


import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase__   	=							datasets.logging.get_logger(__name__)

UpperCAmelCase__   	=							'''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

UpperCAmelCase__   	=							'''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCAmelCase__   	=							'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION)
class       lowerCamelCase__   (       datasets.Metric):





     def      __A					(self       )     ->   Optional[int]:
      return datasets.MetricInfo(
          description=_DESCRIPTION		,     citation=_CITATION		,     homepage='''https://unbabel.github.io/COMET/html/index.html'''		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
              {
                  '''sources''': datasets.Value('''string'''		,     id='''sequence'''       ),
                  '''predictions''': datasets.Value('''string'''		,     id='''sequence'''       ),
                  '''references''': datasets.Value('''string'''		,     id='''sequence'''       ),
              }       )		,     codebase_urls=['''https://github.com/Unbabel/COMET''']		,     reference_urls=[
              '''https://github.com/Unbabel/COMET''',
              '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
              '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
          ]		,     )





     def      __A					(self		,     UpperCAmelCase       )     ->   Dict:
      if self.config_name == "default":
       _lowercase							      =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
      else:
       _lowercase							      =comet.load_from_checkpoint(comet.download_model(self.config_name       )       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=False       )     ->   int:
      if gpus is None:
       _lowercase							      =1 if torch.cuda.is_available() else 0
      _lowercase							      ={'''src''': sources, '''mt''': predictions, '''ref''': references}
      _lowercase							      =[dict(zip(UpperCAmelCase		,     UpperCAmelCase       )       ) for t in zip(*data.values()       )]
      _lowercase      ,    _lowercase							      =self.scorer.predict(UpperCAmelCase		,     gpus=UpperCAmelCase		,     progress_bar=UpperCAmelCase       )
      return {"mean_score": mean_score, "scores": scores}







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

import requests


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





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

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

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

		for i in range(_A							):
			snake_case_		    =     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__    (		_A     ,	_A=None							):







	'''simple docstring'''





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

	snake_case_		    =     f"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
	snake_case_		    =     requests.get(_A     ,	headers=_A							).json()
	snake_case_		    =     {}

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

		for i in range(_A							):
			snake_case_		    =     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__    (		_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





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

	snake_case_		    =     requests.get(_A     ,	headers=_A     ,	allow_redirects=_A							)
	snake_case_		    =     result.headers["Location"]
	snake_case_		    =     requests.get(_A     ,	allow_redirects=_A							)
	snake_case_		    =     os.path.join(_A     ,	f"{artifact_name}.zip"							)
	with open(_A     ,	"wb"							) as fp:
		fp.write(response.content							)


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     []
	snake_case_		    =     []
	snake_case_		    =     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:
							snake_case_		    =     line.decode("UTF-8"							).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									snake_case_		    =     line[: line.index(": "							)]
									snake_case_		    =     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
								snake_case_		    =     line[len("FAILED "							) :]
								failed_tests.append(_A							)
							elif filename == "job_name.txt":
								snake_case_		    =     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."							)

	snake_case_		    =     None
	if job_name and job_links:
		snake_case_		    =     job_links.get(_A     ,	_A							)

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

	return result


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     []

	snake_case_		    =     [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__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     Counter()
	counter.update([x[1] for x in logs]							)
	snake_case_		    =     counter.most_common()
	snake_case_		    =     {}
	for error, count in counts:
		if error_filter is None or error not in error_filter:
			snake_case_		    =     {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     test.split("::"							)[0]
	if test.startswith("tests/models/"							):
		snake_case_		    =     test.split("/"							)[2]
	else:
		snake_case_		    =     None

	return test


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     [(x[0], x[1], get_model(x[2]							)) for x in logs]
	snake_case_		    =     [x for x in logs if x[2] is not None]
	snake_case_		    =     {x[2] for x in logs}

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

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     "| no. | error | status |"
	snake_case_		    =     "|-:|:-|:-|"
	snake_case_		    =     [header, sep]
	for error in reduced_by_error:
		snake_case_		    =     reduced_by_error[error]["count"]
		snake_case_		    =     f"| {count} | {error[:100]} |  |"
		lines.append(_A							)

	return "\n".join(_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     "| model | no. of errors | major error | count |"
	snake_case_		    =     "|-:|-:|-:|-:|"
	snake_case_		    =     [header, sep]
	for model in reduced_by_model:
		snake_case_		    =     reduced_by_model[model]["count"]
		snake_case_    ,      snake_case_		    =     list(reduced_by_model[model]["errors"].items()							)[0]
		snake_case_		    =     f"| {model} | {count} | {error[:60]} | {_count} |"
		lines.append(_A							)

	return "\n".join(_A							)


if __name__ == "__main__":
		lowercase__				:     int 		=						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.")
		lowercase__				:     int 		=						parser.parse_args()

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

		lowercase__				:     Optional[Any] 		=						get_job_links(args.workflow_run_id, token=args.token)
		lowercase__				:     List[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:
								lowercase__				:     Tuple 		=						k.find(" / ")
								lowercase__				:     Union[str, Any] 		=						k[index + len(" / ") :]
						lowercase__				:     Union[str, Any] 		=						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)

		lowercase__				:     Any 		=						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)

		lowercase__				:     List[Any] 		=						get_all_errors(args.output_dir, job_links=job_links)

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

		# print the top 30 most common test errors
		lowercase__				:     Union[str, Any] 		=						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)

		lowercase__				:     int 		=						reduce_by_error(errors)
		lowercase__				:     List[str] 		=						reduce_by_model(errors)

		lowercase__				:     Tuple 		=						make_github_table(reduced_by_error)
		lowercase__				:     Optional[int] 		=						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)
