






'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class     _a						:
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot"""							,							metadata={"""help""": """Model name or path of model to be trained."""}				)
       __a       :  Optional[str]            =      field(
           default="""./"""							,							metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}				)
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot-clean-train"""							,							metadata={"""help""": """Name or path of training dataset."""}				)
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot-clean-valid"""							,							metadata={"""help""": """Name or path of validation dataset."""}				)
       __a       :  Optional[int]            =      field(default=2							,							metadata={"""help""": """Batch size for training."""}				)
       __a       :  Optional[int]            =      field(default=2							,							metadata={"""help""": """Batch size for evaluation."""}				)
       __a       :  Optional[float]            =      field(default=0.1							,							metadata={"""help""": """Value of weight decay."""}				)
       __a       :  Optional[int]            =      field(
           default=10_000							,							metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}				)
       __a       :  Optional[float]            =      field(default=2e-4							,							metadata={"""help""": """Learning rate fo training."""}				)
       __a       :  Optional[str]            =      field(default="""cosine"""							,							metadata={"""help""": """Learning rate."""}				)
       __a       :  Optional[int]            =      field(
           default=750							,							metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}				)
       __a       :  Optional[int]            =      field(
           default=16							,							metadata={"""help""": """Number of gradient accumulation steps."""}				)
       __a       :  Optional[bool]            =      field(
           default=__a							,							metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}				)
       __a       :  Optional[int]            =      field(default=50_000							,							metadata={"""help""": """Maximum number of training steps."""}				)
       __a       :  Optional[int]            =      field(
           default=-1							,							metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}				)
       __a       :  Optional[int]            =      field(default=1_024							,							metadata={"""help""": """Sequence lengths used for training."""}				)
       __a       :  Optional[int]            =      field(default=1							,							metadata={"""help""": """Training seed."""}				)
       __a       :  Optional[int]            =      field(
           default=1_024							,							metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,							)
       __a       :  Optional[str]            =      field(
           default=__a							,							metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}				)
       __a       :  Optional[bool]            =      field(default=__a							,							metadata={"""help""": """If True the data is pretokenized."""}				)






@dataclass
class     _a						:
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot"""							,							metadata={"""help""": """Model name or path of model to be evaluated."""}				)
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot-clean-valid"""							,							metadata={"""help""": """Name or path of validation dataset."""}				)
       __a       :  Optional[int]            =      field(default=2							,							metadata={"""help""": """Batch size used for evaluation."""}				)
       __a       :  Optional[int]            =      field(
           default=-1							,							metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}				)
       __a       :  Optional[int]            =      field(default=1_024							,							metadata={"""help""": """Length of sequences to be evaluated."""}				)
       __a       :  Optional[int]            =      field(default=1							,							metadata={"""help""": """Random seed used for evaluation."""}				)






@dataclass
class     _a						:
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot"""							,							metadata={"""help""": """Model name or path of model to be evaluated."""}				)
       __a       :  Optional[int]            =      field(default=__a							,							metadata={"""help""": """Number of workers used for code evaluation."""}				)
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,							)
       __a       :  Optional[bool]            =      field(
           default=__a							,							metadata={"""help""": """Sample from the language model's output distribution."""}				)
       __a       :  Optional[float]            =      field(default=0.2							,							metadata={"""help""": """Sampling temperature used for generation."""}				)
       __a       :  Optional[int]            =      field(default=256							,							metadata={"""help""": """Maximum number of newly generated tokens."""}				)
       __a       :  Optional[int]            =      field(default=0							,							metadata={"""help""": """Top-k parameter used for generation."""}				)
       __a       :  Optional[float]            =      field(default=0.95							,							metadata={"""help""": """Top-p parameter used for nucleus sampling."""}				)
       __a       :  Optional[int]            =      field(default=10							,							metadata={"""help""": """Number of generations to run in parallel."""}				)
       __a       :  Optional[int]            =      field(
           default=200							,							metadata={"""help""": """Number of completions to generate for each sample."""}				)
       __a       :  Optional[int]            =      field(default=1							,							metadata={"""help""": """Random seed used for evaluation."""}				)
       __a       :  Optional[str]            =      field(
           default="""eval_results.json"""							,							metadata={"""help""": """Random seed used for evaluation."""}				)
       __a       :  Optional[str]            =      field(
           default="""0"""							,							metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}				)
       __a       :  Optional[int]            =      field(
           default=-1							,							metadata={
               """help""": (
                   """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                   """ number corresponds to which GPU device id to run on."""
               )
           }							,							)






@dataclass
class     _a						:
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={
               """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
           }							,							)
       __a       :  Optional[str]            =      field(
           default="""transformersbook/codeparrot"""							,							metadata={"""help""": """Folder or name of dataset to process."""}				)
       __a       :  Optional[str]            =      field(
           default="""codeparrot-clean"""							,							metadata={"""help""": """Folder to save processed processed dataset."""}				)
       __a       :  Optional[int]            =      field(
           default=100_000							,							metadata={"""help""": """Number of files to save per JSON output file."""}				)
       __a       :  Optional[str]            =      field(default="""content"""							,							metadata={"""help""": """Column containing text data to process."""}				)
       __a       :  Optional[float]            =      field(
           default=1_000							,							metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}				)
       __a       :  Optional[float]            =      field(
           default=100							,							metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}				)
       __a       :  Optional[float]            =      field(
           default=0.25							,							metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}				)
       __a       :  Optional[float]            =      field(
           default=1.5							,							metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}				)
       __a       :  Optional[float]            =      field(
           default=0.7							,							metadata={"""help""": """Probability for filtering config, test and uncommon files."""}				)
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot"""							,							metadata={"""help""": """Name or path to the tokenizer."""}							,							)
       __a       :  Optional[bool]            =      field(
           default=__a							,							metadata={"""help""": """If True, near-duplicate samples are removed."""}				)
       __a       :  Optional[float]            =      field(
           default=0.85							,							metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}				)






@dataclass
class     _a						:
       __a       :  Optional[str]            =      field(
           default="""gpt2"""							,							metadata={"""help""": """Base tokenizer to build new tokenizer from."""}				)
       __a       :  Optional[str]            =      field(
           default="""transformersbook/codeparrot-train"""							,							metadata={"""help""": """Dataset to train tokenizer on."""}				)
       __a       :  Optional[str]            =      field(default="""content"""							,							metadata={"""help""": """Column containing text data to process."""}				)
       __a       :  Optional[int]            =      field(default=200_000							,							metadata={"""help""": """Number of examples to train tokenizer on."""}				)
       __a       :  Optional[int]            =      field(
           default=32_768							,							metadata={"""help""": """Number of examples to train the tokenizer on."""}				)
       __a       :  Optional[str]            =      field(default="""codeparrot"""							,							metadata={"""help""": """Name of new tokenizer."""}				)
       __a       :  Optional[bool]            =      field(default=__a							,							metadata={"""help""": """Push saved tokenizer to the hub."""}				)






@dataclass
class     _a						:
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot"""							,							metadata={"""help""": """Name or path to the tokenizer."""}				)
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot-clean-train"""							,							metadata={"""help""": """Name or path to the dataset to pretokenize."""}				)
       __a       :  Optional[str]            =      field(
           default="""tokenized-codeparrot-train"""							,							metadata={"""help""": """Repo name of the pretokenized data."""}				)
       __a       :  Optional[int]            =      field(default=__a							,							metadata={"""help""": """Number of workers used for code evaluation."""}				)






@dataclass
class     _a						:
       __a       :  Optional[str]            =      field(
           default="""gpt2-large"""							,							metadata={"""help""": """Configuration to use for model initialization."""}				)
       __a       :  Optional[str]            =      field(
           default="""codeparrot/codeparrot"""							,							metadata={"""help""": """Tokenizer attached to model."""}				)
       __a       :  Optional[str]            =      field(default="""codeparrot"""							,							metadata={"""help""": """Name of the created model."""}				)
       __a       :  Optional[bool]            =      field(default=__a							,							metadata={"""help""": """Push saved tokenizer to the hub."""}				)










'''simple docstring'''







import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 		snake_case_							(_a			:							List[str]  ):
    UpperCAmelCase     =	{}

    UpperCAmelCase     =	job['''started_at''']
    UpperCAmelCase     =	job['''completed_at''']

    UpperCAmelCase     =	date_parser.parse(_a  )
    UpperCAmelCase     =	date_parser.parse(_a  )

    UpperCAmelCase     =	round((end_datetime - start_datetime).total_seconds() / 60.0  )

    UpperCAmelCase     =	start
    UpperCAmelCase     =	end
    UpperCAmelCase     =	duration_in_min

    return job_info





def 		snake_case_							(_a			:							str					,     _a			:							List[str]=None  ):
    UpperCAmelCase     =	None
    if token is not None:
        UpperCAmelCase     =	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

    UpperCAmelCase     =	F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
    UpperCAmelCase     =	requests.get(_a					,     headers=_a  ).json()
    UpperCAmelCase     =	{}

    try:
        job_time.update({job['''name''']: extract_time_from_single_job(_a  ) for job in result['''jobs''']}  )
        UpperCAmelCase     =	math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0  )

        for i in range(_a  ):
            UpperCAmelCase     =	requests.get(url + F"&page={i + 2}"					,     headers=_a  ).json()
            job_time.update({job['''name''']: extract_time_from_single_job(_a  ) 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"]}""")




