


'''simple docstring'''


import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase   ):
		_UpperCAmelCase		:       int								=      AutoConfig.from_pretrained(__lowerCAmelCase      , **__lowerCAmelCase   )
		_UpperCAmelCase		:       str								=      AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase   )
		model.save_pretrained(__lowerCAmelCase   )
		AutoTokenizer.from_pretrained(__lowerCAmelCase   ).save_pretrained(__lowerCAmelCase   )
		return model


if __name__ == "__main__":
						fire.Fire(save_randomly_initialized_version)







'''simple docstring'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						lowerCamelCase__    	=     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')
						lowerCamelCase__    	=     parser.parse_args()

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

						lowerCamelCase__    	=     model.state_dict()
						lowerCamelCase__    	=     {}

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

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

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
																		]
												std_idx += 1

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



