



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


UpperCAmelCase__     = logging.get_logger(__name__)





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     int					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     set()
			_UpperCAmelCase	 =     []

			def parse_line(_UpperCAmelCase       :     Tuple					):
						for line in fp:
									if isinstance(_UpperCAmelCase							,     _UpperCAmelCase					):
												_UpperCAmelCase	 =     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(_UpperCAmelCase					) > 0:
															_UpperCAmelCase	 =     '\n'.join(_UpperCAmelCase					)
															# Only keep the warnings specified in `targets`
															if any(F": {x}: " in warning for x in targets					):
																		selected_warnings.add(_UpperCAmelCase					)
															buffer.clear()
												continue
									else:
												_UpperCAmelCase	 =     line.strip()
												buffer.append(_UpperCAmelCase					)

			if from_gh:
						for filename in os.listdir(_UpperCAmelCase					):
									_UpperCAmelCase	 =     os.path.join(_UpperCAmelCase							,     _UpperCAmelCase					)
									if not os.path.isdir(_UpperCAmelCase					):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(_UpperCAmelCase					) as fp:
															parse_line(_UpperCAmelCase					)
			else:
						try:
									with zipfile.ZipFile(_UpperCAmelCase					) as z:
												for filename in z.namelist():
															if not os.path.isdir(_UpperCAmelCase					):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(_UpperCAmelCase					) as fp:
																					parse_line(_UpperCAmelCase					)
						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 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     str					)       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     set()

			_UpperCAmelCase	 =     [os.path.join(_UpperCAmelCase							,     _UpperCAmelCase					) for p in os.listdir(_UpperCAmelCase					) if (p.endswith('.zip'					) or from_gh)]
			for p in paths:
						selected_warnings.update(extract_warnings_from_single_artifact(_UpperCAmelCase							,     _UpperCAmelCase					)					)

			return selected_warnings


if __name__ == "__main__":





							def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				Union[str, Any]:







										'''simple docstring'''
										return values.split(','					)

							UpperCAmelCase__     = 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.",
							)

							UpperCAmelCase__     = parser.parse_args()

							UpperCAmelCase__     = 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
														UpperCAmelCase__     = 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
							UpperCAmelCase__     = extract_warnings(args.output_dir, args.targets)
							UpperCAmelCase__     = 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

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
