


'''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


__A		=['bert-base-uncased', 'bert-base-cased']
__A		='hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():


		class 						_snake_case   (       tf.keras.Model		):




							def __init__( self							,							_lowerCamelCase):
										super().__init__()
										UpperCAmelCase__					:   Optional[int]       				=       tokenizer
										UpperCAmelCase__					:   Optional[Any]       				=       AutoConfig.from_pretrained(_lowerCamelCase)
										UpperCAmelCase__					:   List[str]       				=       TFAutoModel.from_config(_lowerCamelCase)




							def 			snake_case__  ( self							,							_lowerCamelCase):
										UpperCAmelCase__					:   List[str]       				=       self.tokenizer(_lowerCamelCase)
										UpperCAmelCase__					:   List[str]       				=       self.bert(**_lowerCamelCase)
										return out["pooler_output"]


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




					def 			snake_case__  ( self):
								super().setUp()

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

								UpperCAmelCase__					:   Optional[Any]       				=       [
								    """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""",
								]
								UpperCAmelCase__					:   Tuple       				=       list(zip(self.test_sentences							,							self.test_sentences[::-1]))




					def 			snake_case__  ( self):
								for tokenizer, tf_tokenizer in zip(self.tokenizers							,							self.tf_tokenizers):
											for test_inputs in (self.test_sentences, self.paired_sentences):
														UpperCAmelCase__					:   List[Any]       				=       tokenizer(_lowerCamelCase							,							return_tensors="""tf"""							,							padding="""longest""")
														UpperCAmelCase__					:   Optional[Any]       				=       tf_tokenizer(_lowerCamelCase)

														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 			snake_case__  ( self):
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase__					:   Optional[Any]       				=       tf_tokenizer(self.paired_sentences)
											UpperCAmelCase__					:   Tuple       				=       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 			snake_case__  ( self):
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase__					:   List[str]       				=       tf.function(_lowerCamelCase)
											for test_inputs in (self.test_sentences, self.paired_sentences):
														UpperCAmelCase__					:   List[str]       				=       tf.constant(_lowerCamelCase)
														UpperCAmelCase__					:   Any       				=       compiled_tokenizer(_lowerCamelCase)
														UpperCAmelCase__					:   Dict       				=       tf_tokenizer(_lowerCamelCase)

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




					@slow
					def 			snake_case__  ( self):
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase__					:   Tuple       				=       ModelToSave(tokenizer=_lowerCamelCase)
											UpperCAmelCase__					:   Any       				=       tf.convert_to_tensor(self.test_sentences)
											UpperCAmelCase__					:   List[str]       				=       model(_lowerCamelCase)  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
														UpperCAmelCase__					:   int       				=       Path(_lowerCamelCase) / """saved.model"""
														model.save(_lowerCamelCase)
														UpperCAmelCase__					:   int       				=       tf.keras.models.load_model(_lowerCamelCase)
											UpperCAmelCase__					:   Optional[Any]       				=       loaded_model(_lowerCamelCase)
											# 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'''



def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       len(UpperCamelCase__   )
			UpperCAmelCase__					:   List[Any]       				=       sum(UpperCamelCase__   )

			UpperCAmelCase__					:   List[str]       				=       [[False for x in range(s + 1   )] for y in range(n + 1   )]

			for i in range(1	, n + 1   ):
						UpperCAmelCase__					:   int       				=       True

			for i in range(1	, s + 1   ):
						UpperCAmelCase__					:   Dict       				=       False

			for i in range(1	, n + 1   ):
						for j in range(1	, s + 1   ):
									UpperCAmelCase__					:   int       				=       dp[i][j - 1]

									if arr[i - 1] <= j:
												UpperCAmelCase__					:   str       				=       dp[i][j] or dp[i - 1][j - arr[i - 1]]

			for j in range(int(s / 2   )	, -1	, -1   ):
						if dp[n][j] is True:
									UpperCAmelCase__					:   int       				=       s - 2 * j
									break

			return diff