

'''simple docstring'''
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict







@pytest.mark.parametrize(
    "files"		,							[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]		,							)
def  _A		(     snake_case		,							snake_case					)      ->     Any:
   _lowercase      :				Any      					=       tmp_path_factory.mktemp("dset_infos_dir"					)
   if "full:README.md" in files:
      with open(dataset_infos_dir / "README.md"		,							"w"					) as f:
         f.write("---\ndataset_info:\n  dataset_size: 42\n---"					)
   if "empty:README.md" in files:
      with open(dataset_infos_dir / "README.md"		,							"w"					) as f:
         f.write(""					)
    # we want to support dataset_infos.json for backward compatibility
   if "dataset_infos.json" in files:
      with open(dataset_infos_dir / "dataset_infos.json"		,							"w"					) as f:
         f.write("{\"default\": {\"dataset_size\": 42}}"					)
   _lowercase      :				List[str]      					=       DatasetInfosDict.from_directory(snake_case					)
   assert dataset_infos
   assert dataset_infos["default"].dataset_size == 42







@pytest.mark.parametrize(
    "dataset_info"		,							[
        DatasetInfo(),
        DatasetInfo(
            description="foo"		,							features=Features({"a": Value("int32"					)}					)		,							builder_name="builder"		,							config_name="config"		,							version="1.0.0"		,							splits=[{"name": "train"}]		,							download_size=42		,							),
    ]		,							)
def  _A		(     snake_case		,							snake_case					)      ->     Dict:
   _lowercase      :				int      					=       str(snake_case					)
   dataset_info.write_to_directory(snake_case					)
   _lowercase      :				str      					=       DatasetInfo.from_directory(snake_case					)
   assert dataset_info == reloaded
   assert os.path.exists(os.path.join(snake_case		,							"dataset_info.json"					)					)







def  _A		(     )      ->     int:
   _lowercase      :				Optional[Any]      					=       DatasetInfo(
       description="foo"		,							citation="bar"		,							homepage="https://foo.bar"		,							license="CC0"		,							features=Features({"a": Value("int32"					)}					)		,							post_processed={}		,							supervised_keys=()		,							task_templates=[]		,							builder_name="builder"		,							config_name="config"		,							version="1.0.0"		,							splits=[{"name": "train", "num_examples": 42}]		,							download_checksums={}		,							download_size=13_37		,							post_processing_size=4_42		,							dataset_size=12_34		,							size_in_bytes=13_37 + 4_42 + 12_34		,							)
   _lowercase      :				Optional[int]      					=       dataset_info._to_yaml_dict()
   assert sorted(snake_case					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      assert key in dataset_info_yaml_dict
      assert isinstance(dataset_info_yaml_dict[key]		,							(list, dict, int, str)					)
   _lowercase      :				Dict      					=       yaml.safe_dump(snake_case					)
   _lowercase      :				int      					=       yaml.safe_load(snake_case					)
   assert dataset_info_yaml_dict == reloaded







def  _A		(     )      ->     Union[str, Any]:
   _lowercase      :				List[str]      					=       DatasetInfo()
   _lowercase      :				Optional[int]      					=       dataset_info._to_yaml_dict()
   assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    "dataset_infos_dict"		,							[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}					),
        DatasetInfosDict({"my_config_name": DatasetInfo()}					),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"		,							features=Features({"a": Value("int32"					)}					)		,							builder_name="builder"		,							config_name="config"		,							version="1.0.0"		,							splits=[{"name": "train"}]		,							download_size=42		,							)
            }					),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42					),
                "v2": DatasetInfo(dataset_size=13_37					),
            }					),
    ]		,							)
def  _A		(     snake_case		,							snake_case					)      ->     Optional[int]:
   _lowercase      :				Optional[int]      					=       str(snake_case					)
   dataset_infos_dict.write_to_directory(snake_case					)
   _lowercase      :				Any      					=       DatasetInfosDict.from_directory(snake_case					)

   # the config_name of the dataset_infos_dict take over the attribute
   for config_name, dataset_info in dataset_infos_dict.items():
      _lowercase      :				Optional[Any]      					=       config_name
      # the yaml representation doesn't include fields like description or citation
      # so we just test that we can recover what we can from the yaml
      _lowercase      :				int      					=       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
   assert dataset_infos_dict == reloaded

   if dataset_infos_dict:
      assert os.path.exists(os.path.join(snake_case		,							"README.md"					)					)









'''simple docstring'''
import os
import sys
import unittest


_snake_case         =  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
_snake_case         =  os.path.join(git_repo_path, 'src', 'transformers')

_snake_case         =  '\n{0} = None\n'

_snake_case         =  '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_snake_case         =  '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'


class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"  )
      self.assertIsNone(_UpperCamelCase  )

      _lowercase      :				Dict      					=       find_backend("    if not is_tokenizers_available():"  )
      self.assertEqual(_UpperCamelCase      ,    "tokenizers"  )

      _lowercase      :				str      					=       find_backend("    if not is_tensorflow_text_available():"  )
      self.assertEqual(_UpperCamelCase      ,    "tensorflow_text"  )

      _lowercase      :				Optional[Any]      					=       find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tokenizers"  )

      _lowercase      :				List[str]      					=       find_backend(
          "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tensorflow_text"  )

      _lowercase      :				Optional[Any]      					=       find_backend(
          "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tokenizers_and_vision"  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("torch"      ,    _UpperCamelCase  )
      self.assertIn("tensorflow_text"      ,    _UpperCamelCase  )
      self.assertIn("sentencepiece_and_tokenizers"      ,    _UpperCamelCase  )

      # 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  ):





      """simple docstring"""





      _lowercase      :				int      					=       create_dummy_object("CONSTANT"      ,    "'torch'"  )
      self.assertEqual(_UpperCamelCase      ,    "\nCONSTANT = None\n"  )

      _lowercase      :				Optional[Any]      					=       create_dummy_object("function"      ,    "'torch'"  )
      self.assertEqual(
          _UpperCamelCase      ,    "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"  )

      _lowercase      :				Union[str, Any]      					=       "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
      _lowercase      :				List[str]      					=       create_dummy_object("FakeClass"      ,    "'torch'"  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
      _lowercase      :				str      					=       create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}  )
      self.assertEqual(dummy_files["torch"]      ,    _UpperCamelCase  )






