



import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def 	__lowercase   ( a__							,   a__   )				-> Optional[Any]:
							assert isinstance(a__							,   a__   )
							assert dataset.num_rows == 4
							assert dataset.num_columns == 1
							assert dataset.column_names == ["text"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('keep_in_memory'							,   [False, True]   )
def 	__lowercase   ( a__							,   a__							,   a__   )				-> Optional[int]:
							__SCREAMING_SNAKE_CASE									=					tmp_path / "cache"
							__SCREAMING_SNAKE_CASE									=					{"text": "string"}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														__SCREAMING_SNAKE_CASE									=					TextDatasetReader(a__							,   cache_dir=a__							,   keep_in_memory=a__   ).read()
							_check_text_dataset(a__							,   a__   )






@pytest.mark.parametrize(
    'features'							,   [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]							,   )
def 	__lowercase   ( a__							,   a__							,   a__   )				-> Tuple:
							__SCREAMING_SNAKE_CASE									=					tmp_path / "cache"
							__SCREAMING_SNAKE_CASE									=					{"text": "string"}
							__SCREAMING_SNAKE_CASE									=					features.copy() if features else default_expected_features
							__SCREAMING_SNAKE_CASE									=					(
							    Features({feature: Value(a__   ) for feature, dtype in features.items()}   ) if features is not None else None
							)
							__SCREAMING_SNAKE_CASE									=					TextDatasetReader(a__							,   features=a__							,   cache_dir=a__   ).read()
							_check_text_dataset(a__							,   a__   )






@pytest.mark.parametrize('split'							,   [None, NamedSplit('train'   ), 'train', 'test']   )
def 	__lowercase   ( a__							,   a__							,   a__   )				-> str:
							__SCREAMING_SNAKE_CASE									=					tmp_path / "cache"
							__SCREAMING_SNAKE_CASE									=					{"text": "string"}
							__SCREAMING_SNAKE_CASE									=					TextDatasetReader(a__							,   cache_dir=a__							,   split=a__   ).read()
							_check_text_dataset(a__							,   a__   )
							assert dataset.split == split if split else "train"






@pytest.mark.parametrize('path_type'							,   [str, list]   )
def 	__lowercase   ( a__							,   a__							,   a__   )				-> List[Any]:







							if issubclass(a__							,   a__   ):
														__SCREAMING_SNAKE_CASE									=					text_path
							elif issubclass(a__							,   a__   ):
														__SCREAMING_SNAKE_CASE									=					[text_path]
							__SCREAMING_SNAKE_CASE									=					tmp_path / "cache"
							__SCREAMING_SNAKE_CASE									=					{"text": "string"}
							__SCREAMING_SNAKE_CASE									=					TextDatasetReader(a__							,   cache_dir=a__   ).read()
							_check_text_dataset(a__							,   a__   )






def 	__lowercase   ( a__							,   a__							,   a__=("train",)   )				-> List[str]:
							assert isinstance(a__							,   a__   )
							for split in splits:
														__SCREAMING_SNAKE_CASE									=					dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 1
														assert dataset.column_names == ["text"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('keep_in_memory'							,   [False, True]   )
def 	__lowercase   ( a__							,   a__							,   a__   )				-> str:
							__SCREAMING_SNAKE_CASE									=					tmp_path / "cache"
							__SCREAMING_SNAKE_CASE									=					{"text": "string"}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														__SCREAMING_SNAKE_CASE									=					TextDatasetReader({'train': text_path}							,   cache_dir=a__							,   keep_in_memory=a__   ).read()
							_check_text_datasetdict(a__							,   a__   )






@pytest.mark.parametrize(
    'features'							,   [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]							,   )
def 	__lowercase   ( a__							,   a__							,   a__   )				-> Tuple:
							__SCREAMING_SNAKE_CASE									=					tmp_path / "cache"
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							__SCREAMING_SNAKE_CASE									=					{"text": "string"}
							__SCREAMING_SNAKE_CASE									=					features.copy() if features else default_expected_features
							__SCREAMING_SNAKE_CASE									=					(
							    Features({feature: Value(a__   ) for feature, dtype in features.items()}   ) if features is not None else None
							)
							__SCREAMING_SNAKE_CASE									=					TextDatasetReader({'train': text_path}							,   features=a__							,   cache_dir=a__   ).read()
							_check_text_datasetdict(a__							,   a__   )






@pytest.mark.parametrize('split'							,   [None, NamedSplit('train'   ), 'train', 'test']   )
def 	__lowercase   ( a__							,   a__							,   a__   )				-> Optional[int]:







							if split:
														__SCREAMING_SNAKE_CASE									=					{split: text_path}
							else:
														__SCREAMING_SNAKE_CASE									=					"train"
														__SCREAMING_SNAKE_CASE									=					{"train": text_path, "test": text_path}
							__SCREAMING_SNAKE_CASE									=					tmp_path / "cache"
							__SCREAMING_SNAKE_CASE									=					{"text": "string"}
							__SCREAMING_SNAKE_CASE									=					TextDatasetReader(a__							,   cache_dir=a__   ).read()
							_check_text_datasetdict(a__							,   a__							,   splits=list(path.keys()   )   )
							assert all(dataset[split].split == split for split in path.keys()   )










"""simple docstring"""







import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							get_activation("swish"						)

         self.assertIsInstance(snake_case__		,     nn.SiLU						)

         self.assertEqual(act(torch.tensor(-100		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							get_activation("silu"						)

         self.assertIsInstance(snake_case__		,     nn.SiLU						)

         self.assertEqual(act(torch.tensor(-100		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							get_activation("mish"						)

         self.assertIsInstance(snake_case__		,     nn.Mish						)

         self.assertEqual(act(torch.tensor(-200		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							get_activation("gelu"						)

         self.assertIsInstance(snake_case__		,     nn.GELU						)

         self.assertEqual(act(torch.tensor(-100		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertNotEqual(act(torch.tensor(-1		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(0		,     dtype=torch.floataa						)						).item()		,     0						)
         self.assertEqual(act(torch.tensor(20		,     dtype=torch.floataa						)						).item()		,     20						)





