



'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_     :       Dict								=						logging.get_logger(__name__)

lowerCAmelCase_     :       List[Any]								=						{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   Optional[Any]	     =				'deta'
						_lowerCAmelCase     :   List[Any]	     =				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						}







						def __init__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      List[str]=None				,    lowerCAmelCase__   :      Optional[Any]=9_00				,    lowerCAmelCase__   :      Dict=20_48				,    lowerCAmelCase__   :      Union[str, Any]=6				,    lowerCAmelCase__   :      int=20_48				,    lowerCAmelCase__   :      Union[str, Any]=8				,    lowerCAmelCase__   :      int=6				,    lowerCAmelCase__   :      Optional[Any]=10_24				,    lowerCAmelCase__   :      Optional[Any]=8				,    lowerCAmelCase__   :      Union[str, Any]=0.0				,    lowerCAmelCase__   :      int=True				,    lowerCAmelCase__   :      Dict="relu"				,    lowerCAmelCase__   :      int=2_56				,    lowerCAmelCase__   :      Optional[int]=0.1				,    lowerCAmelCase__   :      Dict=0.0				,    lowerCAmelCase__   :      Tuple=0.0				,    lowerCAmelCase__   :      int=0.02				,    lowerCAmelCase__   :      List[Any]=1.0				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Tuple=False				,    lowerCAmelCase__   :      Union[str, Any]="sine"				,    lowerCAmelCase__   :      Any=5				,    lowerCAmelCase__   :      Union[str, Any]=4				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Union[str, Any]=True				,    lowerCAmelCase__   :      Optional[int]=3_00				,    lowerCAmelCase__   :      str=True				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      List[Any]=1				,    lowerCAmelCase__   :      List[Any]=5				,    lowerCAmelCase__   :      List[str]=2				,    lowerCAmelCase__   :      Dict=1				,    lowerCAmelCase__   :      Tuple=1				,    lowerCAmelCase__   :      str=5				,    lowerCAmelCase__   :      List[Any]=2				,    lowerCAmelCase__   :      str=0.1				,    lowerCAmelCase__   :      int=0.25				,    **lowerCAmelCase__   :      str				,    ):

													"""simple docstring"""
													if backbone_config is None:
																				logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
																				SCREAMING_SNAKE_CASE					:      str     					=			CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']				)
													else:
																				if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      Any     					=			backbone_config.pop('''model_type'''				)
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			CONFIG_MAPPING[backbone_model_type]
																											SCREAMING_SNAKE_CASE					:      Any     					=			config_class.from_dict(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			backbone_config
													SCREAMING_SNAKE_CASE					:      List[str]     					=			num_queries
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			d_model
													SCREAMING_SNAKE_CASE					:      Tuple     					=			encoder_ffn_dim
													SCREAMING_SNAKE_CASE					:      Any     					=			encoder_layers
													SCREAMING_SNAKE_CASE					:      Any     					=			encoder_attention_heads
													SCREAMING_SNAKE_CASE					:      List[str]     					=			decoder_ffn_dim
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			decoder_layers
													SCREAMING_SNAKE_CASE					:      List[str]     					=			decoder_attention_heads
													SCREAMING_SNAKE_CASE					:      Any     					=			dropout
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			attention_dropout
													SCREAMING_SNAKE_CASE					:      Dict     					=			activation_dropout
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			activation_function
													SCREAMING_SNAKE_CASE					:      Tuple     					=			init_std
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			init_xavier_std
													SCREAMING_SNAKE_CASE					:      List[str]     					=			encoder_layerdrop
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			auxiliary_loss
													SCREAMING_SNAKE_CASE					:      str     					=			position_embedding_type
													# deformable attributes
													SCREAMING_SNAKE_CASE					:      Dict     					=			num_feature_levels
													SCREAMING_SNAKE_CASE					:      Dict     					=			encoder_n_points
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			decoder_n_points
													SCREAMING_SNAKE_CASE					:      Tuple     					=			two_stage
													SCREAMING_SNAKE_CASE					:      Tuple     					=			two_stage_num_proposals
													SCREAMING_SNAKE_CASE					:      int     					=			with_box_refine
													SCREAMING_SNAKE_CASE					:      Tuple     					=			assign_first_stage
													if two_stage is True and with_box_refine is False:
																				raise ValueError('''If two_stage is True, with_box_refine must be True.'''				)
													# Hungarian matcher
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			class_cost
													SCREAMING_SNAKE_CASE					:      List[str]     					=			bbox_cost
													SCREAMING_SNAKE_CASE					:      Tuple     					=			giou_cost
													# Loss coefficients
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			mask_loss_coefficient
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			dice_loss_coefficient
													SCREAMING_SNAKE_CASE					:      List[str]     					=			bbox_loss_coefficient
													SCREAMING_SNAKE_CASE					:      List[str]     					=			giou_loss_coefficient
													SCREAMING_SNAKE_CASE					:      Any     					=			eos_coefficient
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			focal_alpha
													super().__init__(is_encoder_decoder=lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													return self.encoder_attention_heads







						@property
						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													return self.d_model







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			copy.deepcopy(self.__dict__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.backbone_config.to_dict()
													SCREAMING_SNAKE_CASE					:      str     					=			self.__class__.model_type
													return output









'''simple docstring'''



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowerCAmelCase_     :       Any								=						argparse.ArgumentParser(
							    description=(
							        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
							        ' Distillation'
							    )
							)
							parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
							parser.add_argument('--model_name', default='roberta-large', type=str)
							parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
							parser.add_argument('--vocab_transform', action='store_true')
							lowerCAmelCase_     :       Dict								=						parser.parse_args()

							if args.model_type == "roberta":
														lowerCAmelCase_     :       str								=						RobertaForMaskedLM.from_pretrained(args.model_name)
														lowerCAmelCase_     :       Tuple								=						'roberta'
							elif args.model_type == "gpt2":
														lowerCAmelCase_     :       List[Any]								=						GPTaLMHeadModel.from_pretrained(args.model_name)
														lowerCAmelCase_     :       Optional[Any]								=						'transformer'

							lowerCAmelCase_     :       List[Any]								=						model.state_dict()
							lowerCAmelCase_     :       List[Any]								=						{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowerCAmelCase_     :       List[str]								=						state_dict[f'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowerCAmelCase_     :       Any								=						f'{prefix}.embeddings.{w}.weight'
																					lowerCAmelCase_     :       Any								=						state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowerCAmelCase_     :       str								=						f'{prefix}.embeddings.LayerNorm.{w}'
																					lowerCAmelCase_     :       str								=						state_dict[param_name]

    # Transformer Blocks #
							lowerCAmelCase_     :       List[str]								=						0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowerCAmelCase_     :       List[str]								=						state_dict[
																																			    f'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowerCAmelCase_     :       Any								=						state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowerCAmelCase_     :       Any								=						state_dict[
																																			    f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowerCAmelCase_     :       List[Any]								=						state_dict[f'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowerCAmelCase_     :       str								=						state_dict[f'lm_head.dense.{w}']
																												lowerCAmelCase_     :       Optional[int]								=						state_dict[f'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowerCAmelCase_     :       Dict								=						state_dict[f'{prefix}.ln_f.{w}']
														lowerCAmelCase_     :       Any								=						state_dict['lm_head.weight']

							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)




