




'''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    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


     a_    :       str    	=     Dataset.from_dict({"""i""": range(17							)}							)
     a_    :       Union[str, Any]    	=     len(UpperCamelCase__							)
     a_    :       Optional[int]    	=     3
     a_    :       str    	=     [
         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    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


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

     a_    :       Union[str, Any]    	=     3
     a_    :       List[str]    	=     IterableDataset.from_generator(UpperCamelCase__							)
     a_    :       List[Any]    	=     len(list(UpperCamelCase__							)							)
     a_    :       Union[str, Any]    	=     [
         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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]							):




     """simple docstring"""


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

     a_    :       str    	=     3
     a_    :       Optional[int]    	=     shards_per_node * world_size
     a_    :       Optional[int]    	=     {"""shards""": [F"shard_{shard_idx}.txt" for shard_idx in range(UpperCamelCase__							)]}
     a_    :       Any    	=     IterableDataset.from_generator(UpperCamelCase__   ,						gen_kwargs=UpperCamelCase__							)
     a_    :       Union[str, Any]    	=     len(list(UpperCamelCase__							)							)
     assert full_ds.n_shards == world_size * shards_per_node
     a_    :       Optional[int]    	=     [
         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    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


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

     a_    :       Optional[Any]    	=     2
     a_    :       List[Any]    	=     IterableDataset.from_generator(UpperCamelCase__							)
     a_    :       str    	=     len(list(UpperCamelCase__							)							)

     a_    :       Any    	=     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()

     a_    :       Union[str, Any]    	=     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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				List[Any]							):




     """simple docstring"""


     a_    :       Union[str, Any]    	=     2
     a_    :       str    	=     get_torch_dist_unique_port()
     a_    :       str    	=     Path(UpperCamelCase__							).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
     a_    :       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()
     a_    :       Union[str, Any]    	=     F"\n        --streaming={streaming}\n    ".split()
     a_    :       str    	=     [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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


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






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase_			:     str							    =     {}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Optional[int]							    =     ['CpmTokenizer']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_			:     Union[str, Any]							    =     ['CpmTokenizerFast']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_cpm import CpmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_cpm_fast import CpmTokenizerFast

else:
 import sys

 lowerCAmelCase_			:     Any							    =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
