"""simple docstring"""




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							A        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							A        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							A        =   """UNwant\u00E9d,running"""
							A        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							A        =   self.tokenizer_class(self.vocab_file     )

							A        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(snake_case__  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[7, 4, 5, 10, 8, 9]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass





"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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


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




