





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_    =    [
    {"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=True			)		->						Optional[Any]:






				"""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  __lowerCAmelCase	(      lowerCamelCase__					):
			lowerCamelCase_					:					int	      =				None
			lowerCamelCase_					:					Dict	      =				None






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							with TemporaryDirectory() as tmp_dir:
											snake_case_						:   int           =  dataset_module_factory(__A ,							cache_dir=__A   )

											snake_case_						:   Any           =  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_						:   str           =  '''/'''.join(
											    [
											        HF_GCP_BASE_URL,
											        builder_instance._relative_data_dir(with_hash=__A   ).replace(os.sep ,							'''/'''   ),
											        config.DATASET_INFO_FILENAME,
											    ]   )
											snake_case_						:   Optional[Any]           =  cached_path(__A ,							cache_dir=__A   )
											self.assertTrue(os.path.exists(__A   )   )






@pytest.mark.integration
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  tmp_path_factory.mktemp('''test_hf_gcp'''			) / '''test_wikipedia_simple'''
				snake_case_						:   Any           =  dataset_module_factory('''wikipedia'''			,    cache_dir=_UpperCamelCase			)
				snake_case_						:   List[str]           =  import_main_class(dataset_module.module_path			)
				snake_case_						:   DatasetBuilder           =  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_						:   Any           =  None
				builder_instance.download_and_prepare()
				snake_case_						:   Dict           =  builder_instance.as_dataset()
				assert ds






@pytest.mark.integration
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:   Dict           =  dataset_module_factory('''wikipedia'''			,    cache_dir=_UpperCamelCase			)
				snake_case_						:   int           =  import_main_class(dataset_module.module_path			,    dataset=_UpperCamelCase			)
				snake_case_						:   DatasetBuilder           =  builder_cls(
				    cache_dir=_UpperCamelCase			,    config_name='''20220301.frr'''			,    hash=dataset_module.hash			,    )
				snake_case_						:   Any           =  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''']			)			)







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class  __lowerCAmelCase	(      unittest.TestCase,					_a					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  load_tool('''text-to-speech'''   )
							self.tool.setup()






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Optional[int]           =  self.tool('''hey'''   )
							snake_case_						:   Union[str, Any]           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Any           =  self.tool('''hey'''   )
							snake_case_						:   str           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )
