


import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_A			=						importlib.util.find_spec('s3fs') is not None

if _has_safs:
   from .safilesystem import SaFileSystem  # noqa: F401

_A			=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
   if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
      warnings.warn(f"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
   fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):
     if "://" in dataset_path:
          __UpperCamelCase   							=dataset_path.split('://'    )[1]
     return dataset_path

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : fsspec.AbstractFileSystem    ):
     if fs is not None and fs.protocol != "file":
          return True
     else:
          return False

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : fsspec.AbstractFileSystem      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : str    ):
     __UpperCamelCase   							=not is_remote_filesystem(SCREAMING_SNAKE_CASE__    )
     if is_local:
          # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
          shutil.move(fs._strip_protocol(SCREAMING_SNAKE_CASE__    )      ,					fs._strip_protocol(SCREAMING_SNAKE_CASE__    )    )
     else:
          fs.mv(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					recursive=SCREAMING_SNAKE_CASE__    )





def 						_UpperCAmelCase    (      ):
     if hasattr(fsspec.asyn      ,					'reset_lock'    ):
          # for future fsspec>2022.05.0
          fsspec.asyn.reset_lock()
     else:
          __UpperCamelCase   							=None
          __UpperCamelCase   							=None
          __UpperCamelCase   							=threading.Lock()









import argparse

import torch

from transformers import BertForMaskedLM


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

   if args.model_type == "bert":
      _A			=						BertForMaskedLM.from_pretrained(args.model_name)
      _A			=						'bert'
   else:
      raise ValueError('args.model_type should be "bert".')

   _A			=						model.state_dict()
   _A			=						{}

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

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

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _A			=						state_dict['cls.predictions.decoder.weight']
   _A			=						state_dict['cls.predictions.bias']
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _A			=						state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _A			=						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)





