




"""simple docstring"""






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()
lowercase__     						=						logging.get_logger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[Any]    =    RobertaPreLayerNormConfig.from_pretrained(
				    lowercase__							,    architectures=['RobertaPreLayerNormForMaskedLM']		)

				# convert state_dict
				_lowerCamelCase     :			str    =    torch.load(hf_hub_download(repo_id=lowercase__							,    filename='pytorch_model.bin'		)		)
				_lowerCamelCase     :			Dict    =    {}
				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     :			Any    =    '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     :			Dict    =    tensor_value

				_lowerCamelCase     :			List[Any]    =    RobertaPreLayerNormForMaskedLM.from_pretrained(
				    pretrained_model_name_or_path=lowercase__							,    config=lowercase__							,    state_dict=lowercase__		)
				model.save_pretrained(lowercase__		)

				# convert tokenizer
				_lowerCamelCase     :			Dict    =    AutoTokenizer.from_pretrained(lowercase__		)
				tokenizer.save_pretrained(lowercase__		)


if __name__ == "__main__":
			lowercase__     						=						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."""
			)
			lowercase__     						=						parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





"""simple docstring"""






def      _snake_case			(				lowercase__		):
				stooge(lowercase__							,    0							,    len(lowercase__		) - 1		)
				return arr







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if i >= h:
								return

				# If first element is smaller than the last then swap them
				if arr[i] > arr[h]:
								_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    arr[h], arr[i]

				# If there are more than 2 elements in the array
				if h - i + 1 > 2:
								_lowerCamelCase     :			Union[str, Any]    =    (int)((h - i + 1) / 3		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)

								# Recursively sort last 2/3 elements
								stooge(lowercase__							,    i + t							,    (lowercase__)		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)


if __name__ == "__main__":
			lowercase__     						=						input("""Enter numbers separated by a comma:\n""").strip()
			lowercase__     						=						[int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))