
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


SCREAMING_SNAKE_CASE__     :    Any      							=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					{"vocab_file": "vocab.txt"}

SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					{
    "vocab_file": {
        "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt",
        "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt",
        "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt",
        "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt",
    }
}

SCREAMING_SNAKE_CASE__     :    Any      							=					{
    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,
}

SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					{
    "tau/splinter-base": {"do_lower_case": False},
    "tau/splinter-base-qass": {"do_lower_case": False},
    "tau/splinter-large": {"do_lower_case": False},
    "tau/splinter-large-qass": {"do_lower_case": False},
}

class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Union[str, Any]   						=	VOCAB_FILES_NAMES
		a__ :		Union[str, Any]   						=	PRETRAINED_VOCAB_FILES_MAP
		a__ :		Dict   						=	PRETRAINED_INIT_CONFIGURATION
		a__ :		Optional[int]   						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__ :		Tuple   						=	SplinterTokenizer






		def __init__(	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=None							,    SCREAMING_SNAKE_CASE__	:							str=None							,    SCREAMING_SNAKE_CASE__	:							List[str]=True							,    SCREAMING_SNAKE_CASE__	:							Optional[int]="[UNK]"							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]="[SEP]"							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]="[PAD]"							,    SCREAMING_SNAKE_CASE__	:							Dict="[CLS]"							,    SCREAMING_SNAKE_CASE__	:							int="[MASK]"							,    SCREAMING_SNAKE_CASE__	:							List[str]="[QUESTION]"							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=True							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=None							,    **SCREAMING_SNAKE_CASE__	:							Tuple							,    )  ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,    tokenizer_file=SCREAMING_SNAKE_CASE__							,    do_lower_case=SCREAMING_SNAKE_CASE__							,    unk_token=SCREAMING_SNAKE_CASE__							,    sep_token=SCREAMING_SNAKE_CASE__							,    pad_token=SCREAMING_SNAKE_CASE__							,    cls_token=SCREAMING_SNAKE_CASE__							,    mask_token=SCREAMING_SNAKE_CASE__							,    tokenize_chinese_chars=SCREAMING_SNAKE_CASE__							,    strip_accents=SCREAMING_SNAKE_CASE__							,    additional_special_tokens=(question_token,)							,    **SCREAMING_SNAKE_CASE__							,    )

						__lowerCamelCase					      =			json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
						if (
						    pre_tok_state.get('''lowercase'''							,    SCREAMING_SNAKE_CASE__						) != do_lower_case
						    or pre_tok_state.get('''strip_accents'''							,    SCREAMING_SNAKE_CASE__						) != strip_accents
						):
										__lowerCamelCase					      =			getattr(SCREAMING_SNAKE_CASE__							,    pre_tok_state.pop('''type'''						)						)
										__lowerCamelCase					      =			do_lower_case
										__lowerCamelCase					      =			strip_accents
										__lowerCamelCase					      =			pre_tok_class(**SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			do_lower_case






		@property
		def 				__A (	self	:							Optional[Any]						)  ->							Union[str, Any]:
						return self.convert_tokens_to_ids(self.question_token						)






		def 				__A (	self	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							List[int]							,    SCREAMING_SNAKE_CASE__	:							Optional[List[int]] = None						)  ->							List[int]:
						if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						__lowerCamelCase					      =			[self.cls_token_id]
						__lowerCamelCase					      =			[self.sep_token_id]
						__lowerCamelCase					      =			[self.question_token_id] + [self.convert_tokens_to_ids('''.'''						)]
						if self.padding_side == "right":
										# Input is question-then-context
										return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
						else:
										# Input is context-then-question
										return cls + token_ids_a + sep + token_ids_a + question_suffix + sep






		def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							List[int]							,    SCREAMING_SNAKE_CASE__	:							Optional[List[int]] = None						)  ->							List[int]:
						__lowerCamelCase					      =			[self.sep_token_id]
						__lowerCamelCase					      =			[self.cls_token_id]
						__lowerCamelCase					      =			[self.question_token_id] + [self.convert_tokens_to_ids('''.'''						)]
						if token_ids_a is None:
										return len(cls + token_ids_a + sep						) * [0]

						if self.padding_side == "right":
										# Input is question-then-context
										return len(cls + token_ids_a + question_suffix + sep						) * [0] + len(token_ids_a + sep						) * [1]
						else:
										# Input is context-then-question
										return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + question_suffix + sep						) * [1]







		def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							str							,    SCREAMING_SNAKE_CASE__	:							Optional[str] = None						)  ->							Tuple[str]:
						__lowerCamelCase					      =			self._tokenizer.model.save(SCREAMING_SNAKE_CASE__							,    name=SCREAMING_SNAKE_CASE__						)
						return tuple(SCREAMING_SNAKE_CASE__						)





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 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			Dataset.from_dict({'''i''': range(17							)}							)
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(__lowerCAmelCase							) 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 				__magic_name__				(     )    ->     Optional[Any]:
				def gen():
								return ({"i": i} for i in range(17							))

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

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			shards_per_node * world_size
				__lowerCamelCase					      =			{'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(__lowerCAmelCase							)]}
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase	,						gen_kwargs=__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				assert full_ds.n_shards == world_size * shards_per_node
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(__lowerCAmelCase							)							) 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 				__magic_name__				(     )    ->     Optional[int]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)

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

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




@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 				__magic_name__				(     __lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						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 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     int:
				__lowerCamelCase					      =			True
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)



