


'''simple docstring'''







import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests






def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      List[str]=None       ):
       _A					:     Optional[int]     = None
       if token is not None:
              _A					:     List[str]     = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

       _A					:     Union[str, Any]     = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
       _A					:     Optional[int]     = requests.get(__a  ,headers=__a       ).json()
       _A					:     List[str]     = {}

       try:
              job_links.update({job['name']: job['html_url'] for job in result['jobs']}       )
              _A					:     Dict     = math.ceil((result['total_count'] - 100) / 100       )

              for i in range(__a       ):
                     _A					:     Optional[Any]     = requests.get(url + F'&page={i + 2}'  ,headers=__a       ).json()
                     job_links.update({job['name']: job['html_url'] for job in result['jobs']}       )

              return job_links
       except Exception:
              print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'       )

       return {}






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Tuple=None       ):
       _A					:     Union[str, Any]     = None
       if token is not None:
              _A					:     List[Any]     = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

       _A					:     Optional[Any]     = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
       _A					:     Union[str, Any]     = requests.get(__a  ,headers=__a       ).json()
       _A					:     Any     = {}

       try:
              artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}       )
              _A					:     str     = math.ceil((result['total_count'] - 100) / 100       )

              for i in range(__a       ):
                     _A					:     int     = requests.get(url + F'&page={i + 2}'  ,headers=__a       ).json()
                     artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}       )

              return artifacts
       except Exception:
              print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'       )

       return {}






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]       ):
       _A					:     Dict     = None
       if token is not None:
              _A					:     List[Any]     = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

       _A					:     Optional[int]     = requests.get(__a  ,headers=__a  ,allow_redirects=__a       )
       _A					:     str     = result.headers['Location']
       _A					:     List[str]     = requests.get(__a  ,allow_redirects=__a       )
       _A					:     Optional[Any]     = os.path.join(__a  ,F'{artifact_name}.zip'       )
       with open(__a  ,'wb'       ) as fp:
              fp.write(response.content       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      int=None       ):
       _A					:     Any     = []
       _A					:     Any     = []
       _A					:     Tuple     = None

       with zipfile.ZipFile(__a       ) as z:
              for filename in z.namelist():
                     if not os.path.isdir(__a       ):
                            # read the file
                            if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                                   with z.open(__a       ) as f:
                                          for line in f:
                                                 _A					:     Tuple     = line.decode('UTF-8'       ).strip()
                                                 if filename == "failures_line.txt":
                                                        try:
                                                               # `error_line` is the place where `error` occurs
                                                               _A					:     Tuple     = line[: line.index(': '       )]
                                                               _A					:     Union[str, Any]     = line[line.index(': '       ) + len(': '       ) :]
                                                               errors.append([error_line, error]       )
                                                        except Exception:
                                                               # skip un-related lines
                                                               pass
                                                 elif filename == "summary_short.txt" and line.startswith('FAILED '       ):
                                                        # `test` is the test method that failed
                                                        _A					:     Any     = line[len('FAILED '       ) :]
                                                        failed_tests.append(__a       )
                                                 elif filename == "job_name.txt":
                                                        _A					:     Union[str, Any]     = line

       if len(__a       ) != len(__a       ):
              raise ValueError(
                  F'`errors` and `failed_tests` should have the same number of elements. Got {len(__a       )} for `errors` '
                  F'and {len(__a       )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
                  ' problem.'       )

       _A					:     List[str]     = None
       if job_name and job_links:
              _A					:     Union[str, Any]     = job_links.get(__a  ,__a       )

       # A list with elements of the form (line of error, error, failed test)
       _A					:     Optional[Any]     = [x + [y] + [job_link] for x, y in zip(__a  ,__a       )]

       return result






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[str]=None       ):
       _A					:     Any     = []

       _A					:     Any     = [os.path.join(__a  ,__a       ) for p in os.listdir(__a       ) if p.endswith('.zip'       )]
       for p in paths:
              errors.extend(get_errors_from_single_artifact(__a  ,job_links=__a       )       )

       return errors






def 				lowerCAmelCase__	(    lowerCamelCase					:      Dict  ,lowerCamelCase					:      int=None       ):
       _A					:     Optional[int]     = Counter()
       counter.update([x[1] for x in logs]       )
       _A					:     str     = counter.most_common()
       _A					:     Tuple     = {}
       for error, count in counts:
              if error_filter is None or error not in error_filter:
                     _A					:     int     = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

       _A					:     int     = dict(sorted(r.items()  ,key=lambda lowerCamelCase       : item[1]["count"]  ,reverse=__a       )       )
       return r






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]       ):
       _A					:     Tuple     = test.split('::'       )[0]
       if test.startswith('tests/models/'       ):
              _A					:     List[str]     = test.split('/'       )[2]
       else:
              _A					:     Union[str, Any]     = None

       return test






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Any=None       ):
       _A					:     Optional[int]     = [(x[0], x[1], get_model(x[2]       )) for x in logs]
       _A					:     str     = [x for x in logs if x[2] is not None]
       _A					:     int     = {x[2] for x in logs}

       _A					:     Dict     = {}
       for test in tests:
              _A					:     List[str]     = Counter()
              # count by errors in `test`
              counter.update([x[1] for x in logs if x[2] == test]       )
              _A					:     Any     = counter.most_common()
              _A					:     str     = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
              _A					:     Tuple     = sum(error_counts.values()       )
              if n_errors > 0:
                     _A					:     List[Any]     = {'count': n_errors, 'errors': error_counts}

       _A					:     int     = dict(sorted(r.items()  ,key=lambda lowerCamelCase       : item[1]["count"]  ,reverse=__a       )       )
       return r






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       _A					:     Optional[Any]     = '| no. | error | status |'
       _A					:     str     = '|-:|:-|:-|'
       _A					:     Tuple     = [header, sep]
       for error in reduced_by_error:
              _A					:     Dict     = reduced_by_error[error]['count']
              _A					:     List[str]     = F'| {count} | {error[:100]} |  |'
              lines.append(__a       )

       return "\n".join(__a       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
       _A					:     Optional[Any]     = '| model | no. of errors | major error | count |'
       _A					:     Union[str, Any]     = '|-:|-:|-:|-:|'
       _A					:     Optional[int]     = [header, sep]
       for model in reduced_by_model:
              _A					:     Any     = reduced_by_model[model]['count']
              _A					:     Dict     = list(reduced_by_model[model]['errors'].items()       )[0]
              _A					:     Any     = F'| {model} | {count} | {error[:60]} | {_count} |'
              lines.append(__a       )

       return "\n".join(__a       )


if __name__ == "__main__":
   A				:					Union[str, 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.''')
   A				:					Dict    						=			parser.parse_args()

   os.makedirs(args.output_dir, exist_ok=True)

   A				:					List[Any]    						=			get_job_links(args.workflow_run_id, token=args.token)
   A				:					Any    						=			{}
   # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
   # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
   if _job_links:
      for k, v in _job_links.items():
         # This is how GitHub actions combine job names.
         if " / " in k:
            A				:					Dict    						=			k.find(''' / ''')
            A				:					Tuple    						=			k[index + len(''' / ''') :]
         A				:					List[str]    						=			v
   with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
      json.dump(job_links, fp, ensure_ascii=False, indent=4)

   A				:					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)

   for idx, (name, url) in enumerate(artifacts.items()):
      download_artifact(name, url, args.output_dir, args.token)
      # Be gentle to GitHub
      time.sleep(1)

   A				:					List[str]    						=			get_all_errors(args.output_dir, job_links=job_links)

   # `e[1]` is the error
   A				:					Tuple    						=			Counter()
   counter.update([e[1] for e in errors])

   # print the top 30 most common test errors
   A				:					str    						=			counter.most_common(30)
   for item in most_common:
      print(item)

   with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
      json.dump(errors, fp, ensure_ascii=False, indent=4)

   A				:					Tuple    						=			reduce_by_error(errors)
   A				:					List[Any]    						=			reduce_by_model(errors)

   A				:					int    						=			make_github_table(reduced_by_error)
   A				:					str    						=			make_github_table_per_model(reduced_by_model)

   with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
      fp.write(sa)
   with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
      fp.write(sa)










'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be trained."}	)
    a													=   field(
        default="./"    , metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path of training dataset."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for training."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for evaluation."}	)
    a													=   field(default=0.1    , metadata={"help": "Value of weight decay."}	)
    a													=   field(
        default=1_0000    , metadata={"help": "Size of buffer used to shuffle streaming dataset."}	)
    a													=   field(default=2E-4    , metadata={"help": "Learning rate fo training."}	)
    a													=   field(default="cosine"    , metadata={"help": "Learning rate."}	)
    a													=   field(
        default=750    , metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
    a													=   field(
        default=16    , metadata={"help": "Number of gradient accumulation steps."}	)
    a													=   field(
        default=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , metadata={"help": "Maximum number of training steps."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Sequence lengths used for training."}	)
    a													=   field(default=1    , metadata={"help": "Training seed."}	)
    a													=   field(
        default=1024    , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}    , )
    a													=   field(
        default=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , metadata={"help": "If True the data is pretokenized."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size used for evaluation."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Length of sequences to be evaluated."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , metadata={"help": "Sample from the language model's output distribution."}	)
    a													=   field(default=0.2    , metadata={"help": "Sampling temperature used for generation."}	)
    a													=   field(default=256    , metadata={"help": "Maximum number of newly generated tokens."}	)
    a													=   field(default=0    , metadata={"help": "Top-k parameter used for generation."}	)
    a													=   field(default=0.95    , metadata={"help": "Top-p parameter used for nucleus sampling."}	)
    a													=   field(default=10    , metadata={"help": "Number of generations to run in parallel."}	)
    a													=   field(
        default=200    , metadata={"help": "Number of completions to generate for each sample."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="eval_results.json"    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="0"    , metadata={"help": "Allow `code_eval` to execute Python code on machine"}	)
    a													=   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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default=a_    , metadata={
            "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
        }    , )
    a													=   field(
        default="transformersbook/codeparrot"    , metadata={"help": "Folder or name of dataset to process."}	)
    a													=   field(
        default="codeparrot-clean"    , metadata={"help": "Folder to save processed processed dataset."}	)
    a													=   field(
        default=10_0000    , metadata={"help": "Number of files to save per JSON output file."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(
        default=1000    , metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=100    , metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=0.25    , metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}	)
    a													=   field(
        default=1.5    , metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}	)
    a													=   field(
        default=0.7    , metadata={"help": "Probability for filtering config, test and uncommon files."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}    , )
    a													=   field(
        default=a_    , metadata={"help": "If True, near-duplicate samples are removed."}	)
    a													=   field(
        default=0.85    , metadata={"help": "Jaccard threshold for near-duplicate samples."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2"    , metadata={"help": "Base tokenizer to build new tokenizer from."}	)
    a													=   field(
        default="transformersbook/codeparrot-train"    , metadata={"help": "Dataset to train tokenizer on."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(default=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path to the dataset to pretokenize."}	)
    a													=   field(
        default="tokenized-codeparrot-train"    , metadata={"help": "Repo name of the pretokenized data."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2-large"    , metadata={"help": "Configuration to use for model initialization."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Tokenizer attached to model."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of the created model."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)






