'''simple docstring'''






class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Optional[int]     )  ->			None:
        _A    		=  {}  # Mapping from char to TrieNode
        _A    		=  False
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			list[str]     )  ->			None:
        for word in words:
             self.insert(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			str     )  ->			None:
        _A    		=  self
        for char in word:
             if char not in curr.nodes:
                  _A    		=  TrieNode()
             _A    		=  curr.nodes[char]
        _A    		=  True
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			str     )  ->			bool:
        _A    		=  self
        for char in word:
             if char not in curr.nodes:
                  return False
             _A    		=  curr.nodes[char]
        return curr.is_leaf

   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			str     )  ->			None:

        def _delete(UpperCamelCase__      :			TrieNode, UpperCamelCase__      :			str, UpperCamelCase__      :			int     ) -> bool:
             if index == len(UpperCamelCase__     ):
                  # If word does not exist
                  if not curr.is_leaf:
                       return False
                  _A    		=  False
                  return len(curr.nodes     ) == 0
             _A    		=  word[index]
             _A    		=  curr.nodes.get(UpperCamelCase__     )
             # If char not in current trie node
             if not char_node:
                  return False
             # Flag to check if node can be deleted
             _A    		=  _delete(UpperCamelCase__, UpperCamelCase__, index + 1     )
             if delete_curr:
                  del curr.nodes[char]
                  return len(curr.nodes     ) == 0
             return delete_curr

        _delete(self, UpperCamelCase__, 0     )






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			TrieNode   ,		__snake_case							:			str				):
     if node.is_leaf:
          print(__snake_case   ,		end=' '				)

     for key, value in node.nodes.items():
          print_words(__snake_case   ,		word + key				)





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  'banana bananas bandana band apple all beast'.split()
     _A    		=  TrieNode()
     root.insert_many(__snake_case				)
     # print_words(root, "")
     assert all(root.find(__snake_case				) for word in words				)
     assert root.find('banana'				)
     assert not root.find('bandanas'				)
     assert not root.find('apps'				)
     assert root.find('apple'				)
     assert root.find('all'				)
     root.delete('all'				)
     assert not root.find('all'				)
     root.delete('banana'				)
     assert not root.find('banana'				)
     assert root.find('bananas'				)
     return True





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			bool				):
     print(str(__snake_case				)   ,		'works!' if passes else 'doesn\'t work :('				)





def  _SCREAMING_SNAKE_CASE		(      ):
     assert test_trie()





def  _SCREAMING_SNAKE_CASE		(      ):
     print_results('Testing trie functionality'   ,		test_trie()				)


if __name__ == "__main__":
   main()









import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
