





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
							UpperCAmelCase__						:			Any									=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__						:			Any									=parser.parse_args()

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

							UpperCAmelCase__						:			int									=model.state_dict()
							UpperCAmelCase__						:			Optional[Any]									={}

							for w in ["word_embeddings", "position_embeddings"]:
														UpperCAmelCase__						:			int									=state_dict[F"{prefix}.embeddings.{w}.weight"]
							for w in ["weight", "bias"]:
														UpperCAmelCase__						:			Optional[int]									=state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

							UpperCAmelCase__						:			str									=0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														for w in ["weight", "bias"]:
																					UpperCAmelCase__						:			Dict									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
																					]
																					UpperCAmelCase__						:			Tuple									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
																					]
																					UpperCAmelCase__						:			Any									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
																					]

																					UpperCAmelCase__						:			Any									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
																					]
																					UpperCAmelCase__						:			Dict									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
																					]

																					UpperCAmelCase__						:			Dict									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
																					]
																					UpperCAmelCase__						:			Any									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
																					]
																					UpperCAmelCase__						:			int									=state_dict[
																					    F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
																					]
														std_idx += 1

							UpperCAmelCase__						:			Union[str, Any]									=state_dict['''cls.predictions.decoder.weight''']
							UpperCAmelCase__						:			List[str]									=state_dict['''cls.predictions.bias''']
							if args.vocab_transform:
														for w in ["weight", "bias"]:
																					UpperCAmelCase__						:			List[str]									=state_dict[F"cls.predictions.transform.dense.{w}"]
																					UpperCAmelCase__						:			Union[str, Any]									=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)












def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				lowerCamelCase	 =[0 for i in range(r + 1				)]
				# nc0 = 1
				lowerCamelCase	 =1
				for i in range(1			,      n + 1				):
								# to compute current row from previous row.
								lowerCamelCase	 =min(_UpperCAmelCase			,      _UpperCAmelCase				)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]




print(binomial_coefficient(n=10, r=5))





