






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    (   ):




  """simple docstring"""







  __A						      =			Dataset.from_dict({'''i''': range(1_7			)}			)
  __A						      =			len(__UpperCamelCase			)
  __A						      =			3
  __A						      =			[
      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    (   ):




  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(1_7			))

  __A						      =			3
  __A						      =			IterableDataset.from_generator(__UpperCamelCase			)
  __A						      =			len(list(__UpperCamelCase			)			)
  __A						      =			[
      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			):




  """simple docstring"""







  def gen(__UpperCamelCase			):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(1_7			))

  __A						      =			3
  __A						      =			shards_per_node * world_size
  __A						      =			{'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(__UpperCamelCase			)]}
  __A						      =			IterableDataset.from_generator(__UpperCamelCase		,	gen_kwargs=__UpperCamelCase			)
  __A						      =			len(list(__UpperCamelCase			)			)
  assert full_ds.n_shards == world_size * shards_per_node
  __A						      =			[
      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    (   ):




  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(1_7			))

  __A						      =			2
  __A						      =			IterableDataset.from_generator(__UpperCamelCase			)
  __A						      =			len(list(__UpperCamelCase			)			)

  __A						      =			split_dataset_by_node(__UpperCamelCase		,	rank=0		,	world_size=__UpperCamelCase			).shuffle(seed=4_2			)
  assert len(list(__UpperCamelCase			)			) == 1 + full_size // world_size
  with pytest.raises(__UpperCamelCase			):
    split_dataset_by_node(__UpperCamelCase		,	rank=0		,	world_size=__UpperCamelCase			).shuffle()

  __A						      =			split_dataset_by_node(full_ds.shuffle(seed=4_2			)		,	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			):




  """simple docstring"""







  __A						      =			2
  __A						      =			get_torch_dist_unique_port()
  __A						      =			Path(__UpperCamelCase			).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  __A						      =			f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
  __A						      =			f'\n        --streaming={streaming}\n    '.split()
  __A						      =			[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			):




  """simple docstring"""







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












"""simple docstring"""


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  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if is_torch_available():
    import torch

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

  return isinstance(__UpperCamelCase		,	np.ndarray			)





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			fields(self  )

     # Safety and consistency checks
     if not len(_lowerCamelCase  ):
       raise ValueError(f'{self.__class__.__name__} has no fields.'  )

     __A						      =			getattr(self,	class_fields[0].name  )
     __A						      =			all(getattr(self,	field.name  ) is None for field in class_fields[1:]  )

     if other_fields_are_none and isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       for key, value in first_field.items():
         __A						      =			value
     else:
       for field in class_fields:
         __A						      =			getattr(self,	field.name  )
         if v is not None:
           __A						      =			v


   def __delitem__(     self			:					Dict,	*_lowerCamelCase			:					int,	**_lowerCamelCase			:					Any  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	*_lowerCamelCase			:					List[str],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	*_lowerCamelCase			:					Optional[Any],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	*_lowerCamelCase			:					Any,	**_lowerCamelCase			:					Optional[int]  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.'  )


   def __getitem__(     self			:					int,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       __A						      =			dict(self.items()  )
       return inner_dict[k]
     else:
       return self.to_tuple()[k]


   def __setattr__(     self			:					Optional[int],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Union[str, Any]  ):

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


   def __setitem__(     self			:					List[str],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     # Will raise a KeyException if needed
     super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     # Don't call self.__setattr__ to avoid recursion errors
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     return tuple(self[k] for k in self.keys()  )




