

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 				UpperCAmelCase_		(			tf.keras.Model   ):
										"""simple docstring"""







										def __init__(			self       ,							_a       )    ->    Dict:
															super().__init__()
															_a	:    str         =		tokenizer
															_a	:    str         =		AutoConfig.from_pretrained(_a       )
															_a	:    Any         =		TFAutoModel.from_config(_a       )




										def 				__lowercase				(			self       ,							_a       )    ->    List[str]:
															_a	:    str         =		self.tokenizer(_a       )
															_a	:    int         =		self.bert(**_a       )
															return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    int:
												super().setUp()

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

												_a	:    Dict         =		[
												    '''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''',
												]
												_a	:    List[str]         =		list(zip(self.test_sentences       ,							self.test_sentences[::-1]       )       )


							def 				__lowercase				(			self       )    ->    str:
												for tokenizer, tf_tokenizer in zip(self.tokenizers       ,							self.tf_tokenizers       ):
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																						_a	:    str         =		tokenizer(_a       ,							return_tensors='''tf'''       ,							padding='''longest'''       )
																						_a	:    List[str]         =		tf_tokenizer(_a       )

																						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 				__lowercase				(			self       )    ->    int:
												for tf_tokenizer in self.tf_tokenizers:
																	_a	:    Optional[int]         =		tf_tokenizer(self.paired_sentences       )
																	_a	:    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 				__lowercase				(			self       )    ->    Optional[int]:
												for tf_tokenizer in self.tf_tokenizers:
																	_a	:    Optional[Any]         =		tf.function(_a       )
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																						_a	:    Optional[int]         =		tf.constant(_a       )
																						_a	:    str         =		compiled_tokenizer(_a       )
																						_a	:    Dict         =		tf_tokenizer(_a       )

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




							@slow
							def 				__lowercase				(			self       )    ->    List[str]:
												for tf_tokenizer in self.tf_tokenizers:
																	_a	:    Dict         =		ModelToSave(tokenizer=_a       )
																	_a	:    Optional[int]         =		tf.convert_to_tensor(self.test_sentences       )
																	_a	:    List[str]         =		model(_a       )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																						_a	:    str         =		Path(_a       ) / '''saved.model'''
																						model.save(_a       )
																						_a	:    Dict         =		tf.keras.models.load_model(_a       )
																	_a	:    str         =		loaded_model(_a       )
																	# 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       )






def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations(__a      :			int	) -> int:
										if target < 0:
															return 0
										if target == 0:
															return 1
										return sum(count_of_possible_combinations(target - item	) for item in array	)

					return count_of_possible_combinations(__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations_with_dp_array(
					    __a      :			int		,__a      :			list[int]	) -> int:
										if target < 0:
															return 0
										if target == 0:
															return 1
										if dp_array[target] != -1:
															return dp_array[target]
										_a	:    Union[str, Any]         =		sum(
										    count_of_possible_combinations_with_dp_array(target - item		,__a	)
										    for item in array	)
										_a	:    Optional[int]         =		answer
										return answer

					_a	:    int         =		[-1] * (target + 1)
					return count_of_possible_combinations_with_dp_array(__a		,__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					_a	:    str         =		[0] * (target + 1)
					_a	:    Optional[Any]         =		1

					for i in range(1		,target + 1	):
										for j in range(__a	):
															if i - array[j] >= 0:
																				dp_array[i] += dp_array[i - array[j]]

					return dp_array[target]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			a__				=		3
			a__				=		5
			a__				=		[1, 2, 5]
			print(combination_sum_iv(n, array, target))



