


"""simple docstring"""


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


a_       							=    logging.get_logger(__name__)

a_       							=    {
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase    =			'mra'







		def __init__(					self      :       List[str]    ,   a__      :       Optional[int]=5_02_65    ,   a__      :       Tuple=7_68    ,   a__      :       Dict=12    ,   a__      :       Tuple=12    ,   a__      :       int=30_72    ,   a__      :       List[str]="gelu"    ,   a__      :       List[str]=0.1    ,   a__      :       Tuple=0.1    ,   a__      :       str=5_12    ,   a__      :       Any=1    ,   a__      :       Dict=0.0_2    ,   a__      :       str=1E-5    ,   a__      :       Any="absolute"    ,   a__      :       Optional[Any]=4    ,   a__      :       Tuple="full"    ,   a__      :       List[Any]=0    ,   a__      :       List[str]=0    ,   a__      :       Dict=1    ,   a__      :       List[str]=0    ,   a__      :       Optional[int]=2    ,   **a__      :       List[str]    ,   )       ->						Optional[Any]:





						'''simple docstring'''







						super().__init__(pad_token_id=UpperCAmelCase__    ,   bos_token_id=UpperCAmelCase__    ,   eos_token_id=UpperCAmelCase__    ,   **UpperCAmelCase__		)

						_A					  =			vocab_size
						_A					  =			max_position_embeddings
						_A					  =			hidden_size
						_A					  =			num_hidden_layers
						_A					  =			num_attention_heads
						_A					  =			intermediate_size
						_A					  =			hidden_act
						_A					  =			hidden_dropout_prob
						_A					  =			attention_probs_dropout_prob
						_A					  =			initializer_range
						_A					  =			type_vocab_size
						_A					  =			layer_norm_eps
						_A					  =			position_embedding_type
						_A					  =			block_per_row
						_A					  =			approx_mode
						_A					  =			initial_prior_first_n_blocks
						_A					  =			initial_prior_diagonal_n_blocks



"""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)