


'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__    	=     logging.get_logger(__name__)

lowerCamelCase__    	=     {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Union[str, Any]          =     "biogpt"




			def __init__(		self  :     Dict	,  lowerCamelCase__  :     Union[str, Any]=4_23_84	,  lowerCamelCase__  :     Any=10_24	,  lowerCamelCase__  :     List[str]=24	,  lowerCamelCase__  :     Dict=16	,  lowerCamelCase__  :     Optional[Any]=40_96	,  lowerCamelCase__  :     Optional[Any]="gelu"	,  lowerCamelCase__  :     Any=0.1	,  lowerCamelCase__  :     Union[str, Any]=0.1	,  lowerCamelCase__  :     List[str]=10_24	,  lowerCamelCase__  :     List[str]=0.0_2	,  lowerCamelCase__  :     Any=1E-12	,  lowerCamelCase__  :     Tuple=True	,  lowerCamelCase__  :     Optional[Any]=True	,  lowerCamelCase__  :     Union[str, Any]=0.0	,  lowerCamelCase__  :     Optional[Any]=0.0	,  lowerCamelCase__  :     str=1	,  lowerCamelCase__  :     Any=0	,  lowerCamelCase__  :     List[str]=2	,  **lowerCamelCase__  :     List[Any]	,  )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      vocab_size
					_UpperCAmelCase		:       Optional[int]								=      max_position_embeddings
					_UpperCAmelCase		:       List[str]								=      hidden_size
					_UpperCAmelCase		:       List[str]								=      num_hidden_layers
					_UpperCAmelCase		:       List[Any]								=      num_attention_heads
					_UpperCAmelCase		:       int								=      intermediate_size
					_UpperCAmelCase		:       Tuple								=      hidden_act
					_UpperCAmelCase		:       str								=      hidden_dropout_prob
					_UpperCAmelCase		:       Tuple								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       List[Any]								=      initializer_range
					_UpperCAmelCase		:       Optional[int]								=      layer_norm_eps
					_UpperCAmelCase		:       List[Any]								=      scale_embedding
					_UpperCAmelCase		:       List[str]								=      use_cache
					_UpperCAmelCase		:       Optional[int]								=      layerdrop
					_UpperCAmelCase		:       Any								=      activation_dropout
					super().__init__(pad_token_id=lowerCamelCase__	,  bos_token_id=lowerCamelCase__	,  eos_token_id=lowerCamelCase__	,  **lowerCamelCase__ )







'''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)



