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  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      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}}'''							)
     UpperCamelCase__										=      DatasetInfosDict.from_directory(A							)
     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  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      str(A							)
     dataset_info.write_to_directory(A							)
     UpperCamelCase__										=      DatasetInfo.from_directory(A							)
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(A ,							'''dataset_info.json'''							)							)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      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=1337 ,							post_processing_size=442 ,							dataset_size=1234 ,							size_in_bytes=1337 + 442 + 1234 ,							)
     UpperCamelCase__										=      dataset_info._to_yaml_dict()
     assert sorted(A							) == 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)							)
     UpperCamelCase__										=      yaml.safe_dump(A							)
     UpperCamelCase__										=      yaml.safe_load(A							)
     assert dataset_info_yaml_dict == reloaded







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      DatasetInfo()
     UpperCamelCase__										=      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=1337							),
            }							),
    ] ,							)
def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      str(A							)
     dataset_infos_dict.write_to_directory(A							)
     UpperCamelCase__										=      DatasetInfosDict.from_directory(A							)

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          UpperCamelCase__										=      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
          UpperCamelCase__										=      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(A ,							'''README.md'''							)							)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class     _A   (    unittest.TestCase							):






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]	)
       UpperCamelCase__										=      get_activation('''gelu'''	)
       self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_	)   ,				torch_builtin(SCREAMING_SNAKE_CASE_	)	)	)
       self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_	)   ,				gelu_new(SCREAMING_SNAKE_CASE_	)	)	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]	)
       UpperCamelCase__										=      get_activation('''gelu'''	)
       UpperCamelCase__										=      get_activation('''gelu_10'''	)

       UpperCamelCase__										=      torch_builtin(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      geluaa(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.where(y_gelu_aa < 10.0   ,				1   ,				0	)

       self.assertTrue(torch.max(SCREAMING_SNAKE_CASE_	).item() == 10.0	)
       self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,				y_gelu_aa * clipped_mask	)	)






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





       '''simple docstring'''





       get_activation('''gelu'''	)
       get_activation('''gelu_10'''	)
       get_activation('''gelu_fast'''	)
       get_activation('''gelu_new'''	)
       get_activation('''gelu_python'''	)
       get_activation('''gelu_pytorch_tanh'''	)
       get_activation('''linear'''	)
       get_activation('''mish'''	)
       get_activation('''quick_gelu'''	)
       get_activation('''relu'''	)
       get_activation('''sigmoid'''	)
       get_activation('''silu'''	)
       get_activation('''swish'''	)
       get_activation('''tanh'''	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            get_activation('''bogus'''	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            get_activation(SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__										=      get_activation('''gelu'''	)
       UpperCamelCase__										=      1
       UpperCamelCase__										=      get_activation('''gelu'''	)
       self.assertEqual(acta.a   ,				1	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      acta.a



