


'''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__    	=     {
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Tuple          =     "yolos"




			def __init__(		self  :     List[Any]	,  lowerCamelCase__  :     Optional[Any]=7_68	,  lowerCamelCase__  :     Optional[Any]=12	,  lowerCamelCase__  :     Union[str, Any]=12	,  lowerCamelCase__  :     Optional[Any]=30_72	,  lowerCamelCase__  :     Any="gelu"	,  lowerCamelCase__  :     List[Any]=0.0	,  lowerCamelCase__  :     Any=0.0	,  lowerCamelCase__  :     Union[str, Any]=0.0_2	,  lowerCamelCase__  :     str=1E-12	,  lowerCamelCase__  :     List[str]=[5_12, 8_64]	,  lowerCamelCase__  :     Optional[int]=16	,  lowerCamelCase__  :     Dict=3	,  lowerCamelCase__  :     Optional[int]=True	,  lowerCamelCase__  :     Any=1_00	,  lowerCamelCase__  :     Optional[int]=True	,  lowerCamelCase__  :     List[str]=False	,  lowerCamelCase__  :     List[Any]=1	,  lowerCamelCase__  :     Optional[Any]=5	,  lowerCamelCase__  :     Optional[int]=2	,  lowerCamelCase__  :     int=5	,  lowerCamelCase__  :     str=2	,  lowerCamelCase__  :     Optional[Any]=0.1	,  **lowerCamelCase__  :     int	,  )   ->Optional[int]:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__ )

					_UpperCAmelCase		:       Optional[int]								=      hidden_size
					_UpperCAmelCase		:       Dict								=      num_hidden_layers
					_UpperCAmelCase		:       List[str]								=      num_attention_heads
					_UpperCAmelCase		:       Any								=      intermediate_size
					_UpperCAmelCase		:       Dict								=      hidden_act
					_UpperCAmelCase		:       Union[str, Any]								=      hidden_dropout_prob
					_UpperCAmelCase		:       Tuple								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       Dict								=      initializer_range
					_UpperCAmelCase		:       int								=      layer_norm_eps
					_UpperCAmelCase		:       Dict								=      image_size
					_UpperCAmelCase		:       Dict								=      patch_size
					_UpperCAmelCase		:       str								=      num_channels
					_UpperCAmelCase		:       List[str]								=      qkv_bias
					_UpperCAmelCase		:       Optional[Any]								=      num_detection_tokens
					_UpperCAmelCase		:       List[str]								=      use_mid_position_embeddings
					_UpperCAmelCase		:       Optional[Any]								=      auxiliary_loss
					# Hungarian matcher
					_UpperCAmelCase		:       Optional[Any]								=      class_cost
					_UpperCAmelCase		:       Tuple								=      bbox_cost
					_UpperCAmelCase		:       List[Any]								=      giou_cost
					# Loss coefficients
					_UpperCAmelCase		:       str								=      bbox_loss_coefficient
					_UpperCAmelCase		:       Dict								=      giou_loss_coefficient
					_UpperCAmelCase		:       List[str]								=      eos_coefficient






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Union[str, Any]          =     version.parse("1.11"	)




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



					'''simple docstring'''







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




			@property
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->float:



					'''simple docstring'''







					return 1E-4






			@property
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->int:



					'''simple docstring'''







					return 12







'''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)



