





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__           =			logging.get_logger(__name__)





def      _UpperCamelCase  (a__ :str ,      a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      RobertaPreLayerNormConfig.from_pretrained(
							    a__ ,      architectures=["""RobertaPreLayerNormForMaskedLM"""]							)

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

														UpperCamelCase__					  =      tensor_value

							UpperCamelCase__					  =      RobertaPreLayerNormForMaskedLM.from_pretrained(
							    pretrained_model_name_or_path=a__ ,      config=a__ ,      state_dict=a__							)
							model.save_pretrained(a__							)

							# convert tokenizer
							UpperCamelCase__					  =      AutoTokenizer.from_pretrained(a__							)
							tokenizer.save_pretrained(a__							)


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











from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__           =			10





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							for i in range(a__ ,      a__							):
														if array[i] == target:
																					return i
							return -1





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      len(a__							)
							while left <= right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)

														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					UpperCamelCase__					  =      one_third - 1
														elif array[two_third] < target:
																					UpperCamelCase__					  =      two_third + 1

														else:
																					UpperCamelCase__					  =      one_third + 1
																					UpperCamelCase__					  =      two_third - 1
							else:
														return -1





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							if left < right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)
														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					return rec_ternary_search(a__ ,      one_third - 1 ,      a__ ,      a__							)
														elif array[two_third] < target:
																					return rec_ternary_search(two_third + 1 ,      a__ ,      a__ ,      a__							)
														else:
																					return rec_ternary_search(one_third + 1 ,      two_third - 1 ,      a__ ,      a__							)
							else:
														return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase__           =			input("Enter numbers separated by comma:\n").strip()
					UpperCamelCase__           =			[int(item.strip()) for item in user_input.split(",")]
					assert collection == sorted(collection), f"List must be ordered.\n{collection}."
					UpperCamelCase__           =			int(input("Enter the number to be found in the list:\n").strip())
					UpperCamelCase__           =			ite_ternary_search(collection, target)
					UpperCamelCase__           =			rec_ternary_search(0, len(collection) - 1, collection, target)
					if resulta != -1:
										print(f"""Iterative search: {target} found at positions: {resulta}""")
										print(f"""Recursive search: {target} found at positions: {resulta}""")
					else:
										print("Not found")




