


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 __snake_case (				):
 lowerCamelCase_								=  Dataset.from_dict({"i": range(17		)}		)
 lowerCamelCase_								=  len(_SCREAMING_SNAKE_CASE		)
 lowerCamelCase_								=  3
 lowerCamelCase_								=  [
     split_dataset_by_node(_SCREAMING_SNAKE_CASE ,    rank=_SCREAMING_SNAKE_CASE ,    world_size=_SCREAMING_SNAKE_CASE		) for rank in range(_SCREAMING_SNAKE_CASE		)
 ]
 assert sum(len(_SCREAMING_SNAKE_CASE		) 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 __snake_case (				):
 def gen():
  return ({"i": i} for i in range(17		))

 lowerCamelCase_								=  3
 lowerCamelCase_								=  IterableDataset.from_generator(_SCREAMING_SNAKE_CASE		)
 lowerCamelCase_								=  len(list(_SCREAMING_SNAKE_CASE		)		)
 lowerCamelCase_								=  [
     split_dataset_by_node(_SCREAMING_SNAKE_CASE ,    rank=_SCREAMING_SNAKE_CASE ,    world_size=_SCREAMING_SNAKE_CASE		) for rank in range(_SCREAMING_SNAKE_CASE		)
 ]
 assert sum(len(list(_SCREAMING_SNAKE_CASE		)		) 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 __snake_case (				UpperCAmelCase_						:						Dict		):
 def gen(UpperCAmelCase_						:						List[str]		):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17		))

 lowerCamelCase_								=  3
 lowerCamelCase_								=  shards_per_node * world_size
 lowerCamelCase_								=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(_SCREAMING_SNAKE_CASE		)]}
 lowerCamelCase_								=  IterableDataset.from_generator(_SCREAMING_SNAKE_CASE ,    gen_kwargs=_SCREAMING_SNAKE_CASE		)
 lowerCamelCase_								=  len(list(_SCREAMING_SNAKE_CASE		)		)
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase_								=  [
     split_dataset_by_node(_SCREAMING_SNAKE_CASE ,    rank=_SCREAMING_SNAKE_CASE ,    world_size=_SCREAMING_SNAKE_CASE		) for rank in range(_SCREAMING_SNAKE_CASE		)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(_SCREAMING_SNAKE_CASE		)		) 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 __snake_case (				):
 def gen():
  return ({"i": i} for i in range(17		))

 lowerCamelCase_								=  2
 lowerCamelCase_								=  IterableDataset.from_generator(_SCREAMING_SNAKE_CASE		)
 lowerCamelCase_								=  len(list(_SCREAMING_SNAKE_CASE		)		)

 lowerCamelCase_								=  split_dataset_by_node(_SCREAMING_SNAKE_CASE ,    rank=0 ,    world_size=_SCREAMING_SNAKE_CASE		).shuffle(seed=42		)
 assert len(list(_SCREAMING_SNAKE_CASE		)		) == 1 + full_size // world_size
 with pytest.raises(_SCREAMING_SNAKE_CASE		):
  split_dataset_by_node(_SCREAMING_SNAKE_CASE ,    rank=0 ,    world_size=_SCREAMING_SNAKE_CASE		).shuffle()

 lowerCamelCase_								=  split_dataset_by_node(full_ds.shuffle(seed=42		) ,    rank=0 ,    world_size=_SCREAMING_SNAKE_CASE		)
 assert len(list(_SCREAMING_SNAKE_CASE		)		) == 1 + full_size // world_size
 with pytest.raises(_SCREAMING_SNAKE_CASE		):
  split_dataset_by_node(full_ds.shuffle() ,    rank=0 ,    world_size=_SCREAMING_SNAKE_CASE		)





@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 __snake_case (				UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  2
 lowerCamelCase_								=  get_torch_dist_unique_port()
 lowerCamelCase_								=  Path(_SCREAMING_SNAKE_CASE		).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase_								=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase_								=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase_								=  [sys.executable] + distributed_args + args
 execute_subprocess_async(_SCREAMING_SNAKE_CASE ,    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 __snake_case (				UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						List[Any]		):
 lowerCamelCase_								=  True
 lowerCamelCase_								=  get_torch_dist_unique_port()
 lowerCamelCase_								=  Path(_SCREAMING_SNAKE_CASE		).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase_								=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase_								=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase_								=  [sys.executable] + distributed_args + args
 execute_subprocess_async(_SCREAMING_SNAKE_CASE ,    env=os.environ.copy()		)







'''simple docstring'''

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





def __snake_case (				UpperCAmelCase_						:						str		):

 def remove_articles(UpperCAmelCase_						:						Optional[Any]		):
  return re.sub(r"\b(a|an|the)\b" ,    " " ,    UpperCAmelCase_		)

 def white_space_fix(UpperCAmelCase_						:						List[str]		):
  return " ".join(text.split()		)

 def remove_punc(UpperCAmelCase_						:						Any		):
  lowerCamelCase_								=  set(string.punctuation		)
  return "".join(ch for ch in text if ch not in exclude		)

 def lower(UpperCAmelCase_						:						List[str]		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_		)		)		)		)





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						int		):
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  Counter(UpperCAmelCase_		) & Counter(UpperCAmelCase_		)
 lowerCamelCase_								=  sum(common.values()		)
 if num_same == 0:
  return 0
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  (2 * precision * recall) / (precision + recall)
 return fa





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						List[Any]		):
 return normalize_answer(UpperCAmelCase_		) == normalize_answer(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  []
 for ground_truth in ground_truths:
  lowerCamelCase_								=  metric_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
  scores_for_ground_truths.append(UpperCAmelCase_		)
 return max(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Dict		):
 lowerCamelCase_								=  lowerCamelCase_								=  lowerCamelCase_								=  0
 lowerCamelCase_								=  []
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    lowerCamelCase_								=  F'''Unanswered question {qa["id"]} will receive score 0.'''
    print(UpperCAmelCase_ ,    file=sys.stderr		)
    continue

   lowerCamelCase_								=  [x["text"] for x in qa["answers"]]
   lowerCamelCase_								=  predictions[qa["id"]]

   lowerCamelCase_								=  metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)
   if int(_exact_match		) == 1:
    correct_ids.append(qa["id"]		)
   exact_match += _exact_match

   fa += metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  exact_match / total
 lowerCamelCase_								=  fa / total

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


if __name__ == "__main__":
     a_  :    Optional[int]      =			"""1.0"""
     a_  :    Dict      =			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.""")
     a_  :    Optional[int]      =			parser.parse_args()

     with open(args.data_file) as data_file:
          a_  :    Any      =			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,
               )
          a_  :    Any      =			dataset_json["""data"""]

     with open(args.pred_file) as pred_file:
          a_  :    List[str]      =			json.load(pred_file)

     a_     ,       a_  :    int      =			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)



