
import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case					:							Optional[int]           =							logging.get_logger(__name__)

__snake_case					:							Dict           =							{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__snake_case					:							Tuple           =							{
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__snake_case					:							int           =							{
    'microsoft/biogpt': 1_024,
}




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Optional[Any] 		=    set()
			UpperCAmelCase__		:int 		=    word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)							)
						UpperCAmelCase__		:str 		=    char
			return pairs




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    Optional[Any]												=			VOCAB_FILES_NAMES
						__a     :    List[Any]												=			PRETRAINED_VOCAB_FILES_MAP
						__a     :    List[Any]												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__a     :    Dict												=			["""input_ids""", """attention_mask"""]


						def __init__(  self  , A  , A  , A="<unk>"  , A="<s>"  , A="</s>"  , A="</s>"  , A="<pad>"  , **A  , )  ->Union[str, Any]:
									super().__init__(
									    bos_token=A  , eos_token=A  , sep_token=A  , unk_token=A  , pad_token=A  , **A  , )

									try:
												import sacremoses
									except ImportError:
												raise ImportError(
												    'You need to install sacremoses to use BioGptTokenizer. '
												    'See https://pypi.org/project/sacremoses/ for installation.'	)

									UpperCAmelCase__		:List[str] 		=    'en'
									UpperCAmelCase__		:List[str] 		=    sacremoses
									# cache of sm.MosesTokenizer instance
									UpperCAmelCase__		:Tuple 		=    {}
									UpperCAmelCase__		:List[str] 		=    {}
									with open(A  , encoding='utf-8'	) as vocab_handle:
												UpperCAmelCase__		:Optional[Any] 		=    json.load(A	)
									UpperCAmelCase__		:List[Any] 		=    {v: k for k, v in self.encoder.items()}
									with open(A  , encoding='utf-8'	) as merges_handle:
												UpperCAmelCase__		:Union[str, Any] 		=    merges_handle.read().split('\n'	)[:-1]
									UpperCAmelCase__		:List[str] 		=    [tuple(merge.split()[:2]	) for merge in merges]
									UpperCAmelCase__		:Dict 		=    dict(zip(A  , range(len(A	)	)	)	)
									UpperCAmelCase__		:Union[str, Any] 		=    {}


						@property
						def    A__       (  self	)  ->str:
									return len(self.encoder	)


						def    A__       (  self	)  ->Any:
									return dict(self.encoder  , **self.added_tokens_encoder	)


						def    A__       (  self  , A  , A	)  ->List[str]:
									if lang not in self.cache_moses_tokenizer:
												UpperCAmelCase__		:Dict 		=    self.sm.MosesTokenizer(lang=A	)
												UpperCAmelCase__		:List[Any] 		=    moses_tokenizer
									return self.cache_moses_tokenizer[lang].tokenize(
									    A  , aggressive_dash_splits=A  , return_str=A  , escape=A	)


						def    A__       (  self  , A  , A	)  ->List[Any]:
									if lang not in self.cache_moses_detokenizer:
												UpperCAmelCase__		:Tuple 		=    self.sm.MosesDetokenizer(lang=A	)
												UpperCAmelCase__		:Dict 		=    moses_detokenizer
									return self.cache_moses_detokenizer[lang].detokenize(A	)


						def    A__       (  self  , A	)  ->Any:
									UpperCAmelCase__		:List[Any] 		=    tuple(token[:-1]	) + (token[-1] + '</w>',)
									if token in self.cache:
												return self.cache[token]
									UpperCAmelCase__		:List[str] 		=    get_pairs(A	)

									if not pairs:
												return token + "</w>"

									while True:
												UpperCAmelCase__		:str 		=    min(A  , key=lambda A	: self.bpe_ranks.get(A  , float('inf'	)	)	)
												if bigram not in self.bpe_ranks:
															break
												UpperCAmelCase__		,       UpperCAmelCase__		:Dict 		=    bigram
												UpperCAmelCase__		:Union[str, Any] 		=    []
												UpperCAmelCase__		:Optional[Any] 		=    0
												while i < len(A	):
															try:
																		UpperCAmelCase__		:Tuple 		=    word.index(A  , A	)
															except ValueError:
																		new_word.extend(word[i:]	)
																		break
															else:
																		new_word.extend(word[i:j]	)
																		UpperCAmelCase__		:Optional[int] 		=    j

															if word[i] == first and i < len(A	) - 1 and word[i + 1] == second:
																		new_word.append(first + second	)
																		i += 2
															else:
																		new_word.append(word[i]	)
																		i += 1
												UpperCAmelCase__		:Tuple 		=    tuple(A	)
												UpperCAmelCase__		:Tuple 		=    new_word
												if len(A	) == 1:
															break
												else:
															UpperCAmelCase__		:Tuple 		=    get_pairs(A	)
									UpperCAmelCase__		:Optional[int] 		=    ' '.join(A	)
									if word == "\n  </w>":
												UpperCAmelCase__		:List[Any] 		=    '\n</w>'
									UpperCAmelCase__		:int 		=    word
									return word


						def    A__       (  self  , A  , A=False	)  ->List[Any]:
									if bypass_tokenizer:
												UpperCAmelCase__		:Dict 		=    text.split()
									else:
												UpperCAmelCase__		:Tuple 		=    self.moses_tokenize(A  , self.lang	)

									UpperCAmelCase__		:str 		=    []
									for token in text:
												if token:
															split_tokens.extend(list(self.bpe(A	).split(' '	)	)	)

									return split_tokens


						def    A__       (  self  , A	)  ->Optional[int]:
									return self.encoder.get(A  , self.encoder.get(self.unk_token	)	)


						def    A__       (  self  , A	)  ->List[Any]:
									return self.decoder.get(A  , self.unk_token	)


						def    A__       (  self  , A	)  ->List[str]:
									UpperCAmelCase__		:List[Any] 		=    [t.replace(' '  , ''	).replace('</w>'  , ' '	) for t in tokens]
									UpperCAmelCase__		:Optional[Any] 		=    ''.join(A	).split()
									# detokenize
									UpperCAmelCase__		:Union[str, Any] 		=    self.moses_detokenize(A  , self.lang	)
									return text


						def    A__       (  self  , A  , A = None	)  ->List[int]:
									if token_ids_a is None:
												return [self.sep_token_id] + token_ids_a
									UpperCAmelCase__		:List[Any] 		=    [self.sep_token_id]
									return sep + token_ids_a + sep + token_ids_a


						def    A__       (  self  , A  , A = None  , A = False	)  ->List[int]:
									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=A  , token_ids_a=A  , already_has_special_tokens=A	)
									# no bos used in fairseq
									if token_ids_a is not None:
												return [1] + ([0] * len(A	)) + [1] + ([0] * len(A	))
									return [1] + ([0] * len(A	))


						def    A__       (  self  , A  , A = None	)  ->List[int]:
									UpperCAmelCase__		:List[Any] 		=    [self.sep_token_id]

									# no bos used in fairseq
									if token_ids_a is None:
												return len(token_ids_a + sep	) * [0]
									return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]


						def    A__       (  self  , A  , A = None	)  ->Tuple[str]:
									if not os.path.isdir(A	):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
												return
									UpperCAmelCase__		:Any 		=    os.path.join(
									    A  , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
									UpperCAmelCase__		:Union[str, Any] 		=    os.path.join(
									    A  , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

									with open(A  , 'w'  , encoding='utf-8'	) as f:
												f.write(json.dumps(self.encoder  , indent=2  , sort_keys=A  , ensure_ascii=A	) + '\n'	)

									UpperCAmelCase__		:Tuple 		=    0
									with open(A  , 'w'  , encoding='utf-8'	) as writer:
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  , key=lambda A	: kv[1]	):
															if index != token_index:
																		logger.warning(
																		    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																		    ' Please check that the tokenizer is not corrupted!'	)
																		UpperCAmelCase__		:Optional[Any] 		=    token_index
															writer.write(' '.join(A	) + '\n'	)
															index += 1

									return vocab_file, merge_file


						def __getstate__(  self	)  ->Optional[int]:
									UpperCAmelCase__		:Tuple 		=    self.__dict__.copy()
									UpperCAmelCase__		:Any 		=    None
									return state


						def __setstate__(  self  , A	)  ->Dict:
									UpperCAmelCase__		:Any 		=    d

									try:
												import sacremoses
									except ImportError:
												raise ImportError(
												    'You need to install sacremoses to use XLMTokenizer. '
												    'See https://pypi.org/project/sacremoses/ for installation.'	)

									UpperCAmelCase__		:Dict 		=    sacremoses







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 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    Dataset.from_dict({'i': range(17							)}							)
			UpperCAmelCase__		:Optional[Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    3
			UpperCAmelCase__		:List[str] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(SCREAMING_SNAKE_CASE							) 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 		A						(  ):




			"""simple docstring"""


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

			UpperCAmelCase__		:Union[str, Any] 		=    3
			UpperCAmelCase__		:Union[str, Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Dict 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) 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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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

			UpperCAmelCase__		:int 		=    3
			UpperCAmelCase__		:Any 		=    shards_per_node * world_size
			UpperCAmelCase__		:int 		=    {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(SCREAMING_SNAKE_CASE							)]}
			UpperCAmelCase__		:Optional[Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE     ,						gen_kwargs=SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			assert full_ds.n_shards == world_size * shards_per_node
			UpperCAmelCase__		:Optional[Any] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) 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 		A						(  ):




			"""simple docstring"""


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

			UpperCAmelCase__		:Tuple 		=    2
			UpperCAmelCase__		:List[str] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:str 		=    len(list(SCREAMING_SNAKE_CASE							)							)

			UpperCAmelCase__		:List[str] 		=    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle(seed=42							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle()

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




@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 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    2
			UpperCAmelCase__		:str 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Optional[int] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Any 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Optional[int] 		=    f"""
        --streaming={streaming}
    """.split()
			UpperCAmelCase__		:Optional[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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





