




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


UpperCAmelCase_		:					Dict          =  logging.get_logger(__name__)

UpperCAmelCase_		:					str          =  {
    "facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """nllb-moe"""
						UpperCamelCase      =       ["""past_key_values"""]
						UpperCamelCase      =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

						def __init__(		self :str     ,			__snake_case :str=12_81_12     ,			__snake_case :str=10_24     ,			__snake_case :List[Any]=12     ,			__snake_case :Union[str, Any]=40_96     ,			__snake_case :Any=16     ,			__snake_case :Union[str, Any]=12     ,			__snake_case :int=40_96     ,			__snake_case :List[Any]=16     ,			__snake_case :Optional[int]=0.05     ,			__snake_case :Tuple=0.05     ,			__snake_case :Dict=True     ,			__snake_case :Optional[int]=True     ,			__snake_case :str="relu"     ,			__snake_case :List[str]=10_24     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :List[Any]=0.1     ,			__snake_case :int=0.0     ,			__snake_case :int=0.02     ,			__snake_case :Union[str, Any]=2     ,			__snake_case :Union[str, Any]=True     ,			__snake_case :Optional[int]=False     ,			__snake_case :Any="float32"     ,			__snake_case :Union[str, Any]=False     ,			__snake_case :Tuple=1_28     ,			__snake_case :str=64     ,			__snake_case :Dict=4     ,			__snake_case :Tuple=4     ,			__snake_case :Optional[Any]=0.001     ,			__snake_case :Optional[Any]=0.001     ,			__snake_case :List[Any]="all"     ,			__snake_case :int=False     ,			__snake_case :List[Any]=False     ,			__snake_case :Optional[int]=1.0     ,			__snake_case :List[str]=0.2     ,			__snake_case :int=1     ,			__snake_case :Dict=0     ,			__snake_case :List[str]=2     ,			__snake_case :Dict=False     ,			**__snake_case :Optional[Any]     ,			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=vocab_size
										__magic_name__						:						Optional[Any]										=max_position_embeddings
										__magic_name__						:						List[Any]										=d_model
										__magic_name__						:						Union[str, Any]										=encoder_ffn_dim
										__magic_name__						:						List[Any]										=encoder_layers
										__magic_name__						:						Optional[int]										=encoder_attention_heads
										__magic_name__						:						Optional[Any]										=decoder_ffn_dim
										__magic_name__						:						List[str]										=decoder_layers
										__magic_name__						:						Any										=decoder_attention_heads
										__magic_name__						:						int										=dropout
										__magic_name__						:						Any										=attention_dropout
										__magic_name__						:						Optional[Any]										=activation_dropout
										__magic_name__						:						int										=activation_function
										__magic_name__						:						str										=init_std
										__magic_name__						:						Optional[int]										=encoder_layerdrop
										__magic_name__						:						str										=decoder_layerdrop
										__magic_name__						:						Optional[int]										=use_cache
										__magic_name__						:						Union[str, Any]										=encoder_layers
										__magic_name__						:						int										=scale_embedding  # scale factor will be sqrt(d_model) if True
										__magic_name__						:						Any										=router_z_loss_coef
										__magic_name__						:						Tuple										=router_aux_loss_coef
										__magic_name__						:						Optional[Any]										=decoder_sparse_step
										__magic_name__						:						Dict										=encoder_sparse_step
										__magic_name__						:						List[Any]										=num_experts
										__magic_name__						:						Tuple										=expert_capacity
										__magic_name__						:						Union[str, Any]										=router_bias
										if router_dtype not in ["float32", "float16", "bfloat16"]:
														raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"			)
										__magic_name__						:						str										=router_dtype

										__magic_name__						:						Union[str, Any]										=router_ignore_padding_tokens
										__magic_name__						:						Dict										=batch_prioritized_routing
										__magic_name__						:						Optional[Any]										=second_expert_policy
										__magic_name__						:						Tuple										=normalize_router_prob_before_dropping
										__magic_name__						:						Any										=moe_eval_capacity_token_fraction
										__magic_name__						:						List[str]										=moe_token_dropout
										__magic_name__						:						str										=output_router_logits
										super().__init__(
										    pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			is_encoder_decoder=__snake_case     ,			decoder_start_token_id=__snake_case     ,			**__snake_case     ,			)











import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
       _lowerCamelCase      							=   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      							=   parser.parse_args()

       if args.model_type == "roberta":
              _lowerCamelCase      							=   RobertaForMaskedLM.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'roberta'
       elif args.model_type == "gpt2":
              _lowerCamelCase      							=   GPTaLMHeadModel.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'transformer'

       _lowerCamelCase      							=   model.state_dict()
       _lowerCamelCase      							=   {}

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

    # Transformer Blocks #
       _lowerCamelCase      							=   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      							=   state_dict[
                                       F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                                   ]
                     _lowerCamelCase      							=   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      							=   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      							=   state_dict[F'''{layer}''']
              if args.vocab_transform:
                     for w in ["weight", "bias"]:
                            _lowerCamelCase      							=   state_dict[F'''lm_head.dense.{w}''']
                            _lowerCamelCase      							=   state_dict[F'''lm_head.layer_norm.{w}''']
       elif args.model_type == "gpt2":
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.ln_f.{w}''']
              _lowerCamelCase      							=   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)

