


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      str    				=				{}

				lowerCAmelCase_ :      Dict    				=				job['''started_at''']
				lowerCAmelCase_ :      str    				=				job['''completed_at''']

				lowerCAmelCase_ :      Dict    				=				date_parser.parse(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase_ :      Any    				=				date_parser.parse(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase_ :      Any    				=				round((end_datetime - start_datetime).total_seconds() / 60.0 )

				lowerCAmelCase_ :      Any    				=				start
				lowerCAmelCase_ :      List[Any]    				=				end
				lowerCAmelCase_ :      str    				=				duration_in_min

				return job_info



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_=None )-> str:
				lowerCAmelCase_ :      Tuple    				=				None
				if token is not None:
								lowerCAmelCase_ :      List[str]    				=				{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

				lowerCAmelCase_ :      Dict    				=				f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
				lowerCAmelCase_ :      List[Any]    				=				requests.get(SCREAMING_SNAKE_CASE_ ,   headers=SCREAMING_SNAKE_CASE_ ).json()
				lowerCAmelCase_ :      Any    				=				{}

				try:
								job_time.update({job['''name''']: extract_time_from_single_job(SCREAMING_SNAKE_CASE_ ) for job in result['''jobs''']} )
								lowerCAmelCase_ :      List[str]    				=				math.ceil((result['''total_count'''] - 100) / 100 )

								for i in range(SCREAMING_SNAKE_CASE_ ):
												lowerCAmelCase_ :      Any    				=				requests.get(url + f"""&page={i + 2}""" ,   headers=SCREAMING_SNAKE_CASE_ ).json()
												job_time.update({job['''name''']: extract_time_from_single_job(SCREAMING_SNAKE_CASE_ ) 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__":
						_UpperCAmelCase			:  int     =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
						_UpperCAmelCase			:  int     =parser.parse_args()

						_UpperCAmelCase			:  Optional[int]     =get_job_time(args.workflow_run_id)
						_UpperCAmelCase			:  Optional[Any]     =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']}""")



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:  Union[str, Any]     ={
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Optional[Any]     =["""LlamaTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Optional[int]     =["""LlamaTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Tuple     =[
						    """LlamaForCausalLM""",
						    """LlamaModel""",
						    """LlamaPreTrainedModel""",
						    """LlamaForSequenceClassification""",
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama_fast import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

						_UpperCAmelCase			:  List[Any]     =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)