


"""simple docstring"""


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    :					Any							   =   logging.get_logger(__name__)







def 			__snake_case							( UpperCamelCase__			,      UpperCamelCase__						)   ->		Any:







		"""simple docstring"""





		A       							=			set()
		A       							=			[]

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

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







		"""simple docstring"""





		A       							=			set()

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

		return selected_warnings


if __name__ == "__main__":







	def 			__snake_case							( UpperCamelCase__						)   ->		int:







			"""simple docstring"""





			return values.split(','						)

	UpperCamelCase    :					Any							   =   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    :					Tuple							   =   parser.parse_args()

	UpperCamelCase    :					Dict							   =   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    :					str							   =   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    :					Tuple							   =   extract_warnings(args.output_dir, args.targets)
	UpperCamelCase    :					Optional[Any]							   =   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)







"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     return ext

       raise Exception(
           f"""Unable to determine file format from file extension {path}. """
           f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				parser.add_parser("run"      ,       help="Run a pipeline through the CLI"							)
          run_parser.add_argument("--task"      ,       choices=get_supported_tasks()      ,       help="Task to run"							)
          run_parser.add_argument("--input"      ,       type=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       default=-1      ,       help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,       )
          run_parser.add_argument("--overwrite"      ,       action="store_true"      ,       help="Allow overwriting the output file."							)
          run_parser.set_defaults(func=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 lowercase       :							Dict									=				self._reader.save_binary(_a							)
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""							)
          else:
                 self._reader.save(_a							)


