


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__snake_case   							=						logging.get_logger(__name__)



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""pixel_values"""]







       def __init__(			self: Any,A_: bool = True,A_: Union[int, float] = 1 / 255,A_: bool = True,A_: int = 8,**A_: Optional[Any],):






            '''simple docstring'''




            super().__init__(**A_					)

            __UpperCamelCase =			do_rescale
            __UpperCamelCase =			rescale_factor
            __UpperCamelCase =			do_pad
            __UpperCamelCase =			pad_size







       def 		snake_case_	(			self: Any,A_: np.ndarray,A_: float,A_: Optional[Union[str, ChannelDimension]] = None,**A_: Tuple					):






            '''simple docstring'''




            return rescale(A_,scale=A_,data_format=A_,**A_					)







       def 		snake_case_	(			self: int,A_: np.ndarray,A_: int,A_: Optional[Union[str, ChannelDimension]] = None					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			get_image_size(A_					)
            __UpperCamelCase =			(old_height // size + 1) * size - old_height
            __UpperCamelCase =			(old_width // size + 1) * size - old_width

            return pad(A_,((0, pad_height), (0, pad_width)),mode='symmetric',data_format=A_					)







       def 		snake_case_	(			self: Optional[int],A_: ImageInput,A_: Optional[bool] = None,A_: Optional[float] = None,A_: Optional[bool] = None,A_: Optional[int] = None,A_: Optional[Union[str, TensorType]] = None,A_: Union[str, ChannelDimension] = ChannelDimension.FIRST,**A_: List[str],):






            '''simple docstring'''




            __UpperCamelCase =			do_rescale if do_rescale is not None else self.do_rescale
            __UpperCamelCase =			rescale_factor if rescale_factor is not None else self.rescale_factor
            __UpperCamelCase =			do_pad if do_pad is not None else self.do_pad
            __UpperCamelCase =			pad_size if pad_size is not None else self.pad_size

            __UpperCamelCase =			make_list_of_images(A_					)

            if not valid_images(A_					):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'					)

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

            # All transformations expect numpy arrays.
            __UpperCamelCase =			[to_numpy_array(A_					) for image in images]

            if do_rescale:
                 __UpperCamelCase =			[self.rescale(image=A_,scale=A_					) for image in images]

            if do_pad:
                 __UpperCamelCase =			[self.pad(A_,size=A_					) for image in images]

            __UpperCamelCase =			[to_channel_dimension_format(A_,A_					) for image in images]

            __UpperCamelCase =			{'pixel_values': images}
            return BatchFeature(data=A_,tensor_type=A_					)






import pytest

import datasets


# Import fixture modules as plugins
__snake_case   							=						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit'] ):
               continue
          item.add_marker(pytest.mark.unit )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     config.addinivalue_line('markers'					,  'torchaudio_latest: mark test to run with torchaudio>=0.12' )






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			tmp_path_factory.getbasetemp() / 'cache'
     __UpperCamelCase =			test_hf_cache_home / 'datasets'
     __UpperCamelCase =			test_hf_cache_home / 'metrics'
     __UpperCamelCase =			test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'					,  str(_lowercase ) )






@pytest.fixture(autouse=_lowercase					,  scope='session' )
def 		_A		(		)					-> Dict:


     """simple docstring"""


     datasets.disable_progress_bar()






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'					,  _lowercase )






@pytest.fixture
def 		_A		(		_lowercase )					-> Any:


     """simple docstring"""


     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'					,  _lowercase )


