





import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin
class      UpperCamelCase_						(					a__							,					unittest.TestCase   ):






					"""simple docstring"""




					lowerCAmelCase_							=		PhobertTokenizer
					lowerCAmelCase_							=		False




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										_snake_case													=			['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
										_snake_case													=			dict(zip(_lowerCamelCase			,  range(len(_lowerCamelCase					)					)					)					)
										_snake_case													=			['''#version: 0.2''', '''l à</w>''']
										_snake_case													=			{'''unk_token''': '''<unk>'''}

										_snake_case													=			os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES['vocab_file']					)
										_snake_case													=			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(_lowerCamelCase					)					)




					def 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							Union[str, Any]:
										kwargs.update(self.special_tokens_map					)
										return PhobertTokenizer.from_pretrained(self.tmpdirname			,  **_lowerCamelCase					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										_snake_case													=			'''Tôi là VinAI Research'''
										_snake_case													=			'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
										return input_text, output_text






					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			PhobertTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map					)
										_snake_case													=			'''Tôi là VinAI Research'''
										_snake_case													=			'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
										_snake_case													=			tokenizer.tokenize(_lowerCamelCase					)
										print(_lowerCamelCase					)
										self.assertListEqual(_lowerCamelCase			,  _lowerCamelCase					)

										_snake_case													=			tokens + [tokenizer.unk_token]

										_snake_case													=			[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(_lowerCamelCase					)			,  _lowerCamelCase					)










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


UpperCAmelCase_ 							=       [
    {"""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   lowerCamelCase__       (       UpperCamelCase__     :    Dict=True  )     ->	Dict:



					'''simple docstring'''




					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=_lowerCamelCase   )   )
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		None
					lowerCAmelCase_							=		None




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										with TemporaryDirectory() as tmp_dir:
															_snake_case													=			dataset_module_factory(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)

															_snake_case													=			import_main_class(dataset_module.module_path			,  dataset=lowerCAmelCase_					)

															_snake_case													=			builder_cls(
															    cache_dir=lowerCAmelCase_			,  config_name=lowerCAmelCase_			,  hash=dataset_module.hash			,  )

															_snake_case													=			'/'.join(
															    [
															        HF_GCP_BASE_URL,
															        builder_instance._relative_data_dir(with_hash=lowerCAmelCase_					).replace(os.sep			,  '/'					),
															        config.DATASET_INFO_FILENAME,
															    ]					)
															_snake_case													=			cached_path(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)
															self.assertTrue(os.path.exists(lowerCAmelCase_					)					)





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Any  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      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													=			None
					builder_instance.download_and_prepare()
					_snake_case													=			builder_instance.as_dataset()
					assert ds





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path    ,      dataset=UpperCamelCase__  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      config_name='20220301.frr'    ,      hash=dataset_module.hash    ,      )
					_snake_case													=			builder_instance.as_streaming_dataset()
					assert ds
					assert isinstance(UpperCamelCase__    ,      UpperCamelCase__  )
					assert "train" in ds
					assert isinstance(ds['train']    ,      UpperCamelCase__  )
					assert next(iter(ds['train']  )  )



