






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


__a:     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       _SCREAMING_SNAKE_CASE (							__snake_case=True )				->						int:
       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'''







  _lowerCamelCase    =  None
  _lowerCamelCase    =  None


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         with TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase							=    dataset_module_factory(lowerCamelCase       ,				cache_dir=lowerCamelCase							)

                _UpperCAmelCase							=    import_main_class(dataset_module.module_path       ,				dataset=lowerCamelCase							)

                _UpperCAmelCase							=    builder_cls(
                    cache_dir=lowerCamelCase       ,				config_name=lowerCamelCase       ,				hash=dataset_module.hash       ,				)

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




@pytest.mark.integration
def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       _UpperCAmelCase							=    tmp_path_factory.mktemp("""test_hf_gcp""" ) / """test_wikipedia_simple"""
       _UpperCAmelCase							=    dataset_module_factory("""wikipedia"""   ,      cache_dir=__snake_case )
       _UpperCAmelCase							=    import_main_class(dataset_module.module_path )
       _UpperCAmelCase							=    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
       _UpperCAmelCase							=    None
       builder_instance.download_and_prepare()
       _UpperCAmelCase							=    builder_instance.as_dataset()
       assert ds





@pytest.mark.integration
def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    dataset_module_factory("""wikipedia"""   ,      cache_dir=__snake_case )
       _UpperCAmelCase							=    import_main_class(dataset_module.module_path   ,      dataset=__snake_case )
       _UpperCAmelCase							=    builder_cls(
           cache_dir=__snake_case   ,      config_name="""20220301.frr"""   ,      hash=dataset_module.hash   ,      )
       _UpperCAmelCase							=    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 sys
import unittest


__a:     Tuple   					=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__a:     Tuple   					=					os.path.join(git_repo_path, '''src''', '''transformers''')

__a:     Optional[Any]   					=					'''
{0} = None
'''

__a:     List[Any]   					=					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__a:     int   					=					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
         self.assertIsNone(lowerCamelCase							)

         _UpperCAmelCase							=    find_backend("""    if not is_tokenizers_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tokenizers"""							)

         _UpperCAmelCase							=    find_backend("""    if not is_tensorflow_text_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers_and_vision"""							)


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


         """simple docstring"""
         _UpperCAmelCase							=    read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn("""torch"""       ,				lowerCamelCase							)
         self.assertIn("""tensorflow_text"""       ,				lowerCamelCase							)
         self.assertIn("""sentencepiece_and_tokenizers"""       ,				lowerCamelCase							)

         # Likewise, we can't assert on the exact content of a key
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertModel"""       ,				objects["""tf"""]							)
         self.assertIn("""FlaxBertModel"""       ,				objects["""flax"""]							)
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertTokenizer"""       ,				objects["""tensorflow_text"""]							)
         self.assertIn("""convert_slow_tokenizer"""       ,				objects["""sentencepiece_and_tokenizers"""]							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    create_dummy_object("""CONSTANT"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				"""\nCONSTANT = None\n"""							)

         _UpperCAmelCase							=    create_dummy_object("""function"""       ,				"""'torch'"""							)
         self.assertEqual(
             lowerCamelCase       ,				"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

         _UpperCAmelCase							=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
         _UpperCAmelCase							=    create_dummy_object("""FakeClass"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





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


         """simple docstring"""
         _UpperCAmelCase							=    """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
         _UpperCAmelCase							=    create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
         self.assertEqual(dummy_files["""torch"""]       ,				lowerCamelCase							)