






"""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        snake_case   (     A__			,A__			,A__		):
			# Initialise PyTorch model
			UpperCAmelCase_  :				List[Any]      =      MobileBertConfig.from_json_file(A__		)
			print(F"""Building PyTorch model from configuration: {config}"""		)
			UpperCAmelCase_  :				List[Any]      =      MobileBertForPreTraining(A__		)
			# Load weights from tf checkpoint
			UpperCAmelCase_  :				str      =      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__":
			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_     :   List[Any]       			=   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)











"""simple docstring"""
lowerCamelCase_       			=   {
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowerCamelCase_       			=   {value: key for key, value in encode_dict.items()}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      ""
			for letter in word.lower():
						if letter.isalpha() or letter == " ":
									encoded += encode_dict[letter]
						else:
									raise Exception("encode() accepts only letters of the alphabet and spaces"		)
			return encoded

def        snake_case   (     A__		):
			if set(A__		) - {"A", "B", " "} != set():
						raise Exception("decode() accepts only 'A', 'B' and spaces"		)
			UpperCAmelCase_  :				Dict      =      ""
			for word in coded.split():
						while len(A__		) != 0:
									decoded += decode_dict[word[:5]]
									UpperCAmelCase_  :				str      =      word[5:]
						decoded += " "
			return decoded.strip()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



