




"""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:
    SCREAMING_SNAKE_CASE_      =					None


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =					{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_      =					{
    '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',
    },
}

SCREAMING_SNAKE_CASE_      =					{
    'facebook/xglm-564M': 2048,
}

class 							snake_case_ (				lowerCamelCase__ ):







   """simple docstring"""




   A_										=					VOCAB_FILES_NAMES
   A_										=					PRETRAINED_VOCAB_FILES_MAP
   A_										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_										=					['input_ids', 'attention_mask']
   A_										=					XGLMTokenizer







   def __init__(		self					,				lowerCamelCase_=None					,				lowerCamelCase_=None					,				lowerCamelCase_="<s>"					,				lowerCamelCase_="</s>"					,				lowerCamelCase_="</s>"					,				lowerCamelCase_="<s>"					,				lowerCamelCase_="<unk>"					,				lowerCamelCase_="<pad>"					,				**lowerCamelCase_					,				)							->      Any:
       UpperCamelCase										=  7
       UpperCamelCase										=  [F'<madeupword{i}>' for i in range(self.num_madeup_words)]

       UpperCamelCase										=  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__(
           lowercase_					,				tokenizer_file=lowercase_					,				bos_token=lowercase_					,				eos_token=lowercase_					,				sep_token=lowercase_					,				cls_token=lowercase_					,				unk_token=lowercase_					,				pad_token=lowercase_					,				**lowercase_					,				)

       UpperCamelCase										=  vocab_file
       UpperCamelCase										=  False if not self.vocab_file else True







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      List[int]:






       if token_ids_a is None:
           return [self.sep_token_id] + token_ids_a
       UpperCamelCase										=  [self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      List[int]:
       UpperCamelCase										=  [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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      Tuple[str]:






       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(lowercase_):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory.')
           return
       UpperCamelCase										=  os.path.join(
           lowercase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

       return (out_vocab_file,)





"""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     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  Dataset.from_dict({'''i''': range(17						)}						)
    UpperCamelCase										=  len(_lowercase						)
    UpperCamelCase										=  3
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(_lowercase						) 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     __snake_case   (							):


    """simple docstring"""







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

    UpperCamelCase										=  3
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(list(_lowercase						)						) 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     __snake_case   (							_lowercase						):


    """simple docstring"""







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

    UpperCamelCase										=  3
    UpperCamelCase										=  shards_per_node * world_size
    UpperCamelCase										=  {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(_lowercase						)]}
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase ,gen_kwargs=_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    assert full_ds.n_shards == world_size * shards_per_node
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(_lowercase						)						) 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     __snake_case   (							):


    """simple docstring"""







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

    UpperCamelCase										=  2
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)

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

    UpperCamelCase										=  split_dataset_by_node(full_ds.shuffle(seed=42						) ,rank=0 ,world_size=_lowercase						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(full_ds.shuffle() ,rank=0 ,world_size=_lowercase						)





@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     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  2
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,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     __snake_case   (							_lowercase ,_lowercase						):


    """simple docstring"""







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