

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   __UpperCAmelCase					(      __a :     str						,__a :     Optional[int]      )     ->	str:

					"""simple docstring"""

					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   __UpperCAmelCase					(      __a :     Any						,__a :     Union[str, Any]						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

					_a      :       str   =						tmp_path / '''cache'''
					_a      :       List[Any]   =						{'''text''': '''string'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_a      :       List[Any]   =						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   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Optional[Any]						,__a :     int      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Dict   =						tmp_path / '''cache'''
					_a      :       Optional[Any]   =						{'''text''': '''string'''}
					_a      :       Optional[Any]   =						features.copy() if features else default_expected_features
					_a      :       Tuple   =						(
					    Features({feature: Value(__a      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					_a      :       List[str]   =						TextDatasetReader(__a						,features=__a						,cache_dir=__a      ).read()
					_check_text_dataset(__a						,__a      )



@pytest.mark.parametrize('''split'''						,[None, NamedSplit('''train'''      ), '''train''', '''test''']      )
def   __UpperCAmelCase					(      __a :     str						,__a :     int						,__a :     List[str]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Any   =						tmp_path / '''cache'''
					_a      :       List[str]   =						{'''text''': '''string'''}
					_a      :       str   =						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   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     str      )     ->	Optional[Any]:

					"""simple docstring"""

					if issubclass(__a						,__a      ):
										_a      :       Tuple   =						text_path
					elif issubclass(__a						,__a      ):
										_a      :       Tuple   =						[text_path]
					_a      :       Union[str, Any]   =						tmp_path / '''cache'''
					_a      :       Any   =						{'''text''': '''string'''}
					_a      :       int   =						TextDatasetReader(__a						,cache_dir=__a      ).read()
					_check_text_dataset(__a						,__a      )



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Union[str, Any]						,__a :     Optional[int]=("train",)      )     ->	List[Any]:

					"""simple docstring"""

					assert isinstance(__a						,__a      )
					for split in splits:
										_a      :       Union[str, Any]   =						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   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     Optional[Any]      )     ->	Any:

					"""simple docstring"""

					_a      :       List[str]   =						tmp_path / '''cache'''
					_a      :       List[str]   =						{'''text''': '''string'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_a      :       Union[str, Any]   =						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   __UpperCAmelCase					(      __a :     Dict						,__a :     List[str]						,__a :     Optional[int]      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Optional[Any]   =						tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					_a      :       Optional[Any]   =						{'''text''': '''string'''}
					_a      :       str   =						features.copy() if features else default_expected_features
					_a      :       Dict   =						(
					    Features({feature: Value(__a      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					_a      :       Union[str, Any]   =						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   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

					if split:
										_a      :       Union[str, Any]   =						{split: text_path}
					else:
										_a      :       Any   =						'''train'''
										_a      :       Any   =						{'''train''': text_path, '''test''': text_path}
					_a      :       List[str]   =						tmp_path / '''cache'''
					_a      :       Union[str, Any]   =						{'''text''': '''string'''}
					_a      :       Tuple   =						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()      )









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )