


import argparse

import torch

from transformers import BertForMaskedLM


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

	if args.model_type == "bert":
		a : Union[str, Any]		=				BertForMaskedLM.from_pretrained(args.model_name)
		a : str		=				'''bert'''
	else:
		raise ValueError('''args.model_type should be "bert".''')

	a : Union[str, Any]		=				model.state_dict()
	a : str		=				{}

	for w in ["word_embeddings", "position_embeddings"]:
		a : Union[str, Any]		=				state_dict[f'''{prefix}.embeddings.{w}.weight''']
	for w in ["weight", "bias"]:
		a : Any		=				state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

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

			a : List[str]		=				state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
			]
			a : Optional[int]		=				state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
			]

			a : Union[str, Any]		=				state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
			]
			a : List[str]		=				state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
			]
			a : str		=				state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
			]
		std_idx += 1

	a : str		=				state_dict['''cls.predictions.decoder.weight''']
	a : List[Any]		=				state_dict['''cls.predictions.bias''']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			a : Dict		=				state_dict[f'''cls.predictions.transform.dense.{w}''']
			a : Optional[int]		=				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)






from typing import List

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


a : Any		=				logging.get_logger(__name__)

a : int		=				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"efficientformer"






					def __init__(				self ,       snake_case_ = [3, 2, 6, 4] ,       snake_case_ = [4_8, 9_6, 2_2_4, 4_4_8] ,       snake_case_ = [True, True, True, True] ,       snake_case_ = 4_4_8 ,       snake_case_ = 3_2 ,       snake_case_ = 4 ,       snake_case_ = 7 ,       snake_case_ = 5 ,       snake_case_ = 8 ,       snake_case_ = 4 ,       snake_case_ = 0.0 ,       snake_case_ = 1_6 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 3 ,       snake_case_ = 2 ,       snake_case_ = 1 ,       snake_case_ = 0.0 ,       snake_case_ = 1 ,       snake_case_ = True ,       snake_case_ = True ,       snake_case_ = 1e-5 ,       snake_case_ = "gelu" ,       snake_case_ = 0.0_2 ,       snake_case_ = 1e-1_2 ,       snake_case_ = 2_2_4 ,       snake_case_ = 1e-0_5 ,       **snake_case_ ,       )   ->			None:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								__lowercase 					=   hidden_act
								__lowercase 					=   hidden_dropout_prob
								__lowercase 					=   hidden_sizes
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   initializer_range
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   patch_size
								__lowercase 					=   num_channels
								__lowercase 					=   depths
								__lowercase 					=   mlp_expansion_ratio
								__lowercase 					=   downsamples
								__lowercase 					=   dim
								__lowercase 					=   key_dim
								__lowercase 					=   attention_ratio
								__lowercase 					=   resolution
								__lowercase 					=   pool_size
								__lowercase 					=   downsample_patch_size
								__lowercase 					=   downsample_stride
								__lowercase 					=   downsample_pad
								__lowercase 					=   drop_path_rate
								__lowercase 					=   num_metaad_blocks
								__lowercase 					=   distillation
								__lowercase 					=   use_layer_scale
								__lowercase 					=   layer_scale_init_value
								__lowercase 					=   image_size
								__lowercase 					=   batch_norm_eps


