






"""simple docstring"""




import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCamelCase :      Any										=				[
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Any=True       ):
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]



@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=UpperCamelCase_						)						)
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		None
					a_     =		None



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Optional[Any]							,		__A					:  Optional[int]	):
						with TemporaryDirectory() as tmp_dir:
							snake_case__			:				Optional[Any]       =  dataset_module_factory(__A							,		cache_dir=__A	)

							snake_case__			:				Tuple       =  import_main_class(dataset_module.module_path							,		dataset=__A	)

							snake_case__			:				DatasetBuilder       =  builder_cls(
							    cache_dir=__A							,		config_name=__A							,		hash=dataset_module.hash							,		)

							snake_case__			:				int       =  "/".join(
							    [
							        HF_GCP_BASE_URL,
							        builder_instance._relative_data_dir(with_hash=__A	).replace(os.sep							,		"/"	),
							        config.DATASET_INFO_FILENAME,
							    ]	)
							snake_case__			:				str       =  cached_path(__A							,		cache_dir=__A	)
							self.assertTrue(os.path.exists(__A	)	)







@pytest.mark.integration
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__			:				Dict       =  tmp_path_factory.mktemp("test_hf_gcp"       ) / "test_wikipedia_simple"
	snake_case__			:				Optional[int]       =  dataset_module_factory("wikipedia"					,  cache_dir=snake_case_       )
	snake_case__			:				Union[str, Any]       =  import_main_class(dataset_module.module_path       )
	snake_case__			:				DatasetBuilder       =  builder_cls(
	    cache_dir=snake_case_					,  config_name="20220301.frr"					,  hash=dataset_module.hash					,  )
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	snake_case__			:				Any       =  None
	builder_instance.download_and_prepare()
	snake_case__			:				Tuple       =  builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]       ):
	snake_case__			:				Tuple       =  dataset_module_factory("wikipedia"					,  cache_dir=snake_case_       )
	snake_case__			:				Optional[int]       =  import_main_class(dataset_module.module_path					,  dataset=snake_case_       )
	snake_case__			:				DatasetBuilder       =  builder_cls(
	    cache_dir=snake_case_					,  config_name="20220301.frr"					,  hash=dataset_module.hash					,  )
	snake_case__			:				Dict       =  builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(snake_case_					,  snake_case_       )
	assert "train" in ds
	assert isinstance(ds["train"]					,  snake_case_       )
	assert next(iter(ds["train"]       )       )











import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		PhobertTokenizer
					a_     =		False



					def 	_lowercase					( self					:  List[str]	):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						snake_case__			:				Optional[int]       =  ["T@@", "i", "I", "R@@", "r", "e@@"]
						snake_case__			:				int       =  dict(zip(__A							,		range(len(__A	)	)	)	)
						snake_case__			:				Dict       =  ["#version: 0.2", "l à</w>"]
						snake_case__			:				Optional[Any]       =  {"unk_token": "<unk>"}

						snake_case__			:				Tuple       =  os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["vocab_file"]	)
						snake_case__			:				Union[str, Any]       =  os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["merges_file"]	)

						with open(self.vocab_file							,		"w"							,		encoding="utf-8"	) as fp:
							for token in vocab_tokens:
								fp.write(f'''{token} {vocab_tokens[token]}\n'''	)
						with open(self.merges_file							,		"w"							,		encoding="utf-8"	) as fp:
							fp.write("\n".join(__A	)	)



					def 	_lowercase					( self					:  List[str]							,		**__A					:  Any	):
						kwargs.update(self.special_tokens_map	)
						return PhobertTokenizer.from_pretrained(self.tmpdirname							,		**__A	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  List[Any]	):
						snake_case__			:				str       =  "Tôi là VinAI Research"
						snake_case__			:				int       =  "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
						return input_text, output_text






					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				int       =  PhobertTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map	)
						snake_case__			:				Tuple       =  "Tôi là VinAI Research"
						snake_case__			:				List[Any]       =  "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
						snake_case__			:				int       =  tokenizer.tokenize(__A	)
						print(__A	)
						self.assertListEqual(__A							,		__A	)

						snake_case__			:				Any       =  tokens + [tokenizer.unk_token]

						snake_case__			:				Any       =  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)							,		__A	)



