

'''simple docstring'''


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_tensorflow_text_available():
					from transformers.models.bert import TFBertTokenizer


lowerCAmelCase__						:       List[Any]										=						["""bert-base-uncased""", """bert-base-cased"""]
lowerCAmelCase__						:       int										=						"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():




					class     a			(				tf.keras.Model       ):






											"""simple docstring"""




											def __init__(							self					:      Optional[Any]			,			snake_case_					:      Any  ):



													'''simple docstring'''



													super().__init__()
													snake_case__						:       Optional[Any]       						=  tokenizer
													snake_case__						:       str       						=  AutoConfig.from_pretrained(snake_case_  )
													snake_case__						:       int       						=  TFAutoModel.from_config(snake_case_  )







											def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      int  ):



													'''simple docstring'''



													snake_case__						:       List[Any]       						=  self.tokenizer(snake_case_  )
													snake_case__						:       Tuple       						=  self.bert(**snake_case_  )
													return out["pooler_output"]




@require_tf
@require_tensorflow_text
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								super().setUp()

								snake_case__						:       Any       						=  [
								    BertTokenizer.from_pretrained(snake_case_  ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								snake_case__						:       List[str]       						=  [TFBertTokenizer.from_pretrained(snake_case_  ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(snake_case_			,			use_fast_bert_tokenizer=snake_case_  )
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers  ) == len(self.tf_tokenizers  )

								snake_case__						:       List[str]       						=  [
								    '''This is a straightforward English test sentence.''',
								    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
								    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
								    '''And some much more rare Chinese: 齉 堃 齉堃''',
								    '''Je vais aussi écrire en français pour tester les accents''',
								    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
								]
								snake_case__						:       Optional[Any]       						=  list(zip(self.test_sentences			,			self.test_sentences[::-1]  )  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								for tokenizer, tf_tokenizer in zip(self.tokenizers			,			self.tf_tokenizers  ):
										for test_inputs in (self.test_sentences, self.paired_sentences):
												snake_case__						:       Union[str, Any]       						=  tokenizer(snake_case_			,			return_tensors='''tf'''			,			padding='''longest'''  )
												snake_case__						:       Optional[Any]       						=  tf_tokenizer(snake_case_  )

												for key in python_outputs.keys():
														self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape  )  )
														self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,			tf.intaa  ) == tf_outputs[key]  )  )







						@slow
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								for tf_tokenizer in self.tf_tokenizers:
										snake_case__						:       Optional[Any]       						=  tf_tokenizer(self.paired_sentences  )
										snake_case__						:       Any       						=  tf_tokenizer(
										    text=[sentence[0] for sentence in self.paired_sentences]			,			text_pair=[sentence[1] for sentence in self.paired_sentences]			,			)
										for key in merged_outputs.keys():
												self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,			tf.intaa  ) == separated_outputs[key]  )  )







						@slow
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								for tf_tokenizer in self.tf_tokenizers:
										snake_case__						:       Optional[int]       						=  tf.function(snake_case_  )
										for test_inputs in (self.test_sentences, self.paired_sentences):
												snake_case__						:       Dict       						=  tf.constant(snake_case_  )
												snake_case__						:       Union[str, Any]       						=  compiled_tokenizer(snake_case_  )
												snake_case__						:       int       						=  tf_tokenizer(snake_case_  )

												for key in eager_outputs.keys():
														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]  )  )







						@slow
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								for tf_tokenizer in self.tf_tokenizers:
										snake_case__						:       str       						=  ModelToSave(tokenizer=snake_case_  )
										snake_case__						:       Union[str, Any]       						=  tf.convert_to_tensor(self.test_sentences  )
										snake_case__						:       Union[str, Any]       						=  model(snake_case_  )  # Build model with some sample inputs
										with TemporaryDirectory() as tempdir:
												snake_case__						:       Dict       						=  Path(snake_case_  ) / '''saved.model'''
												model.save(snake_case_  )
												snake_case__						:       Tuple       						=  tf.keras.models.load_model(snake_case_  )
										snake_case__						:       Optional[Any]       						=  loaded_model(snake_case_  )
										# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
										self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output  )  )			,			1e-5  )






'''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))



