'''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				(				)  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :Tuple       =Dataset.from_dict({"""i""": range(17      )}      )
     _UpperCamelCase  :Tuple       =len(__a      )
     _UpperCamelCase  :int       =3
     _UpperCamelCase  :Dict       =[
         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  _lowerCAmelCase				(				)  ->      Any:
     '''simple docstring'''







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

     _UpperCamelCase  :Tuple       =3
     _UpperCamelCase  :List[Any]       =IterableDataset.from_generator(__a      )
     _UpperCamelCase  :Optional[Any]       =len(list(__a      )      )
     _UpperCamelCase  :List[str]       =[
         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  _lowerCAmelCase				(				__a      )  ->      List[Any]:
     '''simple docstring'''







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

     _UpperCamelCase  :Tuple       =3
     _UpperCamelCase  :Optional[int]       =shards_per_node * world_size
     _UpperCamelCase  :str       ={"""shards""": [F'''shard_{shard_idx}.txt''' for shard_idx in range(__a      )]}
     _UpperCamelCase  :List[Any]       =IterableDataset.from_generator(__a			,      gen_kwargs=__a      )
     _UpperCamelCase  :List[str]       =len(list(__a      )      )
     assert full_ds.n_shards == world_size * shards_per_node
     _UpperCamelCase  :Any       =[
         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  _lowerCAmelCase				(				)  ->      Dict:
     '''simple docstring'''







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

     _UpperCamelCase  :Tuple       =2
     _UpperCamelCase  :List[str]       =IterableDataset.from_generator(__a      )
     _UpperCamelCase  :List[Any]       =len(list(__a      )      )

     _UpperCamelCase  :Optional[Any]       =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()

     _UpperCamelCase  :Optional[int]       =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  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =2
     _UpperCamelCase  :List[str]       =get_torch_dist_unique_port()
     _UpperCamelCase  :Any       =Path(__a      ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
     _UpperCamelCase  :int       =F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
     _UpperCamelCase  :Tuple       =F'''
        --streaming={streaming}
    '''.split()
     _UpperCamelCase  :List[Any]       =[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  _lowerCAmelCase				(				__a			,      __a      )  ->      Tuple:
     '''simple docstring'''







     _UpperCamelCase  :Any       =True
     _UpperCamelCase  :List[Any]       =get_torch_dist_unique_port()
     _UpperCamelCase  :Optional[Any]       =Path(__a      ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
     _UpperCamelCase  :Any       =F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
     _UpperCamelCase  :str       =F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
     _UpperCamelCase  :Dict       =[sys.executable] + distributed_args + args
     execute_subprocess_async(__a			,      env=os.environ.copy()      )
'''simple docstring'''

from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCamelCase :				int														= [
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

_lowerCamelCase :				int														= get_logger(__name__)


_lowerCamelCase :				List[str]														= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def  _lowerCAmelCase				(				__a      )  ->      Optional[TaskTemplate]:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =task_template_dict.get("""task"""      )
     if task_name is None:
          logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(__a      )}'''      )
          return None
     _UpperCamelCase  :List[str]       =NAMEaTEMPLATE.get(__a      )
     return template.from_dict(__a      )