dict(zip(UpperCAmelCase__		,						range(len(UpperCAmelCase__ ) ) ) )
         UpperCAmelCase__										=     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         UpperCAmelCase__										=     {"unk_token": "<unk>"}

         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"] )
         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["merges_file"] )
         with open(self.vocab_file		,						"w"		,						encoding="utf-8" ) as fp:
                fp.write(json.dumps(UpperCAmelCase__ ) + "\n" )
         with open(self.merges_file		,						"w"		,						encoding="utf-8" ) as fp:
                fp.write("\n".join(UpperCAmelCase__ ) )






  def 			_UpperCAmelCase  (					self      :					Tuple		,						**_lowercase      :					List[str] ):




         """simple docstring"""







         kwargs.update(self.special_tokens_map )
         return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCAmelCase__ )






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						**_lowercase      :					int ):




         """simple docstring"""







         kwargs.update(self.special_tokens_map )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCAmelCase__ )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					Optional[Any] ):




         """simple docstring"""







         return "lower newer", "lower newer"






  @cached_property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         return MvpTokenizer.from_pretrained("RUCAIBox/mvp" )






  @cached_property
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp" )






  @require_torch
  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
         UpperCAmelCase__										=     [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase__										=     tokenizer(UpperCAmelCase__		,						max_length=len(UpperCAmelCase__ )		,						padding=UpperCAmelCase__		,						return_tensors="pt" )
                self.assertIsInstance(UpperCAmelCase__		,						UpperCAmelCase__ )

                self.assertEqual((2, 9)		,						batch.input_ids.shape )
                self.assertEqual((2, 9)		,						batch.attention_mask.shape )
                UpperCAmelCase__										=     batch.input_ids.tolist()[0]
                self.assertListEqual(UpperCAmelCase__		,						UpperCAmelCase__ )
                # Test that special tokens are reset






  @require_torch
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase__										=     tokenizer(UpperCAmelCase__		,						padding=UpperCAmelCase__		,						return_tensors="pt" )
                # check if input_ids are returned and no labels
                self.assertIn("input_ids"		,						UpperCAmelCase__ )
                self.assertIn("attention_mask"		,						UpperCAmelCase__ )
                self.assertNotIn("labels"		,						UpperCAmelCase__ )
                self.assertNotIn("decoder_attention_mask"		,						UpperCAmelCase__ )






  @require_torch
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     [
             "Summary of the text.",
             "Another summary.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase__										=     tokenizer(text_target=UpperCAmelCase__		,						max_length=32		,						padding="max_length"		,						return_tensors="pt" )
                self.assertEqual(32		,						targets["input_ids"].shape[1] )






  @require_torch
  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase__										=     tokenizer(
                    ["I am a small frog" * 10_24, "I am a small frog"]		,						padding=UpperCAmelCase__		,						truncation=UpperCAmelCase__		,						return_tensors="pt" )
                self.assertIsInstance(UpperCAmelCase__		,						UpperCAmelCase__ )
                self.assertEqual(batch.input_ids.shape		,						(2, 10_24) )






  @require_torch
  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     ["A long paragraph for summarization."]
         UpperCAmelCase__										=     [
             "Summary of the text.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase__										=     tokenizer(UpperCAmelCase__		,						text_target=UpperCAmelCase__		,						return_tensors="pt" )
                UpperCAmelCase__										=     inputs["input_ids"]
                UpperCAmelCase__										=     inputs["labels"]
                self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
                self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
                self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
                self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         pass






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




         """simple docstring"""







         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                       UpperCAmelCase__										=     self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,						**UpperCAmelCase__ )
                       UpperCAmelCase__										=     self.tokenizer_class.from_pretrained(UpperCAmelCase__		,						**UpperCAmelCase__ )
                       UpperCAmelCase__										=     "A, <mask> AllenNLP sentence."
                       UpperCAmelCase__										=     tokenizer_r.encode_plus(UpperCAmelCase__		,						add_special_tokens=UpperCAmelCase__		,						return_token_type_ids=UpperCAmelCase__ )
                       UpperCAmelCase__										=     tokenizer_p.encode_plus(UpperCAmelCase__		,						add_special_tokens=UpperCAmelCase__		,						return_token_type_ids=UpperCAmelCase__ )

                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r["token_type_ids"] )		,						sum(tokens_p["token_type_ids"] ) )

                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] )		,						sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] )		,						)

                       UpperCAmelCase__										=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
                       UpperCAmelCase__										=     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )

                       # Rust correctly handles the space before the mask while python doesnt
                       self.assertSequenceEqual(tokens_p["input_ids"]		,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
                       self.assertSequenceEqual(tokens_r["input_ids"]		,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

                       self.assertSequenceEqual(
                           UpperCAmelCase__		,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
                       self.assertSequenceEqual(
                           UpperCAmelCase__		,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )









from __future__ import annotations





def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     [True] * limit
       UpperCAmelCase__										=     False
       UpperCAmelCase__										=     False
       UpperCAmelCase__										=     True

       for i in range(3 ,						int(limit**0.5 + 1			) ,						2			):
              UpperCAmelCase__										=     i * 2
              while index < limit:
                     UpperCAmelCase__										=     False
                     UpperCAmelCase__										=     index + i

       UpperCAmelCase__										=     [2]

       for i in range(3 ,						__A ,						2			):
              if is_prime[i]:
                     primes.append(__A			)

       return primes





def __UpperCAmelCase       (		__A = 1_0_0_0_0_0_0			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     prime_sieve(__A			)
       UpperCAmelCase__										=     0
       UpperCAmelCase__										=     0

       for i in range(len(__A			)			):
              for j in range(i + length ,						len(__A			)			):
                     UpperCAmelCase__										=     sum(primes[i:j]			)
                     if sol >= ceiling:
                            break

                     if sol in primes:
                            UpperCAmelCase__										=     j - i
                            UpperCAmelCase__										=     sol

       return largest


if __name__ == "__main__":
    print(f"{solution() = }")





