


'''simple docstring'''






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		__UpperCAmelCase       =							argparse.ArgumentParser(
		    description=(
		        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
		        ''' Distillation'''
		    )
		)
		parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
		parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
		parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
		parser.add_argument('''--vocab_transform''', action='''store_true''')
		__UpperCAmelCase       =							parser.parse_args()

		if args.model_type == "bert":
				__UpperCAmelCase       =							BertForMaskedLM.from_pretrained(args.model_name)
				__UpperCAmelCase       =							'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		__UpperCAmelCase       =							model.state_dict()
		__UpperCAmelCase       =							{}

		for w in ["word_embeddings", "position_embeddings"]:
				__UpperCAmelCase       =							state_dict[f"""{prefix}.embeddings.{w}.weight"""]
		for w in ["weight", "bias"]:
				__UpperCAmelCase       =							state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

		__UpperCAmelCase       =							0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				for w in ["weight", "bias"]:
						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
						]
						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
						]
						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
						]

						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
						]
						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
						]

						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
						]
						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
						]
						__UpperCAmelCase       =							state_dict[
						    f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
						]
				std_idx += 1

		__UpperCAmelCase       =							state_dict['''cls.predictions.decoder.weight''']
		__UpperCAmelCase       =							state_dict['''cls.predictions.bias''']
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						__UpperCAmelCase       =							state_dict[f"""cls.predictions.transform.dense.{w}"""]
						__UpperCAmelCase       =							state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

		print(f"""N layers selected for distillation: {std_idx}""")
		print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

		print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
		torch.save(compressed_sd, args.dump_checkpoint)



'''simple docstring'''






def  _snake_case       (					A = 10	)						->   str:
							if not isinstance(A			,   A	) or n < 0:
														raise ValueError('''Invalid input'''	)
							lowerCAmelCase__	       =     10**n
							lowerCAmelCase__	       =     28433 * (pow(2			,   7830457			,   A	)) + 1
							return str(number % modulus	)


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

		testmod()
		print(f"""{solution(10) = }""")