





import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_SCREAMING_SNAKE_CASE 		=						"""\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

_SCREAMING_SNAKE_CASE 		=						"""\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

_SCREAMING_SNAKE_CASE 		=						"""
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION   )
class SCREAMING_SNAKE_CASE_  (   datasets.Metric   ):






						def UpperCAmelCase_			( self : List[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							if version.parse(scb.__version__		) < version.parse('1.4.12'		):
								raise ImportWarning(
								    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
								    'You can install it with `pip install "sacrebleu>=1.4.12"`.'		)
							return datasets.MetricInfo(
							    description=_DESCRIPTION						,				citation=_CITATION						,				homepage='http://www.cs.umd.edu/~snover/tercom/'						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
							        {
							            'predictions': datasets.Value('string'						,				id='sequence'		),
							            'references': datasets.Sequence(datasets.Value('string'						,				id='sequence'		)						,				id='references'		),
							        }		)						,				codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']						,				reference_urls=[
							        'https://github.com/jhclark/tercom',
							    ]						,				)






						def UpperCAmelCase_			( self : List[str]						,				_A : Tuple						,				_A : List[Any]						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				)							->      Dict:







							"""simple docstring"""


							snake_case_						:     int     =		len(references[0]		)
							if any(len(_A		) != references_per_prediction for refs in references		):
								raise ValueError('Sacrebleu requires the same number of references for each prediction'		)
							snake_case_						:     List[Any]     =		[[refs[i] for refs in references] for i in range(_A		)]

							snake_case_						:     Dict     =		TER(
							    normalized=_A						,				no_punct=_A						,				asian_support=_A						,				case_sensitive=_A						,				)
							snake_case_						:     int     =		sb_ter.corpus_score(_A						,				_A		)

							return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}








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

import requests


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		None
	if token is not None:
		snake_case_						:     List[str]     =		{'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(__a				,	headers=__a				).json()
	snake_case_						:     List[str]     =		{}

	try:
		job_links.update({job['name']: job['html_url'] for job in result['jobs']}				)
		snake_case_						:     Dict     =		math.ceil((result['total_count'] - 1_00) / 1_00				)

		for i in range(__a				):
			snake_case_						:     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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Union[str, Any]     =		None
	if token is not None:
		snake_case_						:     List[Any]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

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

	try:
		artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}				)
		snake_case_						:     str     =		math.ceil((result['total_count'] - 1_00) / 1_00				)

		for i in range(__a				):
			snake_case_						:     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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				):
	snake_case_						:     Dict     =		None
	if token is not None:
		snake_case_						:     List[Any]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

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


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Any     =		[]
	snake_case_						:     Any     =		[]
	snake_case_						:     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:
							snake_case_						:     Tuple     =		line.decode('UTF-8'				).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									snake_case_						:     Tuple     =		line[: line.index(': '				)]
									snake_case_						:     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
								snake_case_						:     Any     =		line[len('FAILED '				) :]
								failed_tests.append(__a				)
							elif filename == "job_name.txt":
								snake_case_						:     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.'				)

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

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

	return result


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Any     =		[]

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

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


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

	return test


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		[(x[0], x[1], get_model(x[2]				)) for x in logs]
	snake_case_						:     str     =		[x for x in logs if x[2] is not None]
	snake_case_						:     int     =		{x[2] for x in logs}

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

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


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[Any]     =		'| no. | error | status |'
	snake_case_						:     str     =		'|-:|:-|:-|'
	snake_case_						:     Tuple     =		[header, sep]
	for error in reduced_by_error:
		snake_case_						:     Dict     =		reduced_by_error[error]['count']
		snake_case_						:     List[str]     =		f"""| {count} | {error[:1_00]} |  |"""
		lines.append(__a				)

	return "\n".join(__a				)


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

	return "\n".join(__a				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						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.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()

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

						_SCREAMING_SNAKE_CASE 		=						get_job_links(args.workflow_run_id, token=args.token)
						_SCREAMING_SNAKE_CASE 		=						{}
						# 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:
																								_SCREAMING_SNAKE_CASE 		=						k.find(""" / """)
																								_SCREAMING_SNAKE_CASE 		=						k[index + len(""" / """) :]
																		_SCREAMING_SNAKE_CASE 		=						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)

						_SCREAMING_SNAKE_CASE 		=						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)

						_SCREAMING_SNAKE_CASE 		=						get_all_errors(args.output_dir, job_links=job_links)

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

						# print the top 30 most common test errors
						_SCREAMING_SNAKE_CASE 		=						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)

						_SCREAMING_SNAKE_CASE 		=						reduce_by_error(errors)
						_SCREAMING_SNAKE_CASE 		=						reduce_by_model(errors)

						_SCREAMING_SNAKE_CASE 		=						make_github_table(reduced_by_error)
						_SCREAMING_SNAKE_CASE 		=						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)

