



from __future__ import annotations

from collections.abc import Sequence
from typing import Literal




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Dict              =						list(_lowerCamelCase    )
			A							:					Optional[Any]              =						list(_lowerCamelCase    )
			A							:					Optional[Any]              =						0
			for i in range(len(_lowerCamelCase    )    ):
						if lista[i] != lista[i]:
									count += 1
									A							:					Optional[int]              =						"_"
			if count > 1:
						return False
			else:
						return "".join(_lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					List[str]              =						[]
			while True:
						A							:					int              =						["$"] * len(_lowerCamelCase    )
						A							:					List[str]              =						[]
						for i in range(len(_lowerCamelCase    )    ):
									for j in range(i + 1 ,    len(_lowerCamelCase    )    ):
												A							:					List[str]              =						compare_string(binary[i] ,    binary[j]    )
												if k is False:
															A							:					List[Any]              =						"*"
															A							:					int              =						"*"
															temp.append("X"    )
						for i in range(len(_lowerCamelCase    )    ):
									if checka[i] == "$":
												pi.append(binary[i]    )
						if len(_lowerCamelCase    ) == 0:
									return pi
						A							:					List[Any]              =						list(set(_lowerCamelCase    )    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[str]              =						[]
			for minterm in minterms:
						A							:					Optional[Any]              =						""
						for _ in range(_lowerCamelCase    ):
									A							:					int              =						str(minterm % 2    ) + string
									minterm //= 2
						temp.append(_lowerCamelCase    )
			return temp




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					int              =						list(_lowerCamelCase    )
			A							:					Tuple              =						list(_lowerCamelCase    )
			A							:					str              =						0
			for i in range(len(_lowerCamelCase    )    ):
						if lista[i] != lista[i]:
									count_n += 1
			return count_n == count




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[Any]              =						[]
			A							:					str              =						[0] * len(_lowerCamelCase    )
			for i in range(len(chart[0]    )    ):
						A							:					int              =						0
						A							:					Optional[int]              =						-1
						for j in range(len(_lowerCamelCase    )    ):
									if chart[j][i] == 1:
												count += 1
												A							:					Optional[Any]              =						j
						if count == 1:
									A							:					int              =						1
			for i in range(len(_lowerCamelCase    )    ):
						if select[i] == 1:
									for j in range(len(chart[0]    )    ):
												if chart[i][j] == 1:
															for k in range(len(_lowerCamelCase    )    ):
																		A							:					Optional[int]              =						0
									temp.append(prime_implicants[i]    )
			while True:
						A							:					Any              =						0
						A							:					Optional[Any]              =						-1
						A							:					Tuple              =						0
						for i in range(len(_lowerCamelCase    )    ):
									A							:					Dict              =						chart[i].count(1    )
									if count_n > max_n:
												A							:					int              =						count_n
												A							:					Dict              =						i

						if max_n == 0:
									return temp

						temp.append(prime_implicants[rem]    )

						for i in range(len(chart[0]    )    ):
									if chart[rem][i] == 1:
												for j in range(len(_lowerCamelCase    )    ):
															A							:					List[str]              =						0




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[str]              =						[[0 for x in range(len(_lowerCamelCase    )    )] for x in range(len(_lowerCamelCase    )    )]
			for i in range(len(_lowerCamelCase    )    ):
						A							:					int              =						prime_implicants[i].count("_"    )
						for j in range(len(_lowerCamelCase    )    ):
									if is_for_table(prime_implicants[i] ,    binary[j] ,    _lowerCamelCase    ):
												A							:					Optional[Any]              =						1

			return chart




def 	UpperCAmelCase   (						):
			A							:					Optional[Any]              =						int(input("Enter the no. of variables\n"    )    )
			A							:					int              =						[
			    float(_lowerCamelCase    )
			    for x in input(
			        "Enter the decimal representation of Minterms 'Spaces Separated'\n"    ).split()
			]
			A							:					Optional[int]              =						decimal_to_binary(_lowerCamelCase ,    _lowerCamelCase    )

			A							:					int              =						check(_lowerCamelCase    )
			print("Prime Implicants are:"    )
			print(_lowerCamelCase    )
			A							:					Optional[int]              =						prime_implicant_chart(_lowerCamelCase ,    _lowerCamelCase    )

			A							:					Optional[int]              =						selection(_lowerCamelCase ,    _lowerCamelCase    )
			print("Essential Prime Implicants are:"    )
			print(_lowerCamelCase    )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			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."    ):
									A							:					int              =						"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

						A							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


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