



"""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      _lowercase							(    __snake_case   ,__snake_case      )		->  List[str]:
			__lowerCAmelCase :    List[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}}"      )
			__lowerCAmelCase :    Optional[Any]				=			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      _lowercase							(    __snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    List[Any]				=			str(__snake_case      )
			dataset_info.write_to_directory(__snake_case      )
			__lowerCAmelCase :    Union[str, Any]				=			DatasetInfo.from_directory(__snake_case      )
			assert dataset_info == reloaded
			assert os.path.exists(os.path.join(__snake_case   ,"dataset_info.json"      )      )


def      _lowercase							(    )		->  Any:
			__lowerCAmelCase :    Dict				=			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=1_337   ,post_processing_size=442   ,dataset_size=1_234   ,size_in_bytes=1_337 + 442 + 1_234   ,)
			__lowerCAmelCase :    Optional[Any]				=			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)      )
			__lowerCAmelCase :    Dict				=			yaml.safe_dump(__snake_case      )
			__lowerCAmelCase :    Union[str, Any]				=			yaml.safe_load(__snake_case      )
			assert dataset_info_yaml_dict == reloaded


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    str				=			DatasetInfo()
			__lowerCAmelCase :    List[Any]				=			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=1_337      ),
            }      ),
    ]   ,)
def      _lowercase							(    __snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    Optional[Any]				=			str(__snake_case      )
			dataset_infos_dict.write_to_directory(__snake_case      )
			__lowerCAmelCase :    int				=			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():
						__lowerCAmelCase :    List[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
						__lowerCAmelCase :    Any				=			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"      )      )







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-base"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
         # The dog is cute and lives in the garden house

         _UpperCAmelCase							=    torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)





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


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-large"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
         # The dog is cute and lives in the garden house

         _UpperCAmelCase							=    torch.Size((1, 12, 1024)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)