



import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command






class      UpperCamelCase    (						unittest.TestCase     ):
	lowerCAmelCase	:   Tuple				=	inspect.getfile(accelerate.test_utils     )
	lowerCAmelCase	:   Dict				=	os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["""scripts""", """test_cli.py"""]     )

	lowerCAmelCase	:   List[str]				=	["accelerate", "launch"]
	lowerCAmelCase	:   List[Any]				=	Path.home() / ".cache/huggingface/accelerate"
	lowerCAmelCase	:   Tuple				=	"default_config.yaml"
	lowerCAmelCase	:   Tuple				=	config_folder / config_file
	lowerCAmelCase	:   int				=	config_folder / "_default_config.yaml"

	lowerCAmelCase	:   Optional[Any]				=	Path("""tests/test_configs"""     )



	@classmethod
	def 			__A							(       cls		):
							if cls.config_path.is_file():
													cls.config_path.rename(cls.changed_path		)



	@classmethod
	def 			__A							(       cls		):
							if cls.changed_path.is_file():
													cls.changed_path.rename(cls.config_path		)



	def 			__A							(       self		):
							A__							=       self.base_cmd
							if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
													cmd += ["--multi_gpu"]
							execute_subprocess_async(cmd + [self.test_file_path]			,	env=os.environ.copy()		)



	def 			__A							(       self		):
							for config in sorted(self.test_config_path.glob("**/*.yaml"		)		):
													with self.subTest(config_file=lowerCamelCase_		):
																			execute_subprocess_async(
																			    self.base_cmd + ["--config_file", str(lowerCamelCase_		), self.test_file_path]			,	env=os.environ.copy()		)



	def 			__A							(       self		):
							execute_subprocess_async(["accelerate", "test"]			,	env=os.environ.copy()		)






class      UpperCamelCase    (						unittest.TestCase     ):
	lowerCAmelCase	:   Optional[int]				=	"test-tpu"
	lowerCAmelCase	:   Optional[int]				=	"us-central1-a"
	lowerCAmelCase	:   Dict				=	"ls"
	lowerCAmelCase	:   Optional[int]				=	["accelerate", "tpu-config"]
	lowerCAmelCase	:   Optional[Any]				=	"cd /usr/share"
	lowerCAmelCase	:   Optional[Any]				=	"tests/test_samples/test_command_file.sh"
	lowerCAmelCase	:   int				=	"Running gcloud compute tpus tpu-vm ssh"



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd
							    + ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd
							    + [
							        "--config_file",
							        "tests/test_configs/0_12_0.yaml",
							        "--command",
							        self.command,
							        "--tpu_zone",
							        self.tpu_zone,
							        "--tpu_name",
							        self.tpu_name,
							        "--debug",
							    ]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"]			,	return_stdout=lowerCamelCase_		)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd
							    + [
							        "--config_file",
							        "tests/test_configs/latest.yaml",
							        "--command",
							        self.command,
							        "--command",
							        "echo \"Hello World\"",
							        "--debug",
							    ]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd
							    + ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd
							    + [
							        "--config_file",
							        "tests/test_configs/0_12_0.yaml",
							        "--command_file",
							        self.command_file,
							        "--tpu_zone",
							        self.tpu_zone,
							        "--tpu_name",
							        self.tpu_name,
							        "--debug",
							    ]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""			,	lowerCamelCase_			,	)



	def 			__A							(       self		):
							A__							=       run_command(
							    self.cmd
							    + [
							        "--config_file",
							        "tests/test_configs/latest.yaml",
							        "--install_accelerate",
							        "--accelerate_version",
							        "12.0.0",
							        "--debug",
							    ]			,	return_stdout=lowerCamelCase_			,	)
							self.assertIn(
							    F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""			,	lowerCamelCase_			,	)










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_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						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__							=       "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__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


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





