


import pytest

import datasets


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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
					for item in items:
										if any(marker in item.keywords for marker in ["""integration""", """unit"""]  ):
															continue
										item.add_marker(pytest.mark.unit  )


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					config.addinivalue_line("""markers"""						,							"""torchaudio_latest: mark test to run with torchaudio>=0.12"""  )


@pytest.fixture(autouse=__A  )
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
					lowercase_  							=			tmp_path_factory.getbasetemp() / """cache"""
					lowercase_  							=			test_hf_cache_home / """datasets"""
					lowercase_  							=			test_hf_cache_home / """metrics"""
					lowercase_  							=			test_hf_cache_home / """modules"""
					monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""						,							str(__A  )  )
					monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""						,							str(__A  )  )
					monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""						,							str(__A  )  )
					lowercase_  							=			test_hf_datasets_cache / """downloads"""
					monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""						,							str(__A  )  )
					lowercase_  							=			test_hf_datasets_cache / """downloads""" / """extracted"""
					monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""						,							str(__A  )  )


@pytest.fixture(autouse=__A						,							scope="""session"""  )
def 			UpperCAmelCase_				(      ):
					datasets.disable_progress_bar()


@pytest.fixture(autouse=__A  )
def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					# don't take tests into account when counting downloads
					monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""						,							__A  )



@pytest.fixture
def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
					# To be removed once SQLAlchemy 2.0 supported
					monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""						,							__A  )






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


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   ['pixel_values']

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






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )

										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_pad
										lowercase_  							=			pad_size

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )
										lowercase_  							=			(old_height // size + 1) * size - old_height
										lowercase_  							=			(old_width // size + 1) * size - old_width

										return pad(UpperCamelCase__							,					((0, pad_height), (0, pad_width))							,					mode="""symmetric"""							,					data_format=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   Union[str, ChannelDimension] = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






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

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															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.
										lowercase_  							=			[to_numpy_array(UpperCamelCase__       ) for image in images]

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

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

										lowercase_  							=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			{"""pixel_values""": images}
										return BatchFeature(data=UpperCamelCase__							,					tensor_type=UpperCamelCase__       )


