






from __future__ import annotations





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if (direction == 1 and array[indexa] > array[indexa]) or (
	    direction == 0 and array[indexa] < array[indexa]
	):
		__lowercase       ,						__lowercase             =  array[indexa], array[indexa]





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if length > 1:
		__lowercase             =  int(length / 2					)
		for i in range(lowerCamelCase				,  low + middle					):
			comp_and_swap(lowerCamelCase				,  lowerCamelCase				,  i + middle				,  lowerCamelCase					)
		bitonic_merge(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
		bitonic_merge(lowerCamelCase				,  low + middle				,  lowerCamelCase				,  lowerCamelCase					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if length > 1:
		__lowercase             =  int(length / 2					)
		bitonic_sort(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  1					)
		bitonic_sort(lowerCamelCase				,  low + middle				,  lowerCamelCase				,  0					)
		bitonic_merge(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)


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

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print("""\nSorted array in ascending order is: """, end="""""")
						print(*unsorted, sep=""", """)

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print("""Sorted array in descending order is: """, end="""""")
						print(*unsorted, sep=""", """)









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()
__UpperCamelCase      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  RobertaPreLayerNormConfig.from_pretrained(
	    lowerCamelCase				,  architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  filename="""pytorch_model.bin"""					)					)
	__lowercase             =  {}
	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."""					):
			__lowercase             =  """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

		__lowercase             =  tensor_value

	__lowercase             =  RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

	# convert tokenizer
	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	tokenizer.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						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."""
						)
						__UpperCamelCase      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

