



import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Tuple              =						set()
			A							:					List[str]              =						[]

			def parse_line(_lowerCamelCase    ):
						for line in fp:
									if isinstance(_lowerCamelCase ,    _lowerCamelCase    ):
												A							:					int              =						line.decode("UTF-8"    )
									if "warnings summary (final)" in line:
												continue
									# This means we are outside the body of a warning
									elif not line.startswith(" "    ):
												# process a single warning and move it to `selected_warnings`.
												if len(_lowerCamelCase    ) > 0:
															A							:					Tuple              =						"\n".join(_lowerCamelCase    )
															# Only keep the warnings specified in `targets`
															if any(f""": {x}: """ in warning for x in targets    ):
																		selected_warnings.add(_lowerCamelCase    )
															buffer.clear()
												continue
									else:
												A							:					Optional[Any]              =						line.strip()
												buffer.append(_lowerCamelCase    )

			if from_gh:
						for filename in os.listdir(_lowerCamelCase    ):
									A							:					int              =						os.path.join(_lowerCamelCase ,    _lowerCamelCase    )
									if not os.path.isdir(_lowerCamelCase    ):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(_lowerCamelCase    ) as fp:
															parse_line(_lowerCamelCase    )
			else:
						try:
									with zipfile.ZipFile(_lowerCamelCase    ) as z:
												for filename in z.namelist():
															if not os.path.isdir(_lowerCamelCase    ):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(_lowerCamelCase    ) as fp:
																					parse_line(_lowerCamelCase    )
						except Exception:
									logger.warning(
									    f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""    )

			return selected_warnings




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						set()

			A							:					List[Any]              =						[os.path.join(_lowerCamelCase ,    _lowerCamelCase    ) for p in os.listdir(_lowerCamelCase    ) if (p.endswith(".zip"    ) or from_gh)]
			for p in paths:
						selected_warnings.update(extract_warnings_from_single_artifact(_lowerCamelCase ,    _lowerCamelCase    )    )

			return selected_warnings


if __name__ == "__main__":




		def 	UpperCAmelCase   (						_lowerCamelCase    ):
					return values.split(","    )

		__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
		parser.add_argument(
		    """--output_dir""",
		    type=str,
		    required=True,
		    help="""Where to store the downloaded artifacts and other result files.""",
		)
		parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
		# optional parameters
		parser.add_argument(
		    """--targets""",
		    default="""DeprecationWarning,UserWarning,FutureWarning""",
		    type=list_str,
		    help="""Comma-separated list of target warning(s) which we want to extract.""",
		)
		parser.add_argument(
		    """--from_gh""",
		    action="""store_true""",
		    help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
		)

		__SCREAMING_SNAKE_CASE           =  parser.parse_args()

		__SCREAMING_SNAKE_CASE           =  args.from_gh
		if from_gh:
				# The artifacts have to be downloaded using `actions/download-artifact@v3`
				pass
		else:
				os.makedirs(args.output_dir, exist_ok=True)

				# get download links
				__SCREAMING_SNAKE_CASE           =  get_artifacts_links(args.workflow_run_id, token=args.token)
				with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
						json.dump(artifacts, fp, ensure_ascii=False, indent=4)

				# download artifacts
				for idx, (name, url) in enumerate(artifacts.items()):
						print(name)
						print(url)
						print("""=""" * 80)
						download_artifact(name, url, args.output_dir, args.token)
						# Be gentle to GitHub
						time.sleep(1)

    # extract warnings from artifacts
		__SCREAMING_SNAKE_CASE           =  extract_warnings(args.output_dir, args.targets)
		__SCREAMING_SNAKE_CASE           =  sorted(selected_warnings)
		with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
				json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)




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)