



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :int						=			{}

						snake_case_ :List[Any]						=			job["""started_at"""]
						snake_case_ :int						=			job["""completed_at"""]

						snake_case_ :str						=			date_parser.parse(_lowercase	)
						snake_case_ :Tuple						=			date_parser.parse(_lowercase	)

						snake_case_ :Optional[int]						=			round((end_datetime - start_datetime).total_seconds() / 60.0	)

						snake_case_ :int						=			start
						snake_case_ :Optional[int]						=			end
						snake_case_ :Optional[Any]						=			duration_in_min

						return job_info
def    A_  (			_lowercase,					_lowercase=None	):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			None
						if token is not None:
												snake_case_ :Optional[Any]						=			{"""Accept""": """application/vnd.github+json""", """Authorization""": f"""Bearer {token}"""}

						snake_case_ :Union[str, Any]						=			f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						snake_case_ :Optional[int]						=			requests.get(_lowercase,					headers=_lowercase	).json()
						snake_case_ :Optional[Any]						=			{}

						try:
												job_time.update({job["""name"""]: extract_time_from_single_job(_lowercase	) for job in result["""jobs"""]}	)
												snake_case_ :int						=			math.ceil((result["""total_count"""] - 100) / 100	)

												for i in range(_lowercase	):
																		snake_case_ :Union[str, Any]						=			requests.get(url + f"""&page={i + 2}""",					headers=_lowercase	).json()
																		job_time.update({job["""name"""]: extract_time_from_single_job(_lowercase	) for job in result["""jobs"""]}	)

												return job_time
						except Exception:
												print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""	)

						return {}


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
						__a      =				parser.parse_args()

						__a      =				get_job_time(args.workflow_run_id)
						__a      =				dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

						for k, v in job_time.items():
												print(F"""{k}: {v['duration']}""")

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"Salesforce/blip-image-captioning-base"
   lowerCAmelCase_														=							(
       "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
       "image to caption, and returns a text that contains the description in English."
   )
   lowerCAmelCase_														=							"image_captioner"
   lowerCAmelCase_														=							AutoModelForVisionaSeq

   lowerCAmelCase_														=							["image"]
   lowerCAmelCase_														=							["text"]




   def __init__(    self			:				List[Any]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     requires_backends(self       ,	["""vision"""]      )
     super().__init__(*_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				"Image"      ):



     """simple docstring"""






     return self.pre_processor(images=_lowercase       ,	return_tensors="""pt"""      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     return self.model.generate(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Any      ):



     """simple docstring"""






     return self.pre_processor.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )[0].strip()
