






import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





     """simple docstring"""






     a_     =		LxmertTokenizer
     a_     =		LxmertTokenizerFast
     a_     =		True
     a_     =		True



     def 	_lowercase					( self					:  Any	):
      super().setUp()

      snake_case__			:				Any       =  [
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      snake_case__			:				List[str]       =  os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["vocab_file"]	)
      with open(self.vocab_file							,		"w"							,		encoding="utf-8"	) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)



     def 	_lowercase					( self					:  int							,		__A					:  Any	):
      snake_case__			:				int       =  "UNwant\u00E9d,running"
      snake_case__			:				Optional[int]       =  "unwanted, running"
      return input_text, output_text



     def 	_lowercase					( self					:  List[Any]	):
      snake_case__			:				Union[str, Any]       =  self.tokenizer_class(self.vocab_file	)

      snake_case__			:				str       =  tokenizer.tokenize("UNwant\u00E9d,running"	)
      self.assertListEqual(__A							,		["un", "##want", "##ed", ",", "runn", "##ing"]	)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)							,		[7, 4, 5, 1_0, 8, 9]	)






     def 	_lowercase					( self					:  Optional[int]	):
      if not self.test_rust_tokenizer:
       return

      snake_case__			:				Union[str, Any]       =  self.get_tokenizer()
      snake_case__			:				int       =  self.get_rust_tokenizer()

      snake_case__			:				Tuple       =  "I was born in 92000, and this is falsé."

      snake_case__			:				Optional[int]       =  tokenizer.tokenize(__A	)
      snake_case__			:				Optional[int]       =  rust_tokenizer.tokenize(__A	)
      self.assertListEqual(__A							,		__A	)

      snake_case__			:				Optional[Any]       =  tokenizer.encode(__A							,		add_special_tokens=__A	)
      snake_case__			:				List[Any]       =  rust_tokenizer.encode(__A							,		add_special_tokens=__A	)
      self.assertListEqual(__A							,		__A	)

      snake_case__			:				Optional[int]       =  self.get_rust_tokenizer()
      snake_case__			:				List[Any]       =  tokenizer.encode(__A	)
      snake_case__			:				List[str]       =  rust_tokenizer.encode(__A	)
      self.assertListEqual(__A							,		__A	)











import argparse

import torch

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


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				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."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



