



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__A   			=getLogger(__name__)


__A   			="cuda" if torch.cuda.is_available() else "cpu"

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					str = DEFAULT_DEVICE ,   _UpperCAmelCase							:					Optional[Any]=False ,   _UpperCAmelCase							:					Optional[int]="summarization" ,   _UpperCAmelCase							:					Dict=None ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	Path(_UpperCAmelCase						).open('''w''' ,   encoding='''utf-8'''						)
		__UpperCAmelCase    :     Optional[int]													=	str(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).to(_UpperCAmelCase						)
		if fpaa:
				__UpperCAmelCase    :     Dict													=	model.half()

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		__UpperCAmelCase    :     Dict													=	time.time()
		# update config with task specific params
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)
		if prefix is None:
				__UpperCAmelCase    :     List[Any]													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		for examples_chunk in tqdm(list(chunks(_UpperCAmelCase ,   _UpperCAmelCase						)						)						):
				__UpperCAmelCase    :     Any													=	[prefix + text for text in examples_chunk]
				__UpperCAmelCase    :     Union[str, Any]													=	tokenizer(_UpperCAmelCase ,   return_tensors='''pt''' ,   truncation=_UpperCAmelCase ,   padding='''longest'''						).to(_UpperCAmelCase						)
				__UpperCAmelCase    :     Dict													=	model.generate(
				    input_ids=batch.input_ids ,   attention_mask=batch.attention_mask ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     List[Any]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				for hypothesis in dec:
						fout.write(hypothesis + '''\n'''						)
						fout.flush()
		fout.close()
		__UpperCAmelCase    :     Dict													=	int(time.time() - start_time						)  # seconds
		__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,   4						)}

def        a						(      ):







		'''simple docstring'''





		return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)

def        a						(      _UpperCAmelCase							:					Any=True						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	argparse.ArgumentParser()
		parser.add_argument('''model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
		parser.add_argument('''input_path''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument('''save_path''' ,   type=_UpperCAmelCase ,   help='''where to save summaries'''						)
		parser.add_argument('''--reference_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''like cnn_dm/test.target'''						)
		parser.add_argument('''--score_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default='''metrics.json''' ,   help='''where to save metrics'''						)
		parser.add_argument('''--device''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''cuda, cuda:1, cpu etc.'''						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--dump-args''' ,   action='''store_true''' ,   help='''print the custom hparams with the results'''						)
		parser.add_argument(
		    '''--info''' ,   nargs='''?''' ,   type=_UpperCAmelCase ,   const=datetime_now() ,   help=(
		        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
		        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
		    ) ,   )
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	parser.parse_known_args()
		__UpperCAmelCase    :     Optional[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if parsed_args and verbose:
				print(f'parsed the following generate kwargs: {parsed_args}'						)
		__UpperCAmelCase    :     Optional[int]													=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
		if args.n_obs > 0:
				__UpperCAmelCase    :     Optional[int]													=	examples[: args.n_obs]
		Path(args.save_path						).parent.mkdir(exist_ok=_UpperCAmelCase						)

		if args.reference_path is None and Path(args.score_path						).exists():
				warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'						)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

		__UpperCAmelCase    :     int													=	generate_summaries_or_translations(
		    _UpperCAmelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_UpperCAmelCase ,   )

		if args.reference_path is None:
				return {}

		# Compute scores
		__UpperCAmelCase    :     str													=	calculate_bleu if '''translation''' in args.task else calculate_rouge
		__UpperCAmelCase    :     Tuple													=	[x.rstrip() for x in open(args.save_path						).readlines()]
		__UpperCAmelCase    :     int													=	[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_UpperCAmelCase						)]
		__UpperCAmelCase    :     dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
		scores.update(_UpperCAmelCase						)

		if args.dump_args:
				scores.update(_UpperCAmelCase						)
		if args.info:
				__UpperCAmelCase    :     Dict													=	args.info

		if verbose:
				print(_UpperCAmelCase						)

		if args.score_path is not None:
				json.dump(_UpperCAmelCase ,   open(args.score_path ,   '''w'''						)						)

		return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)










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

import requests

def        a						(      _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Any=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	None
		if token is not None:
				__UpperCAmelCase    :     Optional[Any]													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

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

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

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Optional[Any]													=	requests.get(url + f'&page={i + 2}' ,   headers=_UpperCAmelCase						).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        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	None
		if token is not None:
				__UpperCAmelCase    :     Tuple													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     int													=	f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
		__UpperCAmelCase    :     str													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase						).json()
		__UpperCAmelCase    :     List[Any]													=	{}

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

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Dict													=	requests.get(url + f'&page={i + 2}' ,   headers=_UpperCAmelCase						).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        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





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

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

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	[]
		__UpperCAmelCase    :     int													=	[]
		__UpperCAmelCase    :     List[Any]													=	None

		with zipfile.ZipFile(_UpperCAmelCase						) as z:
				for filename in z.namelist():
						if not os.path.isdir(_UpperCAmelCase						):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(_UpperCAmelCase						) as f:
												for line in f:
														__UpperCAmelCase    :     Tuple													=	line.decode('''UTF-8'''						).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		__UpperCAmelCase    :     int													=	line[: line.index(''': '''						)]
																		__UpperCAmelCase    :     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
																__UpperCAmelCase    :     str													=	line[len('''FAILED '''						) :]
																failed_tests.append(_UpperCAmelCase						)
														elif filename == "job_name.txt":
																__UpperCAmelCase    :     Tuple													=	line

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

		__UpperCAmelCase    :     str													=	None
		if job_name and job_links:
				__UpperCAmelCase    :     Any													=	job_links.get(_UpperCAmelCase ,   _UpperCAmelCase						)

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

		return result

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Tuple=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	[]

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

		return errors

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Optional[Any]=None						):







		'''simple docstring'''





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

		__UpperCAmelCase    :     Union[str, Any]													=	dict(sorted(r.items() ,   key=lambda _UpperCAmelCase						: item[1]["count"] ,   reverse=_UpperCAmelCase						)						)
		return r

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	test.split('''::'''						)[0]
		if test.startswith('''tests/models/'''						):
				__UpperCAmelCase    :     Optional[int]													=	test.split('''/'''						)[2]
		else:
				__UpperCAmelCase    :     Optional[Any]													=	None

		return test

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					int=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[(x[0], x[1], get_model(x[2]						)) for x in logs]
		__UpperCAmelCase    :     int													=	[x for x in logs if x[2] is not None]
		__UpperCAmelCase    :     Tuple													=	{x[2] for x in logs}

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

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

def        a						(      _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	'''| no. | error | status |'''
		__UpperCAmelCase    :     List[str]													=	'''|-:|:-|:-|'''
		__UpperCAmelCase    :     Union[str, Any]													=	[header, sep]
		for error in reduced_by_error:
				__UpperCAmelCase    :     Tuple													=	reduced_by_error[error]['''count''']
				__UpperCAmelCase    :     int													=	f'| {count} | {error[:1_00]} |  |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	'''| model | no. of errors | major error | count |'''
		__UpperCAmelCase    :     List[Any]													=	'''|-:|-:|-:|-:|'''
		__UpperCAmelCase    :     Tuple													=	[header, sep]
		for model in reduced_by_model:
				__UpperCAmelCase    :     List[Any]													=	reduced_by_model[model]['''count''']
				__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	list(reduced_by_model[model]['''errors'''].items()						)[0]
				__UpperCAmelCase    :     Dict													=	f'| {model} | {count} | {error[:60]} | {_count} |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)


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.")
				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   			=parser.parse_args()

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

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

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

				# print the top 30 most common test errors
				__A   			=counter.most_common(3_0)
				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   			=reduce_by_error(errors)
				__A   			=reduce_by_model(errors)

				__A   			=make_github_table(reduced_by_error)
				__A   			=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)





