





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
    _UpperCAmelCase       : Tuple			     =       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""")
    _UpperCAmelCase       : Optional[Any]			     =       parser.parse_args()

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

    _UpperCAmelCase       : Tuple			     =       model.state_dict()
    _UpperCAmelCase       : Tuple			     =       {}

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

    # Transformer Blocks #
    _UpperCAmelCase       : List[Any]			     =       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"]:
                    _UpperCAmelCase       : Tuple			     =       state_dict[
                        F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                    ]
            _UpperCAmelCase       : List[str]			     =       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"]:
                    _UpperCAmelCase       : Optional[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"]:
            _UpperCAmelCase       : Any			     =       state_dict[F"""{layer}"""]
        if args.vocab_transform:
            for w in ["weight", "bias"]:
                _UpperCAmelCase       : Dict			     =       state_dict[F"""lm_head.dense.{w}"""]
                _UpperCAmelCase       : int			     =       state_dict[F"""lm_head.layer_norm.{w}"""]
    elif args.model_type == "gpt2":
        for w in ["weight", "bias"]:
            _UpperCAmelCase       : Dict			     =       state_dict[F"""{prefix}.ln_f.{w}"""]
        _UpperCAmelCase       : Optional[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)












from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

_UpperCAmelCase       : Union[str, Any]			     =       (3, 9, -11, 0, 7, 5, 1, -1)
_UpperCAmelCase       : Union[str, Any]			     =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	42
 UpperCAmelCase__									=	42


class      lowerCAmelCase							:
 def __init__(   self						:   Any    ,  UpperCAmelCase						:   Iterable[int]			)    ->			None:
     lowerCamelCase__					:   Node | None      				=	None
     for i in sorted(UpperCAmelCase    ,  reverse=UpperCAmelCase			):
         lowerCamelCase__					:   Tuple      				=	Node(UpperCAmelCase    ,  self.head			)
 def __iter__(   self						:   Optional[int]			)    ->			Iterator[int]:
     lowerCamelCase__					:   str      				=	self.head
     while node:
         yield node.data
         lowerCamelCase__					:   List[str]      				=	node.next_node
 def __len__(   self						:   List[str]			)    ->			int:
     return sum(1 for _ in self			)







 def __str__(   self						:   Any			)    ->			str:
     return " -> ".join([str(UpperCAmelCase			) for node in self]			)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  SortedLinkedList:
    return SortedLinkedList(list(_UpperCAmelCase				) + list(_UpperCAmelCase				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    _UpperCAmelCase       : Optional[Any]			     =       SortedLinkedList
    print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





