


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						snake_case							: List[str]      =   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''')
						snake_case							: Union[str, Any]      =   parser.parse_args()

						if args.model_type == "bert":
												snake_case							: Any      =   BertForMaskedLM.from_pretrained(args.model_name)
												snake_case							: Optional[Any]      =   '''bert'''
						else:
												raise ValueError('''args.model_type should be "bert".''')

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

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

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

																		snake_case							: Optional[int]      =   state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
																		]
																		snake_case							: Union[str, Any]      =   state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
																		]

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

						snake_case							: int      =   state_dict['''cls.predictions.decoder.weight''']
						snake_case							: int      =   state_dict['''cls.predictions.bias''']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		snake_case							: Optional[int]      =   state_dict[f"""cls.predictions.transform.dense.{w}"""]
																		snake_case							: Optional[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     (				__lowerCAmelCase							:       int = 2_0_0     ):
							a__    =  [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
							a__    =  [0] * (pence + 1)
							a__    =  1  # base case: 1 way to make 0 pence

							for coin in coins:
														for i in range(__lowerCAmelCase      ,					pence + 1      ,					1     ):
																					number_of_ways[i] += number_of_ways[i - coin]
							return number_of_ways[pence]


if __name__ == "__main__":
						assert solution(2_00) == 7_36_82




