

from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available





def  _snake_case		(snake_case_					:     Optional[Any]  )							->		Optional[Any]:
  if is_torch_available():
    import torch

    if isinstance(snake_case_    ,       torch.Tensor  ):
      return True

  return isinstance(snake_case_    ,       np.ndarray  )







class      lowerCamelCase_ (			_lowerCAmelCase):




 def     a       (			self							):
   __UpperCAmelCase	:  Dict		=     fields(self							)

   # Safety and consistency checks
   if not len(_UpperCAmelCase							):
     raise ValueError(F"{self.__class__.__name__} has no fields."							)

   __UpperCAmelCase	:  int		=     getattr(self  ,  class_fields[0].name							)
   __UpperCAmelCase	:  Union[str, Any]		=     all(getattr(self  ,  field.name							) is None for field in class_fields[1:]							)

   if other_fields_are_none and isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
     for key, value in first_field.items():
       __UpperCAmelCase	:  str		=     value
   else:
     for field in class_fields:
       __UpperCAmelCase	:  Optional[Any]		=     getattr(self  ,  field.name							)
       if v is not None:
         __UpperCAmelCase	:  Optional[Any]		=     v




 def __delitem__(			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance."							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance."							)




 def __getitem__(			self  ,  _UpperCAmelCase							):
   if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  List[Any]		=     dict(self.items()							)
     return inner_dict[k]
   else:
     return self.to_tuple()[k]




 def __setattr__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   if name in self.keys() and value is not None:
     # Don't call self.__setitem__ to avoid recursion errors
     super().__setitem__(_UpperCAmelCase  ,  _UpperCAmelCase							)
   super().__setattr__(_UpperCAmelCase  ,  _UpperCAmelCase							)




 def __setitem__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # Will raise a KeyException if needed
   super().__setitem__(_UpperCAmelCase  ,  _UpperCAmelCase							)
   # Don't call self.__setattr__ to avoid recursion errors
   super().__setattr__(_UpperCAmelCase  ,  _UpperCAmelCase							)




 def     a       (			self							):
   return tuple(self[k] for k in self.keys()							)







"""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							():
	__lowerCAmelCase     :		Optional[int]		     = Dataset.from_dict({'i': range(17						)}						)
	__lowerCAmelCase     :		List[Any]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Tuple		     = 3
	__lowerCAmelCase     :		List[Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(_UpperCamelCase						) 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							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[Any]		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		List[Any]		     = len(list(_UpperCamelCase						)						)
	__lowerCAmelCase     :		str		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(list(_UpperCamelCase						)						) 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							(_UpperCamelCase						):
	def gen(_UpperCamelCase						):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[int]		     = shards_per_node * world_size
	__lowerCAmelCase     :		Dict		     = {'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCamelCase						)]}
	__lowerCAmelCase     :		Dict		     = IterableDataset.from_generator(_UpperCamelCase			,    gen_kwargs=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = len(list(_UpperCamelCase						)						)
	assert full_ds.n_shards == world_size * shards_per_node
	__lowerCAmelCase     :		Union[str, Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(_UpperCamelCase						)						) 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							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		str		     = 2
	__lowerCAmelCase     :		Any		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = len(list(_UpperCamelCase						)						)

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

	__lowerCAmelCase     :		List[str]		     = split_dataset_by_node(full_ds.shuffle(seed=42						)			,    rank=0			,    world_size=_UpperCamelCase						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(full_ds.shuffle()			,    rank=0			,    world_size=_UpperCamelCase						)
@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							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = 2
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		List[Any]		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		int		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		Tuple		     = F"\n        --streaming={streaming}\n    ".split()
	__lowerCAmelCase     :		Optional[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    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							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = True
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		Tuple		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		Optional[Any]		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)