



"""simple docstring"""







import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


A		=						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						if "token" in model_name_or_path:
												return "rag_token"
						if "sequence" in model_name_or_path:
												return "rag_sequence"
						if "bart" in model_name_or_path:
												return "bart"
						return None

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						return max(metric_fn(lowerCamelCase_     ,		lowerCamelCase_ ) for gt in ground_truths )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			Any						    = [line.strip() for line in open(lowerCamelCase_     ,		"r" ).readlines()]
						snake_case   :			int						    = []

						if args.gold_data_mode == "qa":
												snake_case   :			int						    = pd.read_csv(lowerCamelCase_     ,		sep="\t"     ,		header=lowerCamelCase_ )
												for answer_list in data[1]:
																		snake_case   :			str						    = ast.literal_eval(lowerCamelCase_ )
																		answers.append(lowerCamelCase_ )
						else:
												snake_case   :			Tuple						    = [line.strip() for line in open(lowerCamelCase_     ,		"r" ).readlines()]
												snake_case   :			Optional[Any]						    = [[reference] for reference in references]

						snake_case   :			int						    = 0
						for prediction, ground_truths in zip(lowerCamelCase_     ,		lowerCamelCase_ ):
												total += 1
												em += metric_max_over_ground_truths(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												fa += metric_max_over_ground_truths(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )

						snake_case   :			Optional[Any]						    = 1_0_0.0 * em / total
						snake_case   :			List[Any]						    = 1_0_0.0 * fa / total

						logger.info(f'''F1: {fa:.2f}''' )
						logger.info(f'''EM: {em:.2f}''' )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any]     ,		lowerCamelCase_:							List[Any]     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = args.k
						snake_case   :			Dict						    = [line.strip() for line in open(lowerCamelCase_     ,		"r" ).readlines()]
						snake_case   :			Any						    = [line.strip() for line in open(lowerCamelCase_     ,		"r" ).readlines()]

						snake_case   :			Optional[int]						    = 0
						for hypo, reference in zip(lowerCamelCase_     ,		lowerCamelCase_ ):
												snake_case   :			Any						    = set(hypo.split("\t" )[:k] )
												snake_case   :			int						    = set(reference.split("\t" ) )
												total += 1
												em += len(hypo_provenance & ref_provenance ) / k

						snake_case   :			Union[str, Any]						    = 1_0_0.0 * em / total
						logger.info(f'''Precision@{k}: {em: .2f}''' )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						def strip_title(lowerCamelCase_:							List[Any] ):
												if title.startswith("\"" ):
																		snake_case   :			List[Any]						    = title[1:]
												if title.endswith("\"" ):
																		snake_case   :			Dict						    = title[:-1]
												return title

						snake_case   :			Union[str, Any]						    = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    lowerCamelCase_     ,		return_tensors="pt"     ,		padding=lowerCamelCase_     ,		truncation=lowerCamelCase_     ,		)["input_ids"].to(args.device )

						snake_case   :			Any						    = rag_model.rag.question_encoder(lowerCamelCase_ )
						snake_case   :			List[Any]						    = question_enc_outputs[0]

						snake_case   :			Union[str, Any]						    = rag_model.retriever(
						    lowerCamelCase_     ,		question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy()     ,		prefix=rag_model.rag.generator.config.prefix     ,		n_docs=rag_model.config.n_docs     ,		return_tensors="pt"     ,		)
						snake_case   :			Any						    = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
						snake_case   :			Any						    = []
						for docs in all_docs:
												snake_case   :			Dict						    = [strip_title(lowerCamelCase_ ) for title in docs["title"]]
												provenance_strings.append("\t".join(lowerCamelCase_ ) )
						return provenance_strings

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple ):
						"""simple docstring"""






						with torch.no_grad():
												snake_case   :			str						    = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    lowerCamelCase_     ,		return_tensors="pt"     ,		padding=lowerCamelCase_     ,		truncation=lowerCamelCase_ )

												snake_case   :			Optional[Any]						    = inputs_dict.input_ids.to(args.device )
												snake_case   :			Optional[int]						    = inputs_dict.attention_mask.to(args.device )
												snake_case   :			Dict						    = rag_model.generate(  # rag_model overwrites generate
												    lowerCamelCase_     ,		attention_mask=lowerCamelCase_     ,		num_beams=args.num_beams     ,		min_length=args.min_length     ,		max_length=args.max_length     ,		early_stopping=lowerCamelCase_     ,		num_return_sequences=1     ,		bad_words_ids=[[0, 0]]     ,		)
												snake_case   :			Optional[int]						    = rag_model.retriever.generator_tokenizer.batch_decode(lowerCamelCase_     ,		skip_special_tokens=lowerCamelCase_ )

												if args.print_predictions:
																		for q, a in zip(lowerCamelCase_     ,		lowerCamelCase_ ):
																								logger.info("Q: {} - A: {}".format(lowerCamelCase_     ,		lowerCamelCase_ ) )

												return answers

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = argparse.ArgumentParser()
						parser.add_argument(
						    "--model_type"     ,		choices=["rag_sequence", "rag_token", "bart"]     ,		type=lowerCamelCase_     ,		help=(
						        "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
						        " model_name_or_path"
						    )     ,		)
						parser.add_argument(
						    "--index_name"     ,		default=lowerCamelCase_     ,		choices=["exact", "compressed", "legacy"]     ,		type=lowerCamelCase_     ,		help="RAG model retriever type"     ,		)
						parser.add_argument(
						    "--index_path"     ,		default=lowerCamelCase_     ,		type=lowerCamelCase_     ,		help="Path to the retrieval index"     ,		)
						parser.add_argument("--n_docs"     ,		default=5     ,		type=lowerCamelCase_     ,		help="Number of retrieved docs" )
						parser.add_argument(
						    "--model_name_or_path"     ,		default=lowerCamelCase_     ,		type=lowerCamelCase_     ,		required=lowerCamelCase_     ,		help="Path to pretrained checkpoints or model identifier from huggingface.co/models"     ,		)
						parser.add_argument(
						    "--eval_mode"     ,		choices=["e2e", "retrieval"]     ,		default="e2e"     ,		type=lowerCamelCase_     ,		help=(
						        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
						        " precision@k."
						    )     ,		)
						parser.add_argument("--k"     ,		default=1     ,		type=lowerCamelCase_     ,		help="k for the precision@k calculation" )
						parser.add_argument(
						    "--evaluation_set"     ,		default=lowerCamelCase_     ,		type=lowerCamelCase_     ,		required=lowerCamelCase_     ,		help="Path to a file containing evaluation samples"     ,		)
						parser.add_argument(
						    "--gold_data_path"     ,		default=lowerCamelCase_     ,		type=lowerCamelCase_     ,		required=lowerCamelCase_     ,		help="Path to a tab-separated file with gold samples"     ,		)
						parser.add_argument(
						    "--gold_data_mode"     ,		default="qa"     ,		type=lowerCamelCase_     ,		choices=["qa", "ans"]     ,		help=(
						        "Format of the gold data file"
						        "qa - a single line in the following format: question [tab] answer_list"
						        "ans - a single line of the gold file contains the expected answer string"
						    )     ,		)
						parser.add_argument(
						    "--predictions_path"     ,		type=lowerCamelCase_     ,		default="predictions.txt"     ,		help="Name of the predictions file, to be stored in the checkpoints directory"     ,		)
						parser.add_argument(
						    "--eval_all_checkpoints"     ,		action="store_true"     ,		help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"     ,		)
						parser.add_argument(
						    "--eval_batch_size"     ,		default=8     ,		type=lowerCamelCase_     ,		help="Batch size per GPU/CPU for evaluation."     ,		)
						parser.add_argument(
						    "--recalculate"     ,		help="Recalculate predictions even if the prediction file exists"     ,		action="store_true"     ,		)
						parser.add_argument(
						    "--num_beams"     ,		default=4     ,		type=lowerCamelCase_     ,		help="Number of beams to be used when generating answers"     ,		)
						parser.add_argument("--min_length"     ,		default=1     ,		type=lowerCamelCase_     ,		help="Min length of the generated answers" )
						parser.add_argument("--max_length"     ,		default=5_0     ,		type=lowerCamelCase_     ,		help="Max length of the generated answers" )

						parser.add_argument(
						    "--print_predictions"     ,		action="store_true"     ,		help="If True, prints predictions while evaluating."     ,		)
						parser.add_argument(
						    "--print_docs"     ,		action="store_true"     ,		help="If True, prints docs retried while generating."     ,		)
						snake_case   :			List[Any]						    = parser.parse_args()
						snake_case   :			int						    = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
						return args

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






						snake_case   :			Any						    = {}
						if args.model_type is None:
												snake_case   :			Optional[int]						    = infer_model_type(args.model_name_or_path )
												assert args.model_type is not None
						if args.model_type.startswith("rag" ):
												snake_case   :			int						    = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
												snake_case   :			Dict						    = args.n_docs
												if args.index_name is not None:
																		snake_case   :			Optional[int]						    = args.index_name
												if args.index_path is not None:
																		snake_case   :			List[Any]						    = args.index_path
						else:
												snake_case   :			List[str]						    = BartForConditionalGeneration

						snake_case   :			Any						    = (
						    [f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
						    if args.eval_all_checkpoints
						    else [args.model_name_or_path]
						)

						logger.info("Evaluate the following checkpoints: %s"     ,		lowerCamelCase_ )

						snake_case   :			Dict						    = get_scores if args.eval_mode == "e2e" else get_precision_at_k
						snake_case   :			Optional[Any]						    = evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

						for checkpoint in checkpoints:
												if os.path.exists(args.predictions_path ) and (not args.recalculate):
																		logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path ) )
																		score_fn(lowerCamelCase_     ,		args.predictions_path     ,		args.gold_data_path )
																		continue

												logger.info("***** Running evaluation for {} *****".format(lowerCamelCase_ ) )
												logger.info("  Batch size = %d"     ,		args.eval_batch_size )
												logger.info("  Predictions will be stored under {}".format(args.predictions_path ) )

												if args.model_type.startswith("rag" ):
																		snake_case   :			Any						    = RagRetriever.from_pretrained(lowerCamelCase_     ,		**lowerCamelCase_ )
																		snake_case   :			Union[str, Any]						    = model_class.from_pretrained(lowerCamelCase_     ,		retriever=lowerCamelCase_     ,		**lowerCamelCase_ )
																		model.retriever.init_retrieval()
												else:
																		snake_case   :			Union[str, Any]						    = model_class.from_pretrained(lowerCamelCase_     ,		**lowerCamelCase_ )
												model.to(args.device )

												with open(args.evaluation_set     ,		"r" ) as eval_file, open(args.predictions_path     ,		"w" ) as preds_file:
																		snake_case   :			List[Any]						    = []
																		for line in tqdm(lowerCamelCase_ ):
																								questions.append(line.strip() )
																								if len(lowerCamelCase_ ) == args.eval_batch_size:
																														snake_case   :			Dict						    = evaluate_batch_fn(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )
																														preds_file.write("\n".join(lowerCamelCase_ ) + "\n" )
																														preds_file.flush()
																														snake_case   :			Any						    = []
																		if len(lowerCamelCase_ ) > 0:
																								snake_case   :			Tuple						    = evaluate_batch_fn(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )
																								preds_file.write("\n".join(lowerCamelCase_ ) )
																								preds_file.flush()

																		score_fn(lowerCamelCase_     ,		args.predictions_path     ,		args.gold_data_path )


if __name__ == "__main__":
			A		=						get_args()
			main(args)








"""simple docstring"""







import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('3.8'):
			import importlib_metadata
else:
			import importlib.metadata as importlib_metadata

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							List[Any]=False ):
						"""simple docstring"""






						try:
												snake_case   :			Optional[int]						    = os.environ[key]
						except KeyError:
												# KEY isn't set, default to `default`.
												snake_case   :			List[Any]						    = default
						else:
												# KEY is set, convert it to True or False.
												try:
																		snake_case   :			Dict						    = strtobool(lowerCamelCase_ )
												except ValueError:
																		# More values are supported, but let's keep the message simple.
																		raise ValueError(f'''If set, {key} must be yes or no.''' )
						return _value


A		=						parse_flag_from_env('RUN_SLOW', default=False)
A		=						parse_flag_from_env('RUN_REMOTE', default=False)
A		=						parse_flag_from_env('RUN_LOCAL', default=True)
A		=						parse_flag_from_env('RUN_PACKAGED', default=True)

# Compression
A		=						pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')
A		=						pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')
A		=						pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')

# Audio
A		=						pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),
    reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ',
)

# Beam
A		=						pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),
    reason='test requires apache-beam and a compatible dill version',
)

# Dill-cloudpickle compatibility
A		=						pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('0.3.2'),
    reason='test requires dill>0.3.2 for cloudpickle compatibility',
)

# Windows
A		=						pytest.mark.skipif(
    sys.platform == 'win32',
    reason='test should not be run on Windows',
)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						try:
												import faiss  # noqa
						except ImportError:
												snake_case   :			Dict						    = unittest.skip("test requires faiss" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						try:
												import regex  # noqa
						except ImportError:
												snake_case   :			List[Any]						    = unittest.skip("test requires regex" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						try:
												import elasticsearch  # noqa
						except ImportError:
												snake_case   :			Tuple						    = unittest.skip("test requires elasticsearch" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						try:
												import sqlalchemy  # noqa
						except ImportError:
												snake_case   :			Dict						    = unittest.skip("test requires sqlalchemy" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						if not config.TORCH_AVAILABLE:
												snake_case   :			Dict						    = unittest.skip("test requires PyTorch" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						if not config.TF_AVAILABLE:
												snake_case   :			Tuple						    = unittest.skip("test requires TensorFlow" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						if not config.JAX_AVAILABLE:
												snake_case   :			str						    = unittest.skip("test requires JAX" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						if not config.PIL_AVAILABLE:
												snake_case   :			Union[str, Any]						    = unittest.skip("test requires Pillow" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						try:
												import transformers  # noqa F401
						except ImportError:
												return unittest.skip("test requires transformers" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
						"""simple docstring"""






						try:
												import tiktoken  # noqa F401
						except ImportError:
												return unittest.skip("test requires tiktoken" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						try:
												import spacy  # noqa F401
						except ImportError:
												return unittest.skip("test requires spacy" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						def _require_spacy_model(lowerCamelCase_:							Dict ):
												try:
																		import spacy  # noqa F401

																		spacy.load(lowerCamelCase_ )
												except ImportError:
																		return unittest.skip("test requires spacy" )(lowerCamelCase_ )
												except OSError:
																		return unittest.skip("test requires spacy model '{}'".format(lowerCamelCase_ ) )(lowerCamelCase_ )
												else:
																		return test_case

						return _require_spacy_model

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
						"""simple docstring"""






						try:
												import pyspark  # noqa F401
						except ImportError:
												return unittest.skip("test requires pyspark" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						try:
												import joblibspark  # noqa F401
						except ImportError:
												return unittest.skip("test requires joblibspark" )(lowerCamelCase_ )
						else:
												return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						if not _run_slow_tests or _run_slow_tests == 0:
												snake_case   :			Dict						    = unittest.skip("test is slow" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any ):
						"""simple docstring"""






						if not _run_local_tests or _run_local_tests == 0:
												snake_case   :			int						    = unittest.skip("test is local" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
						"""simple docstring"""






						if not _run_packaged_tests or _run_packaged_tests == 0:
												snake_case   :			List[str]						    = unittest.skip("test is packaged" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






						if not _run_remote_tests or _run_remote_tests == 0:
												snake_case   :			Union[str, Any]						    = unittest.skip("test requires remote" )(lowerCamelCase_ )
						return test_case

def  __SCREAMING_SNAKE_CASE		(			*lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						def decorate(cls:							int ):
												for name, fn in cls.__dict__.items():
																		if callable(lowerCamelCase_ ) and name.startswith("test" ):
																								for decorator in decorators:
																														snake_case   :			Optional[Any]						    = decorator(lowerCamelCase_ )
																								setattr(cls     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												return cls

						return decorate

class       _a  ( SCREAMING_SNAKE_CASE__):
			pass



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     0
			__magic_name__     					=     1
			__magic_name__     					=     2



@contextmanager
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int=OfflineSimulationMode.CONNECTION_FAILS     ,		lowerCamelCase_:							Dict=1e-16 ):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = requests.Session().request

						def timeout_request(lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							List[Any]     ,		lowerCamelCase_:							str     ,		**lowerCamelCase_:							List[Any] ):
												# Change the url to an invalid url so that the connection hangs
												snake_case   :			Optional[int]						    = "https://10.255.255.1"
												if kwargs.get("timeout" ) is None:
																		raise RequestWouldHangIndefinitelyError(
																		    f'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' )
												snake_case   :			str						    = timeout
												try:
																		return online_request(lowerCamelCase_     ,		lowerCamelCase_     ,		**lowerCamelCase_ )
												except Exception as e:
																		# The following changes in the error are just here to make the offline timeout error prettier
																		snake_case   :			Dict						    = url
																		snake_case   :			Union[str, Any]						    = e.args[0]
																		snake_case   :			Any						    = (max_retry_error.args[0].replace("10.255.255.1"     ,		f'''OfflineMock[{url}]''' ),)
																		snake_case   :			List[Any]						    = (max_retry_error,)
																		raise

						def raise_connection_error(lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							str     ,		**lowerCamelCase_:							int ):
												raise requests.ConnectionError("Offline mode is enabled."     ,		request=lowerCamelCase_ )

						if mode is OfflineSimulationMode.CONNECTION_FAILS:
												with patch("requests.Session.send"     ,		lowerCamelCase_ ):
																		yield
						elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
												# inspired from https://stackoverflow.com/a/904609
												with patch("requests.Session.request"     ,		lowerCamelCase_ ):
																		yield
						elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
												with patch("datasets.config.HF_DATASETS_OFFLINE"     ,		lowerCamelCase_ ):
																		yield
						else:
												raise ValueError("Please use a value from the OfflineSimulationMode enum." )

@contextmanager
def  __SCREAMING_SNAKE_CASE		(			*lowerCamelCase_:							List[str]     ,		**lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Any						    = str(Path().resolve() )
						with tempfile.TemporaryDirectory(*lowerCamelCase_     ,		**lowerCamelCase_ ) as tmp_dir:
												try:
																		os.chdir(lowerCamelCase_ )
																		yield
												finally:
																		os.chdir(lowerCamelCase_ )

@contextmanager
def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						import gc

						gc.collect()
						snake_case   :			Tuple						    = pa.total_allocated_bytes()
						yield
						assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."

@contextmanager
def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						import gc

						gc.collect()
						snake_case   :			Optional[int]						    = pa.total_allocated_bytes()
						yield
						assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						return deepcopy(lowerCamelCase_ ).integers(0     ,		1_0_0     ,		1_0 ).tolist() == deepcopy(lowerCamelCase_ ).integers(0     ,		1_0_0     ,		1_0 ).tolist()

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						import decorator
						from requests.exceptions import HTTPError

						def _wrapper(lowerCamelCase_:							Dict     ,		*lowerCamelCase_:							Tuple     ,		**lowerCamelCase_:							Any ):
												try:
																		return func(*lowerCamelCase_     ,		**lowerCamelCase_ )
												except HTTPError as err:
																		if str(lowerCamelCase_ ).startswith("500" ) or str(lowerCamelCase_ ).startswith("502" ):
																								pytest.xfail(str(lowerCamelCase_ ) )
																		raise err

						return decorator.decorator(_wrapper     ,		lowerCamelCase_ )

class       _a  :



			def __init__(			self							:				str   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				str   ,	_lowercase							:				Optional[Any]		)							->					Optional[int]:
									snake_case   :			List[Any]						    = returncode
									snake_case   :			Tuple						    = stdout
									snake_case   :			Dict						    = stderr



async def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						while True:
												snake_case   :			List[Any]						    = await stream.readline()
												if line:
																		callback(lowerCamelCase_ )
												else:
																		break

async def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Any     ,		lowerCamelCase_:							Dict=None     ,		lowerCamelCase_:							Union[str, Any]=None     ,		lowerCamelCase_:							int=None     ,		lowerCamelCase_:							str=False     ,		lowerCamelCase_:							Optional[Any]=False ):
						"""simple docstring"""






						if echo:
												print("\nRunning: "     ,		" ".join(lowerCamelCase_ ) )

						snake_case   :			Any						    = await asyncio.create_subprocess_exec(
						    cmd[0]     ,		*cmd[1:]     ,		stdin=lowerCamelCase_     ,		stdout=asyncio.subprocess.PIPE     ,		stderr=asyncio.subprocess.PIPE     ,		env=lowerCamelCase_     ,		)

						# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
						# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
						#
						# If it starts hanging, will need to switch to the following code. The problem is that no data
						# will be seen until it's done and if it hangs for example there will be no debug info.
						# out, err = await p.communicate()
						# return _RunOutput(p.returncode, out, err)

						snake_case   :			Optional[int]						    = []
						snake_case   :			Tuple						    = []

						def tee(lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Union[str, Any]="" ):
												snake_case   :			Optional[Any]						    = line.decode("utf-8" ).rstrip()
												sink.append(lowerCamelCase_ )
												if not quiet:
																		print(lowerCamelCase_     ,		lowerCamelCase_     ,		file=lowerCamelCase_ )

    # XXX: the timeout doesn't seem to make any difference here
						await asyncio.wait(
						    [
						        _read_stream(p.stdout     ,		lambda lowerCamelCase_ : tee(lowerCamelCase_     ,		lowerCamelCase_     ,		sys.stdout     ,		label="stdout:" ) ),
						        _read_stream(p.stderr     ,		lambda lowerCamelCase_ : tee(lowerCamelCase_     ,		lowerCamelCase_     ,		sys.stderr     ,		label="stderr:" ) ),
						    ]     ,		timeout=lowerCamelCase_     ,		)
						return _RunOutput(await p.wait()     ,		lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any]=None     ,		lowerCamelCase_:							Any=None     ,		lowerCamelCase_:							List[str]=1_8_0     ,		lowerCamelCase_:							Dict=False     ,		lowerCamelCase_:							Optional[Any]=True ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = asyncio.get_event_loop()
						snake_case   :			str						    = loop.run_until_complete(
						    _stream_subprocess(lowerCamelCase_     ,		env=lowerCamelCase_     ,		stdin=lowerCamelCase_     ,		timeout=lowerCamelCase_     ,		quiet=lowerCamelCase_     ,		echo=lowerCamelCase_ ) )

						snake_case   :			Union[str, Any]						    = " ".join(lowerCamelCase_ )
						if result.returncode > 0:
												snake_case   :			List[Any]						    = "\n".join(result.stderr )
												raise RuntimeError(
												    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
												    f'''The combined stderr from workers follows:\n{stderr}''' )

						# check that the subprocess actually did run and produced some output, should the test rely on
						# the remote side to do the testing
						if not result.stdout and not result.stderr:
												raise RuntimeError(f'''\'{cmd_str}\' produced no output.''' )

						return result

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			int						    = os.environ.get("PYTEST_XDIST_WORKER"     ,		"gw0" )
						snake_case   :			Dict						    = re.sub(r"^gw"     ,		""     ,		lowerCamelCase_     ,		0     ,		re.M )
						return int(lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			int						    = 2_9_5_0_0
						snake_case   :			str						    = pytest_xdist_worker_id()
						return port + uniq_delta



