





"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 lowercase__ 	=     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')
 lowercase__ 	=     parser.parse_args()

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

 lowercase__ 	=     model.state_dict()
 lowercase__ 	=     {}

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

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



"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowercase__ 	=     '2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
 raise ImportWarning(
     'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
 )

if version.parse(pyarrow.__version__).major < 8:
 raise ImportWarning(
     'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
     'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
 )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowercase__ 	=     concatenate_datasets
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadManager
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


