


'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__    	=     logging.get_logger(__name__)

lowerCamelCase__    	=     {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	List[Any]          =     "data2vec-vision"




			def __init__(		self  :     Any	,  lowerCamelCase__  :     List[str]=7_68	,  lowerCamelCase__  :     Optional[Any]=12	,  lowerCamelCase__  :     List[str]=12	,  lowerCamelCase__  :     Optional[Any]=30_72	,  lowerCamelCase__  :     Dict="gelu"	,  lowerCamelCase__  :     List[Any]=0.0	,  lowerCamelCase__  :     str=0.0	,  lowerCamelCase__  :     List[str]=0.0_2	,  lowerCamelCase__  :     Any=1E-12	,  lowerCamelCase__  :     Any=2_24	,  lowerCamelCase__  :     Dict=16	,  lowerCamelCase__  :     Optional[Any]=3	,  lowerCamelCase__  :     str=False	,  lowerCamelCase__  :     Optional[int]=False	,  lowerCamelCase__  :     Optional[Any]=False	,  lowerCamelCase__  :     int=False	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     Dict=0.1	,  lowerCamelCase__  :     str=True	,  lowerCamelCase__  :     int=[3, 5, 7, 11]	,  lowerCamelCase__  :     Optional[Any]=[1, 2, 3, 6]	,  lowerCamelCase__  :     List[Any]=True	,  lowerCamelCase__  :     Optional[Any]=0.4	,  lowerCamelCase__  :     Dict=2_56	,  lowerCamelCase__  :     Optional[int]=1	,  lowerCamelCase__  :     List[str]=False	,  lowerCamelCase__  :     Dict=2_55	,  **lowerCamelCase__  :     List[Any]	,  )   ->List[str]:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__ )

					_UpperCAmelCase		:       Tuple								=      hidden_size
					_UpperCAmelCase		:       int								=      num_hidden_layers
					_UpperCAmelCase		:       List[Any]								=      num_attention_heads
					_UpperCAmelCase		:       Tuple								=      intermediate_size
					_UpperCAmelCase		:       Dict								=      hidden_act
					_UpperCAmelCase		:       Union[str, Any]								=      hidden_dropout_prob
					_UpperCAmelCase		:       Optional[Any]								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       List[str]								=      initializer_range
					_UpperCAmelCase		:       Optional[Any]								=      layer_norm_eps

					_UpperCAmelCase		:       Tuple								=      image_size
					_UpperCAmelCase		:       Optional[Any]								=      patch_size
					_UpperCAmelCase		:       Dict								=      num_channels
					_UpperCAmelCase		:       Union[str, Any]								=      use_mask_token
					_UpperCAmelCase		:       List[Any]								=      use_absolute_position_embeddings
					_UpperCAmelCase		:       Union[str, Any]								=      use_relative_position_bias
					_UpperCAmelCase		:       Any								=      use_shared_relative_position_bias
					_UpperCAmelCase		:       List[str]								=      layer_scale_init_value
					_UpperCAmelCase		:       Dict								=      drop_path_rate
					_UpperCAmelCase		:       List[Any]								=      use_mean_pooling
					# decode head attributes (semantic segmentation)
					_UpperCAmelCase		:       Dict								=      out_indices
					_UpperCAmelCase		:       Any								=      pool_scales
					# auxiliary head attributes (semantic segmentation)
					_UpperCAmelCase		:       Union[str, Any]								=      use_auxiliary_head
					_UpperCAmelCase		:       Optional[int]								=      auxiliary_loss_weight
					_UpperCAmelCase		:       List[str]								=      auxiliary_channels
					_UpperCAmelCase		:       List[Any]								=      auxiliary_num_convs
					_UpperCAmelCase		:       Optional[Any]								=      auxiliary_concat_input
					_UpperCAmelCase		:       List[Any]								=      semantic_loss_ignore_index






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Any          =     version.parse("1.11"	)




			@property
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Mapping[str, Mapping[int, str]]:



					'''simple docstring'''







					return OrderedDict(
					    [
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ] )






			@property
			def 							lowerCAmelCase__    (		self  :     Dict )   ->float:



					'''simple docstring'''







					return 1E-4







'''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)



