

'''simple docstring'''


import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    BlenderbotSmallTokenizer
						__UpperCAmelCase			     =    False







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								super().setUp()

								snake_case__						:       Optional[Any]       						=  ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								snake_case__						:       str       						=  dict(zip(snake_case_			,			range(len(snake_case_  )  )  )  )

								snake_case__						:       List[str]       						=  ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								snake_case__						:       str       						=  {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								snake_case__						:       str       						=  os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']  )
								snake_case__						:       Optional[Any]       						=  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(snake_case_  ) + '''\n'''  )
								with open(self.merges_file			,			'''w'''			,			encoding='''utf-8'''  ) as fp:
										fp.write('''\n'''.join(snake_case_  )  )







						def       __magic_name__							(							self					:      Optional[int]			,			**snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,			**snake_case_  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       str       						=  '''adapt act apte'''
								snake_case__						:       str       						=  '''adapt act apte'''
								return input_text, output_text







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  BlenderbotSmallTokenizer(self.vocab_file			,			self.merges_file			,			**self.special_tokens_map  )
								snake_case__						:       Any       						=  '''adapt act apte'''
								snake_case__						:       int       						=  ['''adapt''', '''act''', '''ap@@''', '''te''']
								snake_case__						:       int       						=  tokenizer.tokenize(snake_case_  )
								self.assertListEqual(snake_case_			,			snake_case_  )

								snake_case__						:       List[str]       						=  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								snake_case__						:       List[Any]       						=  [0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_  )			,			snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1_3_8_4]
								snake_case__						:       Dict       						=  '''I am a small frog.'''
								snake_case__						:       int       						=  tok([src_text]			,			padding=snake_case_			,			truncation=snake_case_  )['''input_ids''']
								snake_case__						:       Any       						=  tok.batch_decode(snake_case_			,			skip_special_tokens=snake_case_			,			clean_up_tokenization_spaces=snake_case_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								snake_case__						:       Dict       						=  '''I am a small frog .'''
								snake_case__						:       Any       						=  '''.'''
								snake_case__						:       Union[str, Any]       						=  tok(snake_case_  )['''input_ids''']
								snake_case__						:       Optional[int]       						=  tok(snake_case_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]






'''simple docstring'''


from collections.abc import Sequence




def 	_a       (				__lowerCAmelCase		:      Sequence[int] | None = None     ):

		"""simple docstring"""

		if nums is None or not nums:
				raise ValueError('''Input sequence should not be empty'''     )

		snake_case__						:       Optional[Any]       						=  nums[0]
		for i in range(1   ,							len(__lowerCAmelCase     )     ):
				snake_case__						:       Tuple       						=  nums[i]
				snake_case__						:       Optional[int]       						=  max(__lowerCAmelCase   ,							ans + num   ,							__lowerCAmelCase     )

		return ans


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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



