import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase					      =							get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
     from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase					      =							2_5_0_0_0_4
UpperCAmelCase					      =							2_5_0_0_2_0



@require_sentencepiece
@require_tokenizers
class        snake_case__							(					UpperCamelCase__       ,    unittest.TestCase				):
   _SCREAMING_SNAKE_CASE						:			Tuple         =   MBartTokenizer
   _SCREAMING_SNAKE_CASE						:			List[Any]         =   MBartTokenizerFast
   _SCREAMING_SNAKE_CASE						:			Optional[int]         =   True
   _SCREAMING_SNAKE_CASE						:			List[str]         =   True



   def        UpperCAmelCase__							(					self : Union[str, Any] )				->  Optional[int]:




         '''simple docstring'''




         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case_  :      List[str]  	=							MBartTokenizer(__snake_case      ,						keep_accents=__snake_case )
         tokenizer.save_pretrained(self.tmpdirname )



   def        UpperCAmelCase__							(					self : str )				->  Optional[Any]:




         '''simple docstring'''




         snake_case_  :      Any  	=							MBartTokenizer(__snake_case      ,						keep_accents=__snake_case )

         snake_case_  :      int  	=							tokenizer.tokenize("This is a test" )
         self.assertListEqual(__snake_case      ,						["▁This", "▁is", "▁a", "▁t", "est"] )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__snake_case )      ,						[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,						)

         snake_case_  :      Optional[int]  	=							tokenizer.tokenize("I was born in 92000, and this is falsé." )
         self.assertListEqual(
             __snake_case      ,						[
                 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",
                 ".",
             ]      ,						)
         snake_case_  :      Optional[Any]  	=							tokenizer.convert_tokens_to_ids(__snake_case )
         self.assertListEqual(
             __snake_case      ,						[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ]      ,						)

         snake_case_  :      Any  	=							tokenizer.convert_ids_to_tokens(__snake_case )
         self.assertListEqual(
             __snake_case      ,						[
                 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        UpperCAmelCase__							(					self : Tuple )				->  str:




         '''simple docstring'''




         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

         snake_case_  :      Tuple  	=							(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                     snake_case_  :      List[Any]  	=							self.rust_tokenizer_class.from_pretrained(__snake_case      ,						**__snake_case )
                     snake_case_  :      List[str]  	=							self.tokenizer_class.from_pretrained(__snake_case      ,						**__snake_case )

                     snake_case_  :      Optional[Any]  	=							tempfile.mkdtemp()

                     snake_case_  :      Dict  	=							tokenizer_r.save_pretrained(__snake_case )
                     snake_case_  :      Dict  	=							tokenizer_p.save_pretrained(__snake_case )

                     # 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 ) )
                     snake_case_  :      int  	=							tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
                     self.assertSequenceEqual(__snake_case      ,						__snake_case )

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

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

                     shutil.rmtree(__snake_case )

                     # Save tokenizer rust, legacy_format=True
                     snake_case_  :      List[str]  	=							tempfile.mkdtemp()

                     snake_case_  :      Optional[int]  	=							tokenizer_r.save_pretrained(__snake_case      ,						legacy_format=__snake_case )
                     snake_case_  :      Dict  	=							tokenizer_p.save_pretrained(__snake_case )

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

                     # Checks everything loads correctly in the same way
                     snake_case_  :      Any  	=							tokenizer_r.from_pretrained(__snake_case )
                     snake_case_  :      int  	=							tokenizer_p.from_pretrained(__snake_case )

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

                     shutil.rmtree(__snake_case )

                     # Save tokenizer rust, legacy_format=False
                     snake_case_  :      List[Any]  	=							tempfile.mkdtemp()

                     snake_case_  :      Dict  	=							tokenizer_r.save_pretrained(__snake_case      ,						legacy_format=__snake_case )
                     snake_case_  :      List[str]  	=							tokenizer_p.save_pretrained(__snake_case )

                     # 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
                     snake_case_  :      str  	=							tokenizer_r.from_pretrained(__snake_case )
                     snake_case_  :      Optional[int]  	=							tokenizer_p.from_pretrained(__snake_case )

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

                     shutil.rmtree(__snake_case )



@require_torch
@require_sentencepiece
@require_tokenizers
class        snake_case__							(					unittest.TestCase				):
   _SCREAMING_SNAKE_CASE						:			Optional[int]         =   "facebook/mbart-large-en-ro"
   _SCREAMING_SNAKE_CASE						:			Any         =   [
       " UN Chief Says There Is No Military Solution in Syria",
       " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
   ]
   _SCREAMING_SNAKE_CASE						:			Any         =   [
       "Şeful ONU declară că nu există o soluţie militară în Siria",
       "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
       " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
       " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
   ]
   _SCREAMING_SNAKE_CASE						:			Dict         =   [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]



   @classmethod
   def        UpperCAmelCase__							(					cls : str )				->  int:




         '''simple docstring'''




         snake_case_  :      MBartTokenizer  	=							MBartTokenizer.from_pretrained(
             cls.checkpoint_name      ,						src_lang="en_XX"      ,						tgt_lang="ro_RO" )
         snake_case_  :      Any  	=							1
         return cls



   def        UpperCAmelCase__							(					self : Any )				->  int:




         '''simple docstring'''




         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]      ,						25_00_01 )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]      ,						25_00_04 )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]      ,						25_00_20 )



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  Union[str, Any]:




         '''simple docstring'''




         snake_case_  :      Any  	=							self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens      ,						__snake_case )



   def        UpperCAmelCase__							(					self : List[Any] )				->  Optional[int]:




         '''simple docstring'''




         self.assertIn(__snake_case      ,						self.tokenizer.all_special_ids )
         snake_case_  :      Union[str, Any]  	=							[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
         snake_case_  :      Optional[int]  	=							self.tokenizer.decode(__snake_case      ,						skip_special_tokens=__snake_case )
         snake_case_  :      List[str]  	=							self.tokenizer.decode(generated_ids[1:]      ,						skip_special_tokens=__snake_case )
         self.assertEqual(__snake_case      ,						__snake_case )
         self.assertNotIn(self.tokenizer.eos_token      ,						__snake_case )



   def        UpperCAmelCase__							(					self : Tuple )				->  Union[str, Any]:




         '''simple docstring'''




         snake_case_  :      str  	=							["""this is gunna be a long sentence """ * 20]
         assert isinstance(src_text[0]      ,						__snake_case )
         snake_case_  :      Dict  	=							10
         snake_case_  :      Optional[Any]  	=							self.tokenizer(__snake_case      ,						max_length=__snake_case      ,						truncation=__snake_case ).input_ids[0]
         self.assertEqual(ids[-2]      ,						2 )
         self.assertEqual(ids[-1]      ,						__snake_case )
         self.assertEqual(len(__snake_case )      ,						__snake_case )



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  int:




         '''simple docstring'''




         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] )      ,						[25_00_26, 25_00_01] )



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  List[Any]:




         '''simple docstring'''




         snake_case_  :      Optional[int]  	=							tempfile.mkdtemp()
         snake_case_  :      Dict  	=							self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__snake_case )
         snake_case_  :      Dict  	=							MBartTokenizer.from_pretrained(__snake_case )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,						__snake_case )



   @require_torch
   def        UpperCAmelCase__							(					self : List[Any] )				->  Optional[int]:




         '''simple docstring'''




         snake_case_  :      Any  	=							self.tokenizer(self.src_text      ,						text_target=self.tgt_text      ,						padding=__snake_case      ,						return_tensors="pt" )
         snake_case_  :      str  	=							shift_tokens_right(batch["labels"]      ,						self.tokenizer.pad_token_id )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
         assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
         assert batch.decoder_input_ids[1][-1] == 2
         assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



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




         '''simple docstring'''




         snake_case_  :      List[Any]  	=							self.tokenizer(
             self.src_text      ,						text_target=self.tgt_text      ,						padding=__snake_case      ,						truncation=__snake_case      ,						max_length=len(self.expected_src_tokens )      ,						return_tensors="pt"      ,						)

         snake_case_  :      Any  	=							shift_tokens_right(batch["labels"]      ,						self.tokenizer.pad_token_id )

         self.assertIsInstance(__snake_case      ,						__snake_case )

         self.assertEqual((2, 14)      ,						batch.input_ids.shape )
         self.assertEqual((2, 14)      ,						batch.attention_mask.shape )
         snake_case_  :      int  	=							batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens      ,						__snake_case )
         self.assertEqual(2      ,						batch.decoder_input_ids[0, -1] )  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens      ,						[] )
         self.assertEqual(self.tokenizer.suffix_tokens      ,						[self.tokenizer.eos_token_id, EN_CODE] )



   def        UpperCAmelCase__							(					self : List[str] )				->  Dict:




         '''simple docstring'''




         snake_case_  :      Tuple  	=							self.tokenizer(self.src_text      ,						padding=__snake_case      ,						truncation=__snake_case      ,						max_length=3      ,						return_tensors="pt" )
         snake_case_  :      Tuple  	=							self.tokenizer(
             text_target=self.tgt_text      ,						padding=__snake_case      ,						truncation=__snake_case      ,						max_length=10      ,						return_tensors="pt" )
         snake_case_  :      List[Any]  	=							targets["""input_ids"""]
         snake_case_  :      List[str]  	=							shift_tokens_right(__snake_case      ,						self.tokenizer.pad_token_id )

         self.assertEqual(batch.input_ids.shape[1]      ,						3 )
         self.assertEqual(batch.decoder_input_ids.shape[1]      ,						10 )



   @require_torch
   def        UpperCAmelCase__							(					self : str )				->  Dict:




         '''simple docstring'''




         snake_case_  :      Union[str, Any]  	=							self.tokenizer._build_translation_inputs(
             "A test"      ,						return_tensors="pt"      ,						src_lang="en_XX"      ,						tgt_lang="ar_AR" )

         self.assertEqual(
             nested_simplify(__snake_case )      ,						{
                 # A, test, EOS, en_XX
                 "input_ids": [[62, 30_34, 2, 25_00_04]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # ar_AR
                 "forced_bos_token_id": 25_00_01,
             }      ,						)









from collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




