





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_a						=   logging.get_logger(__name__)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	RobertaPreLayerNormConfig.from_pretrained(
	    __snake_case ,architectures=['''RobertaPreLayerNormForMaskedLM''']							)

	# convert state_dict
	lowerCamelCase__              =	torch.load(hf_hub_download(repo_id=__snake_case ,filename='''pytorch_model.bin'''							)							)
	lowerCamelCase__              =	{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith('''roberta.'''							):
			lowerCamelCase__              =	'''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''							) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith('''.self.LayerNorm.weight'''							) or tensor_key.endswith('''.self.LayerNorm.bias'''							):
			continue

		lowerCamelCase__              =	tensor_value

	lowerCamelCase__              =	RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__snake_case ,config=__snake_case ,state_dict=__snake_case							)
	model.save_pretrained(__snake_case							)

	# convert tokenizer
	lowerCamelCase__              =	AutoTokenizer.from_pretrained(__snake_case							)
	tokenizer.save_pretrained(__snake_case							)


if __name__ == "__main__":
			_a						=   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--checkpoint-repo",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_a						=   parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)










import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_a						=   logging.get_logger(__name__)






class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		None





@experimental
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)

	return _map_with_joblib(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	num_proc if num_proc <= len(__snake_case							) else len(__snake_case							)
	lowerCamelCase__              =	[]  # We organize the splits ourselve (contiguous splits)
	for index in range(__snake_case							):
		lowerCamelCase__              =	len(__snake_case							) // num_proc
		lowerCamelCase__              =	len(__snake_case							) % num_proc
		lowerCamelCase__              =	div * index + min(__snake_case ,__snake_case							)
		lowerCamelCase__              =	start + div + (1 if index < mod else 0)
		split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

	if len(__snake_case							) != sum(len(i[1]							) for i in split_kwds							):
		raise ValueError(
		    F'Error dividing inputs iterable among processes. '
		    F'Total number of objects {len(__snake_case							)}, '
		    F'length: {sum(len(i[1]							) for i in split_kwds							)}'							)

	logger.info(
	    F'Spawning {num_proc} processes for {len(__snake_case							)} objects in slices of {[len(i[1]							) for i in split_kwds]}'							)
	lowerCamelCase__       ,			lowerCamelCase__              =	None, None
	if not disable_tqdm:
		lowerCamelCase__       ,			lowerCamelCase__              =	(RLock(),), tqdm.set_lock
	with Pool(__snake_case ,initargs=__snake_case ,initializer=__snake_case							) as pool:
		lowerCamelCase__              =	pool.map(__snake_case ,__snake_case							)
	logger.info(F'Finished {num_proc} processes'							)
	lowerCamelCase__              =	[obj for proc_res in mapped for obj in proc_res]
	logger.info(F'Unpacked {len(__snake_case							)} objects'							)

	return mapped





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	import joblib

	with joblib.parallel_backend(ParallelBackendConfig.backend_name ,n_jobs=__snake_case							):
		return joblib.Parallel()(
		    joblib.delayed(__snake_case							)((function, obj, types, None, True, None)							) for obj in iterable							)





@experimental
@contextlib.contextmanager
def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	backend_name

	if backend_name == "spark":
		from joblibspark import register_spark

		register_spark()

		# TODO: call create_cache_and_write_probe if "download" in steps
		# TODO: raise NotImplementedError when Dataset.map etc is called

	try:
		yield
	finally:
		lowerCamelCase__              =	None



