





from __future__ import annotations







class     lowerCamelCase:
					'''simple docstring'''


					def __init__(						self							,    snake_case_       ):
									_A    =       value
									_A    =       None
									_A    =       None







class     lowerCamelCase:
					'''simple docstring'''


					__magic_name__        = 42




					def __init__(						self       ):
									_A    =       0




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_       ):
									if node is None:
													return

									if path_sum == self.target:
													self.paths += 1

									if node.left:
													self.depth_first_search(node.left							,    path_sum + node.left.value       )
									if node.right:
													self.depth_first_search(node.right							,    path_sum + node.right.value       )




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_ = None       ):
									if node is None:
													return 0
									if target is not None:
													_A    =       target

									self.depth_first_search(_lowerCAmelCase							,    node.value       )
									self.path_sum(node.left       )
									self.path_sum(node.right       )

									return self.paths


if __name__ == "__main__":
	import doctest

	doctest.testmod()












from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     __magic_name__        = SqueezeBertTokenizer
     __magic_name__        = SqueezeBertTokenizerFast
     __magic_name__        = True




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'       )

         _A    =       tokenizer.encode('sequence builders'							,    add_special_tokens=snake_case_       )
         _A    =       tokenizer.encode('multi-sequence build'							,    add_special_tokens=snake_case_       )

         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_       )
         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_							,    snake_case_       )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]





