



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

     A__				=   3
     A__				=   IterableDataset.from_generator(__a      )
     A__				=   len(list(__a      )      )
     A__				=   [
         split_dataset_by_node(__a		,							rank=__a		,							world_size=__a      ) for rank in range(__a      )
     ]
     assert sum(len(list(__a      )      ) 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   a_	(			__a      ):
     def gen(__a      ):
          for shard in shards:
               yield from ({"i": i, "shard": shard} for i in range(17      ))

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

     A__				=   2
     A__				=   IterableDataset.from_generator(__a      )
     A__				=   len(list(__a      )      )

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

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



@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   a_	(			__a      ):
     A__				=   2
     A__				=   get_torch_dist_unique_port()
     A__				=   Path(__a      ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
     A__				=   f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
     A__				=   f'''
        --streaming={streaming}
    '''.split()
     A__				=   [sys.executable] + distributed_args + args
     execute_subprocess_async(__a		,							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   a_	(			__a		,							__a      ):
     A__				=   True
     A__				=   get_torch_dist_unique_port()
     A__				=   Path(__a      ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
     A__				=   f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
     A__				=   f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
     A__				=   [sys.executable] + distributed_args + args
     execute_subprocess_async(__a		,							env=os.environ.copy()      )








"""simple docstring"""







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case	:				List[str]  					=			logging.getLogger()



def   a_	(			):
     A__				=   argparse.ArgumentParser()
     parser.add_argument('''-f'''      )
     A__				=   parser.parse_args()
     return args.f

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   logging.StreamHandler(sys.stdout )
          logger.addHandler(_lowerCamelCase )

          A__				=   self.get_auto_remove_tmp_dir()
          A__				=   F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

          with patch.object(_lowerCamelCase	,      '''argv'''	,      _lowerCamelCase ):
               A__				=   run_glue_with_pabee.main()
               for value in result.values():
                    self.assertGreaterEqual(_lowerCamelCase	,      0.75 )



