






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       a		:       Any			     = 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")
       a		:       Union[str, Any]			     = parser.parse_args()

       if args.model_type == "bert":
              a		:       Optional[Any]			     = BertForMaskedLM.from_pretrained(args.model_name)
              a		:       List[str]			     = "bert"
       else:
              raise ValueError("args.model_type should be \"bert\".")

       a		:       Dict			     = model.state_dict()
       a		:       Union[str, Any]			     = {}

       for w in ["word_embeddings", "position_embeddings"]:
              a		:       Tuple			     = state_dict[f"""{prefix}.embeddings.{w}.weight"""]
       for w in ["weight", "bias"]:
              a		:       int			     = state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

       a		:       Optional[int]			     = 0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     a		:       List[str]			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
                     ]
                     a		:       str			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
                     ]
                     a		:       Tuple			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
                     ]

                     a		:       Tuple			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
                     ]
                     a		:       Union[str, Any]			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
                     ]

                     a		:       str			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
                     ]
                     a		:       List[str]			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
                     ]
                     a		:       Optional[Any]			     = state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
                     ]
              std_idx += 1

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










from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			Any		,      __lowercase					:			Optional[NestedDataStructureLike[PathLike]] = None		,      __lowercase					:			Optional[NamedSplit] = None		,      __lowercase					:			Optional[Features] = None		,      __lowercase					:			str = None		,      __lowercase					:			bool = False		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[int] = None		,      **__lowercase					:			List[str]		,      )							->      Tuple:
        __UpperCAmelCase							:	Any           =  path_or_paths
        __UpperCAmelCase							:	Dict           =  split if split or isinstance(__lowercase		,      __lowercase					) else """train"""
        __UpperCAmelCase							:	Optional[int]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	str           =  keep_in_memory
        __UpperCAmelCase							:	Optional[int]           =  streaming
        __UpperCAmelCase							:	Dict           =  num_proc
        __UpperCAmelCase							:	Tuple           =  kwargs




      @abstractmethod
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
        pass


class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			List[str]		,      __lowercase					:			Optional[Features] = None		,      __lowercase					:			str = None		,      __lowercase					:			bool = False		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[int] = None		,      **__lowercase					:			Optional[Any]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	Optional[Any]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	Optional[int]           =  keep_in_memory
        __UpperCAmelCase							:	Dict           =  streaming
        __UpperCAmelCase							:	Optional[Any]           =  num_proc
        __UpperCAmelCase							:	Union[str, Any]           =  kwargs




      @abstractmethod
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Union[Dataset, IterableDataset]:
        pass


