


'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__    	=     logging.get_logger(__name__)

lowerCamelCase__    	=     {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}






class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):
			lowerCAmelCase :	str          =     "convnextv2"




			def __init__(		self  :     List[Any]	,  lowerCamelCase__  :     List[Any]=3	,  lowerCamelCase__  :     Dict=4	,  lowerCamelCase__  :     Any=4	,  lowerCamelCase__  :     Any=None	,  lowerCamelCase__  :     List[Any]=None	,  lowerCamelCase__  :     List[str]="gelu"	,  lowerCamelCase__  :     Optional[int]=0.0_2	,  lowerCamelCase__  :     Tuple=1E-12	,  lowerCamelCase__  :     Any=0.0	,  lowerCamelCase__  :     str=2_24	,  lowerCamelCase__  :     Optional[Any]=None	,  lowerCamelCase__  :     Any=None	,  **lowerCamelCase__  :     List[Any]	,  )   ->Union[str, Any]:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__ )

					_UpperCAmelCase		:       int								=      num_channels
					_UpperCAmelCase		:       Union[str, Any]								=      patch_size
					_UpperCAmelCase		:       List[str]								=      num_stages
					_UpperCAmelCase		:       Any								=      [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
					_UpperCAmelCase		:       Any								=      [3, 3, 9, 3] if depths is None else depths
					_UpperCAmelCase		:       Tuple								=      hidden_act
					_UpperCAmelCase		:       str								=      initializer_range
					_UpperCAmelCase		:       Optional[Any]								=      layer_norm_eps
					_UpperCAmelCase		:       List[Any]								=      drop_path_rate
					_UpperCAmelCase		:       str								=      image_size
					_UpperCAmelCase		:       int								=      ["stem"] + [F"""stage{idx}""" for idx in range(1	,  len(self.depths ) + 1 )]
					_UpperCAmelCase					,    _UpperCAmelCase		:       List[Any]								=      get_aligned_output_features_output_indices(
					    out_features=lowerCamelCase__	,  out_indices=lowerCamelCase__	,  stage_names=self.stage_names )







'''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)



