




"""simple docstring"""





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   List[str]							     =   Dataset.from_dict({'''i''': range(17						)}						)
						snake_case__    :   int							     =   len(lowerCamelCase						)
						snake_case__    :   str							     =   3
						snake_case__    :   Optional[Any]							     =   [
						    split_dataset_by_node(lowerCamelCase ,     rank=lowerCamelCase ,     world_size=lowerCamelCase						) for rank in range(lowerCamelCase						)
						]
						assert sum(len(lowerCamelCase						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						def gen():
												return ({"i": i} for i in range(17						))

						snake_case__    :   int							     =   3
						snake_case__    :   str							     =   IterableDataset.from_generator(lowerCamelCase						)
						snake_case__    :   List[str]							     =   len(list(lowerCamelCase						)						)
						snake_case__    :   str							     =   [
						    split_dataset_by_node(lowerCamelCase ,     rank=lowerCamelCase ,     world_size=lowerCamelCase						) for rank in range(lowerCamelCase						)
						]
						assert sum(len(list(lowerCamelCase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size

@pytest.mark.parametrize('''shards_per_node''' ,     [1, 2, 3]						)
def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						def gen(lowerCamelCase						):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17						))

						snake_case__    :   Optional[int]							     =   3
						snake_case__    :   Union[str, Any]							     =   shards_per_node * world_size
						snake_case__    :   Any							     =   {'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(lowerCamelCase						)]}
						snake_case__    :   List[str]							     =   IterableDataset.from_generator(lowerCamelCase ,     gen_kwargs=lowerCamelCase						)
						snake_case__    :   Union[str, Any]							     =   len(list(lowerCamelCase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						snake_case__    :   Any							     =   [
						    split_dataset_by_node(lowerCamelCase ,     rank=lowerCamelCase ,     world_size=lowerCamelCase						) for rank in range(lowerCamelCase						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(lowerCamelCase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						def gen():
												return ({"i": i} for i in range(17						))

						snake_case__    :   List[Any]							     =   2
						snake_case__    :   Dict							     =   IterableDataset.from_generator(lowerCamelCase						)
						snake_case__    :   str							     =   len(list(lowerCamelCase						)						)

						snake_case__    :   Optional[int]							     =   split_dataset_by_node(lowerCamelCase ,     rank=0 ,     world_size=lowerCamelCase						).shuffle(seed=42						)
						assert len(list(lowerCamelCase						)						) == 1 + full_size // world_size
						with pytest.raises(lowerCamelCase						):
												split_dataset_by_node(lowerCamelCase ,     rank=0 ,     world_size=lowerCamelCase						).shuffle()

						snake_case__    :   Optional[int]							     =   split_dataset_by_node(full_ds.shuffle(seed=42						) ,     rank=0 ,     world_size=lowerCamelCase						)
						assert len(list(lowerCamelCase						)						) == 1 + full_size // world_size
						with pytest.raises(lowerCamelCase						):
												split_dataset_by_node(full_ds.shuffle() ,     rank=0 ,     world_size=lowerCamelCase						)

@pytest.mark.parametrize('''streaming''' ,     [False, True]						)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,     reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Optional[int]							     =   2
						snake_case__    :   Optional[Any]							     =   get_torch_dist_unique_port()
						snake_case__    :   Dict							     =   Path(lowerCamelCase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
						snake_case__    :   Optional[int]							     =   f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
						snake_case__    :   List[Any]							     =   f'''
        --streaming={streaming}
    '''.split()
						snake_case__    :   Dict							     =   [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase ,     env=os.environ.copy()						)

@pytest.mark.parametrize(
    '''nproc_per_node, num_workers''' ,     [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ] ,     )
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,     reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   True
						snake_case__    :   Union[str, Any]							     =   get_torch_dist_unique_port()
						snake_case__    :   List[Any]							     =   Path(lowerCamelCase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
						snake_case__    :   List[Any]							     =   f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
						snake_case__    :   Optional[int]							     =   f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
						snake_case__    :   Union[str, Any]							     =   [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase ,     env=os.environ.copy()						)








"""simple docstring"""





import argparse
import json
import re
import string
import sys
from collections import Counter

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						def remove_articles(lowerCamelCase						):
												return re.sub(R'''\b(a|an|the)\b''' ,     ''' ''' ,     lowerCamelCase						)

						def white_space_fix(lowerCamelCase						):
												return " ".join(text.split()						)

						def remove_punc(lowerCamelCase						):
												snake_case__    :   Tuple							     =   set(string.punctuation						)
												return "".join(ch for ch in text if ch not in exclude						)

						def lower(lowerCamelCase						):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase						)						)						)						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Dict							     =   normalize_answer(lowerCamelCase						).split()
						snake_case__    :   Dict							     =   normalize_answer(lowerCamelCase						).split()
						snake_case__    :   List[Any]							     =   Counter(lowerCamelCase						) & Counter(lowerCamelCase						)
						snake_case__    :   List[str]							     =   sum(common.values()						)
						if num_same == 0:
												return 0
						snake_case__    :   int							     =   1.0 * num_same / len(lowerCamelCase						)
						snake_case__    :   str							     =   1.0 * num_same / len(lowerCamelCase						)
						snake_case__    :   Union[str, Any]							     =   (2 * precision * recall) / (precision + recall)
						return fa

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						return normalize_answer(lowerCamelCase						) == normalize_answer(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   int							     =   []
						for ground_truth in ground_truths:
												snake_case__    :   Optional[Any]							     =   metric_fn(lowerCamelCase ,     lowerCamelCase						)
												scores_for_ground_truths.append(lowerCamelCase						)
						return max(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Union[str, Any]							     =   0
						snake_case__    :   Any							     =   []
						for passage in dataset:
												for qa in passage["qas"]:
																		total += 1
																		if qa["id"] not in predictions:
																								snake_case__    :   int							     =   f'''Unanswered question {qa['id']} will receive score 0.'''
																								print(lowerCamelCase ,     file=sys.stderr						)
																								continue

																		snake_case__    :   Any							     =   [x['''text'''] for x in qa['''answers''']]
																		snake_case__    :   Dict							     =   predictions[qa['''id''']]

																		snake_case__    :   Union[str, Any]							     =   metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)
																		if int(_exact_match						) == 1:
																								correct_ids.append(qa['''id''']						)
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

						snake_case__    :   str							     =   exact_match / total
						snake_case__    :   Optional[int]							     =   fa / total

						return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
							_lowercase		 =      """1.0"""
							_lowercase		 =      argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
							parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
							parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
							parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
							_lowercase		 =      parser.parse_args()

							with open(args.data_file) as data_file:
														_lowercase		 =      json.load(data_file)
														if dataset_json["version"] != expected_version:
																					print(
																					    f"""Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}""",
																					    file=sys.stderr,
																					)
														_lowercase		 =      dataset_json["""data"""]

							with open(args.pred_file) as pred_file:
														_lowercase		 =      json.load(pred_file)

							_lowercase ,_lowercase		 =      evaluate(dataset, predictions)

							if args.output_correct_ids:
														print(f"""Output {len(correct_ids)} correctly answered question IDs.""")
														with open("""correct_ids.json""", """w""") as f:
																					json.dump(correct_ids, f)


