


"""simple docstring"""


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


a_       							=    logging.get_logger(__name__)

a_       							=    {}
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'llama'
		__UpperCamelCase           =			['past_key_values']







		def __init__(					self      :       Tuple    ,   a__      :       List[Any]=3_20_00    ,   a__      :       Optional[int]=40_96    ,   a__      :       Optional[Any]=1_10_08    ,   a__      :       Optional[int]=32    ,   a__      :       Union[str, Any]=32    ,   a__      :       Union[str, Any]=None    ,   a__      :       Optional[Any]="silu"    ,   a__      :       str=20_48    ,   a__      :       Optional[int]=0.0_2    ,   a__      :       List[Any]=1E-6    ,   a__      :       Any=True    ,   a__      :       Optional[int]=0    ,   a__      :       Optional[int]=1    ,   a__      :       Any=2    ,   a__      :       Optional[int]=1    ,   a__      :       Optional[Any]=False    ,   a__      :       Dict=None    ,   **a__      :       str    ,   )       ->						Dict:





						'''simple docstring'''







						_A					  =			vocab_size
						_A					  =			max_position_embeddings
						_A					  =			hidden_size
						_A					  =			intermediate_size
						_A					  =			num_hidden_layers
						_A					  =			num_attention_heads

						# for backward compatibility
						if num_key_value_heads is None:
										_A					  =			num_attention_heads

						_A					  =			num_key_value_heads
						_A					  =			hidden_act
						_A					  =			initializer_range
						_A					  =			rms_norm_eps
						_A					  =			pretraining_tp
						_A					  =			use_cache
						_A					  =			rope_scaling
						self._rope_scaling_validation()

						super().__init__(
						    pad_token_id=a__    ,   bos_token_id=a__    ,   eos_token_id=a__    ,   tie_word_embeddings=a__    ,   **a__    ,   )







		def  a_ (					self      :       Dict		)       ->						Tuple:





						'''simple docstring'''







						if self.rope_scaling is None:
										return

						if not isinstance(self.rope_scaling    ,   a__		) or len(self.rope_scaling		) != 2:
										raise ValueError(
										    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
										    F"""got {self.rope_scaling}"""		)
						_A					  =			self.rope_scaling.get("type"    ,   a__		)
						_A					  =			self.rope_scaling.get("factor"    ,   a__		)
						if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
										raise ValueError(
										    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""		)
						if rope_scaling_factor is None or not isinstance(a__    ,   a__		) or rope_scaling_factor <= 1.0:
										raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)



"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

		a_       							=    model.state_dict()
		a_       							=    {}

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

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