





'''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 						__UpperCamelCase						(			):
		__UpperCAmelCase	:							Any     =     Dataset.from_dict({"i": range(17  )}  )
		__UpperCAmelCase	:							List[str]     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     3
		__UpperCAmelCase	:							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 						__UpperCamelCase						(			):
		def gen():
				return ({"i": i} for i in range(17  ))

		__UpperCAmelCase	:							Tuple     =     3
		__UpperCAmelCase	:							List[Any]     =     IterableDataset.from_generator(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     len(list(_UpperCAmelCase  )  )
		__UpperCAmelCase	:							int     =     [
		    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 						__UpperCamelCase						(			_UpperCAmelCase  ):
		def gen(_UpperCAmelCase  ):
				for shard in shards:
						yield from ({"i": i, "shard": shard} for i in range(17  ))

		__UpperCAmelCase	:							Dict     =     3
		__UpperCAmelCase	:							Tuple     =     shards_per_node * world_size
		__UpperCAmelCase	:							Any     =     {"shards": [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCAmelCase  )]}
		__UpperCAmelCase	:							Tuple     =     IterableDataset.from_generator(_UpperCAmelCase,       gen_kwargs=_UpperCAmelCase  )
		__UpperCAmelCase	:							Optional[int]     =     len(list(_UpperCAmelCase  )  )
		assert full_ds.n_shards == world_size * shards_per_node
		__UpperCAmelCase	:							List[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 						__UpperCamelCase						(			):
		def gen():
				return ({"i": i} for i in range(17  ))

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

		__UpperCAmelCase	:							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()

		__UpperCAmelCase	:							List[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 						__UpperCamelCase						(			_UpperCAmelCase  ):
		__UpperCAmelCase	:							List[str]     =     2
		__UpperCAmelCase	:							List[Any]     =     get_torch_dist_unique_port()
		__UpperCAmelCase	:							Any     =     Path(_UpperCAmelCase  ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		__UpperCAmelCase	:							Union[str, Any]     =     F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
		__UpperCAmelCase	:							Any     =     F"\n        --streaming={streaming}\n    ".split()
		__UpperCAmelCase	:							Union[str, 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 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							Tuple     =     True
		__UpperCAmelCase	:							Optional[Any]     =     get_torch_dist_unique_port()
		__UpperCAmelCase	:							Dict     =     Path(_UpperCAmelCase  ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		__UpperCAmelCase	:							Tuple     =     F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
		__UpperCAmelCase	:							Union[str, Any]     =     F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
		__UpperCAmelCase	:							int     =     [sys.executable] + distributed_args + args
		execute_subprocess_async(_UpperCAmelCase,       env=os.environ.copy()  )











'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xglm import XGLMTokenizer
else:
			lowerCAmelCase__      :   List[str]      	=			None


lowerCAmelCase__      :   Union[str, Any]      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   Tuple      	=			{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__      :   List[str]      	=			{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase__      :   int      	=			{
    "facebook/xglm-564M": 20_48,
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE							=	PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE							=	['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE							=	XGLMTokenizer
					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      str="<s>"		,	UpperCAmelCase_       :      List[Any]="</s>"		,	UpperCAmelCase_       :      Dict="</s>"		,	UpperCAmelCase_       :      Optional[Any]="<s>"		,	UpperCAmelCase_       :      List[str]="<unk>"		,	UpperCAmelCase_       :      Union[str, Any]="<pad>"		,	**UpperCAmelCase_       :      List[Any]		,	):







							"""simple docstring"""
							# Compatibility with the original tokenizer
							__UpperCAmelCase	:							Union[str, Any]     =     7
							__UpperCAmelCase	:							Tuple     =     [f"<madeupword{i}>" for i in range(self.num_madeup_words		)]

							__UpperCAmelCase	:							List[Any]     =     kwargs.get("additional_special_tokens"		,	[]		)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    UpperCAmelCase_		,	tokenizer_file=UpperCAmelCase_		,	bos_token=UpperCAmelCase_		,	eos_token=UpperCAmelCase_		,	sep_token=UpperCAmelCase_		,	cls_token=UpperCAmelCase_		,	unk_token=UpperCAmelCase_		,	pad_token=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Dict     =     vocab_file
							__UpperCAmelCase	:							Optional[int]     =     False if not self.vocab_file else True
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""

							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							__UpperCAmelCase	:							Any     =     [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     [self.sep_token_id]

							if token_ids_a is None:
									return len(sep + token_ids_a		) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      Optional[str] = None		):







							"""simple docstring"""
							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."		)

							if not os.path.isdir(UpperCAmelCase_		):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
									return
							__UpperCAmelCase	:							str     =     os.path.join(
							    UpperCAmelCase_		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase_		):
									copyfile(self.vocab_file		,	UpperCAmelCase_		)

							return (out_vocab_file,)




