



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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict=True				)  ->     Dict:
   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=_a))
class 	lowerCamelCase__	(       _a):



 """simple docstring"""

 a__					: Dict =  None
 a__					: List[str] =  None

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Optional[int]      )   ->	Union[str, Any]:
    with TemporaryDirectory() as tmp_dir:
       _A      =       dataset_module_factory(snake_case_			,						cache_dir=snake_case_      )

       _A      =       import_main_class(dataset_module.module_path			,						dataset=snake_case_      )

       _A      =       builder_cls(
           cache_dir=snake_case_			,						config_name=snake_case_			,						hash=dataset_module.hash			,						)

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

@pytest.mark.integration
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     Tuple:
   _A      =       tmp_path_factory.mktemp('''test_hf_gcp'''				) / """test_wikipedia_simple"""
   _A      =       dataset_module_factory('''wikipedia'''	,   cache_dir=_lowerCAmelCase				)
   _A      =       import_main_class(dataset_module.module_path				)
   _A      =       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
   _A      =       None
   builder_instance.download_and_prepare()
   _A      =       builder_instance.as_dataset()
   assert ds


@pytest.mark.integration
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     List[str]:
   _A      =       dataset_module_factory('''wikipedia'''	,   cache_dir=_lowerCAmelCase				)
   _A      =       import_main_class(dataset_module.module_path	,   dataset=_lowerCAmelCase				)
   _A      =       builder_cls(
       cache_dir=_lowerCAmelCase	,   config_name='''20220301.frr'''	,   hash=dataset_module.hash	,   )
   _A      =       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'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__a  			=						"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
       import torch

       print("Torch version:", torch.__version__)
       print("Cuda available:", torch.cuda.is_available())
       print("Cuda version:", torch.version.cuda)
       print("CuDNN version:", torch.backends.cudnn.version())
       print("Number of GPUs available:", torch.cuda.device_count())
       print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
       print("Torch version:", None)

try:
       import deepspeed

       print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
       print("DeepSpeed version:", None)

try:
       import tensorflow as tf

       print("TensorFlow version:", tf.__version__)
       print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
       print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
       print("TensorFlow version:", None)
