


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


lowerCAmelCase__  :Any             =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[Any]             =							{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[Any]           =     'pegasus'
		_a     :  Union[str, Any]           =     ['past_key_values']
		_a     :  Union[str, Any]           =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=50265      ,   _SCREAMING_SNAKE_CASE=1024      ,   _SCREAMING_SNAKE_CASE=12      ,   _SCREAMING_SNAKE_CASE=4096      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=12      ,   _SCREAMING_SNAKE_CASE=4096      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=1024      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=1      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      d_model
									_UpperCAmelCase					=      encoder_ffn_dim
									_UpperCAmelCase					=      encoder_layers
									_UpperCAmelCase					=      encoder_attention_heads
									_UpperCAmelCase					=      decoder_ffn_dim
									_UpperCAmelCase					=      decoder_layers
									_UpperCAmelCase					=      decoder_attention_heads
									_UpperCAmelCase					=      dropout
									_UpperCAmelCase					=      attention_dropout
									_UpperCAmelCase					=      activation_dropout
									_UpperCAmelCase					=      activation_function
									_UpperCAmelCase					=      init_std
									_UpperCAmelCase					=      encoder_layerdrop
									_UpperCAmelCase					=      decoder_layerdrop
									_UpperCAmelCase					=      use_cache
									_UpperCAmelCase					=      encoder_layers
									_UpperCAmelCase					=      scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=_SCREAMING_SNAKE_CASE      ,   eos_token_id=_SCREAMING_SNAKE_CASE      ,   is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,   decoder_start_token_id=_SCREAMING_SNAKE_CASE      ,   forced_eos_token_id=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return self.encoder_attention_heads






		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return self.d_model




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							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__  :Optional[int]             =							parser.parse_args()

		if args.model_type == "bert":
				lowerCAmelCase__  :Tuple             =							BertForMaskedLM.from_pretrained(args.model_name)
				lowerCAmelCase__  :Optional[int]             =							'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		lowerCAmelCase__  :Any             =							model.state_dict()
		lowerCAmelCase__  :Dict             =							{}

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

		lowerCAmelCase__  :str             =							0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						lowerCAmelCase__  :List[str]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

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

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

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