
"""simple docstring"""




import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_=0.01			,					A_=1000							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Any   			=  p_stop
         _lowercase:      List[Any]   			=  max_length



       def __iter__(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      str   			=  0
         _lowercase:      List[str]   			=  False
         while not stop and count < self.max_length:
           yield count
           count += 1
           _lowercase:      Any   			=  random.random() < self.p_stop


class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self			,					A_			,					A_			,					A_=False			,					A_=True							)  ->						Any:





         """simple docstring"""




         _lowercase:      List[Any]   			=  [
             BatchSamplerShard(A_			,					2			,					A_			,					split_batches=A_			,					even_batches=A_							)
             for i in range(2							)
         ]
         _lowercase:      Optional[int]   			=  [list(A_							) for batch_sampler_shard in batch_sampler_shards]
         if not split_batches:
           self.assertListEqual([len(A_							) for shard in batch_sampler_shards]			,					[len(A_							) for e in expected]							)
         self.assertListEqual(A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[str]   			=  BatchSampler(range(24							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      List[Any]   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
         ]
         self.check_batch_sampler_shards(A_			,					A_							)

         _lowercase:      str   			=  BatchSampler(range(24							)			,					batch_size=3			,					drop_last=A_							)
         # Expected shouldn't change
         self.check_batch_sampler_shards(A_			,					A_							)

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         _lowercase:      Any   			=  BatchSampler(range(21							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Optional[Any]   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
         ]
         self.check_batch_sampler_shards(A_			,					A_							)

         _lowercase:      Optional[int]   			=  BatchSampler(range(21							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      str   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_							)

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         _lowercase:      Dict   			=  BatchSampler(range(22							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      List[Any]   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
         ]
         self.check_batch_sampler_shards(A_			,					A_							)

         _lowercase:      List[Any]   			=  BatchSampler(range(22							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Union[str, Any]   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_							)

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         _lowercase:      Optional[int]   			=  BatchSampler(range(20							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      int   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
         ]
         self.check_batch_sampler_shards(A_			,					A_							)

         _lowercase:      Optional[Any]   			=  BatchSampler(range(20							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Optional[int]   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_							)

         # Check the shards when the dataset is very small.
         _lowercase:      Optional[int]   			=  BatchSampler(range(2							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      int   			=  [[[0, 1, 0]], [[1, 0, 1]]]
         self.check_batch_sampler_shards(A_			,					A_							)

         _lowercase:      Dict   			=  BatchSampler(range(2							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Optional[int]   			=  [[], []]
         self.check_batch_sampler_shards(A_			,					A_							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  BatchSampler(range(24							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      List[Any]   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)

         _lowercase:      str   			=  BatchSampler(range(24							)			,					batch_size=4			,					drop_last=A_							)
         # Expected shouldn't change
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)

         # Check the shards when the dataset is not a round multiple of batch size.
         _lowercase:      Any   			=  BatchSampler(range(22							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      int   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)

         _lowercase:      List[str]   			=  BatchSampler(range(22							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      str   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         _lowercase:      Optional[Any]   			=  BatchSampler(range(21							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Dict   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)

         _lowercase:      Any   			=  BatchSampler(range(21							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      str   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)

         # Check the shards when the dataset is very small.
         _lowercase:      List[str]   			=  BatchSampler(range(2							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Optional[int]   			=  [[[0, 1]], [[0, 1]]]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)

         _lowercase:      List[str]   			=  BatchSampler(range(2							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      str   			=  [[], []]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      str   			=  BatchSampler(range(24							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      str   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         _lowercase:      List[str]   			=  BatchSampler(range(24							)			,					batch_size=3			,					drop_last=A_							)
         # Expected shouldn't change
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         # Check the shards when the dataset is a round multiple of batch size but not total batch size.
         _lowercase:      str   			=  BatchSampler(range(21							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      str   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         _lowercase:      Optional[Any]   			=  BatchSampler(range(21							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Any   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
         # num_processes batch.
         _lowercase:      Dict   			=  BatchSampler(range(22							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Tuple   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         _lowercase:      Any   			=  BatchSampler(range(22							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Dict   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         _lowercase:      Union[str, Any]   			=  BatchSampler(range(20							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      Any   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         _lowercase:      Dict   			=  BatchSampler(range(20							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      List[Any]   			=  [
             [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
             [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         # Check the shards when the dataset is very small.
         _lowercase:      int   			=  BatchSampler(range(2							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      List[Any]   			=  [[[0, 1]], []]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)

         _lowercase:      Optional[Any]   			=  BatchSampler(range(2							)			,					batch_size=3			,					drop_last=A_							)
         _lowercase:      str   			=  [[], []]
         self.check_batch_sampler_shards(A_			,					A_			,					even_batches=A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  BatchSampler(range(24							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      int   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)

         _lowercase:      int   			=  BatchSampler(range(24							)			,					batch_size=4			,					drop_last=A_							)
         # Expected shouldn't change
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)

         # Check the shards when the dataset is not a round multiple of batch size.
         _lowercase:      Dict   			=  BatchSampler(range(22							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Dict   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)

         _lowercase:      Dict   			=  BatchSampler(range(22							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Tuple   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)

         # Check the shards when the dataset is not a round multiple of batch size or num_processes.
         _lowercase:      List[Any]   			=  BatchSampler(range(21							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      List[Any]   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)

         _lowercase:      Optional[int]   			=  BatchSampler(range(21							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Any   			=  [
             [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
             [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
         ]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)

         # Check the shards when the dataset is very small.
         _lowercase:      List[str]   			=  BatchSampler(range(2							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Optional[int]   			=  [[[0, 1]], []]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)

         _lowercase:      Dict   			=  BatchSampler(range(2							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Optional[Any]   			=  [[], []]
         self.check_batch_sampler_shards(A_			,					A_			,					split_batches=A_			,					even_batches=A_							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      List[Any]   			=  [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
         _lowercase:      List[str]   			=  [BatchSamplerShard(A_			,					2			,					A_			,					even_batches=A_							) for i in range(2							)]

         self.assertEqual(len(batch_sampler_shards[0]							)			,					3							)
         self.assertEqual(len(batch_sampler_shards[1]							)			,					2							)

         self.assertListEqual(list(batch_sampler_shards[0]							)			,					[[0, 1, 2], [5, 6, 7, 8], [12, 13]]							)
         self.assertListEqual(list(batch_sampler_shards[1]							)			,					[[3, 4], [9, 10, 11]]							)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_=False			,					A_=2			,					A_=False							)  ->						Optional[int]:





         """simple docstring"""




         random.seed(A_							)
         _lowercase:      List[Any]   			=  list(A_							)

         _lowercase:      Optional[int]   			=  [
             IterableDatasetShard(
                 A_			,					batch_size=A_			,					drop_last=A_			,					num_processes=A_			,					process_index=A_			,					split_batches=A_			,					)
             for i in range(A_							)
         ]
         _lowercase:      Tuple   			=  []
         for iterable_dataset_shard in iterable_dataset_shards:
           # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
           random.seed(A_							)
           iterable_dataset_lists.append(list(A_							)							)

         _lowercase:      Dict   			=  batch_size // num_processes if split_batches else batch_size
         # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
         _lowercase:      Optional[int]   			=  iterable_dataset_lists[0]
         for l in iterable_dataset_lists[1:]:
           self.assertEqual(len(A_							)			,					len(A_							)							)
           self.assertTrue(len(A_							) % shard_batch_size == 0							)

         _lowercase:      Optional[Any]   			=  []
         for idx in range(0			,					len(A_							)			,					A_							):
           for l in iterable_dataset_lists:
             observed += l[idx : idx + shard_batch_size]

         if not drop_last:
           while len(A_							) < len(A_							):
             reference += reference
         self.assertListEqual(A_			,					reference[: len(A_							)]							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      int   			=  42
         _lowercase:      int   			=  RandomIterableDataset()

         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)
         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)
         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)
         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)

         # Edge case with a very small dataset
         _lowercase:      List[Any]   			=  RandomIterableDataset(max_length=2							)

         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)
         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)
         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)
         self.check_iterable_dataset_shards(A_			,					A_			,					batch_size=4			,					drop_last=A_			,					split_batches=A_							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      str   			=  BatchSampler(range(16							)			,					batch_size=4			,					drop_last=A_							)
         _lowercase:      Union[str, Any]   			=  SkipBatchSampler(A_			,					2							)
         self.assertListEqual(list(A_							)			,					[[8, 9, 10, 11], [12, 13, 14, 15]]							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  SkipDataLoader(list(range(16							)							)			,					batch_size=4			,					skip_batches=2							)
         self.assertListEqual([t.tolist() for t in dataloader]			,					[[8, 9, 10, 11], [12, 13, 14, 15]]							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      List[Any]   			=  DataLoader(list(range(16							)							)			,					batch_size=4							)
         _lowercase:      Dict   			=  skip_first_batches(A_			,					num_batches=2							)
         self.assertListEqual([t.tolist() for t in new_dataloader]			,					[[8, 9, 10, 11], [12, 13, 14, 15]]							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  DataLoaderShard(list(range(16							)							)			,					batch_size=4							)
         for idx, _ in enumerate(A_							):
           self.assertEqual(dataloader.end_of_dataloader			,					idx == 3							)

         # Test it also works on the second iteration
         for idx, _ in enumerate(A_							):
           self.assertEqual(dataloader.end_of_dataloader			,					idx == 3							)



       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         Accelerator()
         _lowercase:      Optional[int]   			=  DataLoaderDispatcher(range(16							)			,					batch_size=4							)
         for idx, _ in enumerate(A_							):
           self.assertEqual(dataloader.end_of_dataloader			,					idx == 3							)

         # Test it also works on the second iteration
         for idx, _ in enumerate(A_							):
           self.assertEqual(dataloader.end_of_dataloader			,					idx == 3							)


"""simple docstring"""




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A__ :       Any				= [
    'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
    ' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
    ' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
    'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
    ' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
    ' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
    ' body.',
    'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
    ' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
    ' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
    ' punishment.',
]

A__ :       Tuple				= [
    'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
    ' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
    ' had informed his Lufthansa training school of an episode of severe depression, airline says .',
    'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
    ' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
    ' Israelis .',
    'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
    ' death . Organization claims that governments around the world are using the threat of terrorism to advance'
    ' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
    ' sentences up by 28% .',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Any   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''', '''rougeL''']    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					bootstrap_aggregation=_UpperCamelCase			,					rouge_keys=['''rouge2''']    )
  assert (
      pd.DataFrame(no_aggregation['''rouge2''']    ).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra['''rouge2''']    ).fmeasure.mean()
  )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  '''rougeLsum'''
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  _lowercase:      List[str]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=[k]    )[k]
  assert score > score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Tuple   			=  ['''rouge1''', '''rouge2''', '''rougeL''']
  _lowercase:      Dict   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase			,					rouge_keys=_UpperCamelCase    )
  assert score_sep == score_no_sep




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  [
      '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
  ]
  _lowercase:      Union[str, Any]   			=  [
      '''Margot Frank, died in 1945, a month earlier than previously thought.''',
      '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
      ''' the final seconds on board Flight 9525.''',
  ]
  assert calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    ) == calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					newline_sep=_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      int   			=  [
      '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
  ]
  _lowercase:      Union[str, Any]   			=  [
      ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
  ]

  _lowercase:      List[Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']			,					newline_sep=_UpperCamelCase    )['''rougeLsum''']
  _lowercase:      Union[str, Any]   			=  calculate_rouge(_UpperCamelCase			,					_UpperCamelCase			,					rouge_keys=['''rougeLsum''']    )['''rougeLsum''']
  assert new_score > prev_score







def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  Path('''examples/seq2seq/test_data/wmt_en_ro'''    )
  _lowercase:      int   			=  calculate_rouge_path(data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  calculate_rouge_path(
      data_dir.joinpath('''test.source'''    )			,					data_dir.joinpath('''test.target'''    )			,					bootstrap_aggregation=_UpperCamelCase    )
  assert isinstance(_UpperCamelCase			,					_UpperCamelCase    )
