"""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    :							Union[str, 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 							_UpperCAmelCase		(       lowerCamelCase__=True     ):





		"""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=__UpperCamelCase			)			)
class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Dict			=    None
					UpperCamelCase_ :	Dict			=    None




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							with TemporaryDirectory() as tmp_dir:
									lowerCAmelCase__        =   dataset_module_factory(snake_case__  ,						cache_dir=snake_case__     )

									lowerCAmelCase__        =   import_main_class(dataset_module.module_path  ,						dataset=snake_case__     )

									lowerCAmelCase__        =   builder_cls(
									    cache_dir=snake_case__  ,						config_name=snake_case__  ,						hash=dataset_module.hash  ,						)

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







@pytest.mark.integration
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   tmp_path_factory.mktemp("""test_hf_gcp"""     ) / """test_wikipedia_simple"""
		lowerCAmelCase__        =   dataset_module_factory("""wikipedia"""			,  cache_dir=lowerCamelCase__     )
		lowerCAmelCase__        =   import_main_class(dataset_module.module_path     )
		lowerCAmelCase__        =   builder_cls(
		    cache_dir=lowerCamelCase__			,  config_name="""20220301.frr"""			,  hash=dataset_module.hash			,  )
		# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
		lowerCAmelCase__        =   None
		builder_instance.download_and_prepare()
		lowerCAmelCase__        =   builder_instance.as_dataset()
		assert ds







@pytest.mark.integration
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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





"""simple docstring"""




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

							lowerCAmelCase__        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(snake_case__  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[7, 4, 5, 10, 8, 9]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass




