



"""simple docstring"""

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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					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}}"""			)
					snake_case_					:		Dict 					=					DatasetInfosDict.from_directory(snake_case__			)
					assert dataset_infos
					assert dataset_infos["default"].dataset_size == 4_2







@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=4_2				,    ),
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  DatasetInfo			):



					"""simple docstring"""





					snake_case_					:		str 					=					str(snake_case__			)
					dataset_info.write_to_directory(snake_case__			)
					snake_case_					:		List[Any] 					=					DatasetInfo.from_directory(snake_case__			)
					assert dataset_info == reloaded
					assert os.path.exists(os.path.join(snake_case__				,    """dataset_info.json"""			)			)







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					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""": 4_2}]				,    download_checksums={}				,    download_size=1_3_3_7				,    post_processing_size=4_4_2				,    dataset_size=1_2_3_4				,    size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4				,    )
					snake_case_					:		List[str] 					=					dataset_info._to_yaml_dict()
					assert sorted(snake_case__			) == 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)			)
					snake_case_					:		Optional[Any] 					=					yaml.safe_dump(snake_case__			)
					snake_case_					:		str 					=					yaml.safe_load(snake_case__			)
					assert dataset_info_yaml_dict == reloaded







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					DatasetInfo()
					snake_case_					:		int 					=					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=4_2				,    )
            }			),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=4_2			),
                """v2""": DatasetInfo(dataset_size=1_3_3_7			),
            }			),
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  DatasetInfosDict			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					str(snake_case__			)
					dataset_infos_dict.write_to_directory(snake_case__			)
					snake_case_					:		List[str] 					=					DatasetInfosDict.from_directory(snake_case__			)

					# the config_name of the dataset_infos_dict take over the attribute
					for config_name, dataset_info in dataset_infos_dict.items():
										snake_case_					:		Tuple 					=					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
										snake_case_					:		Union[str, Any] 					=					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(snake_case__				,    """README.md"""			)			)









"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							int							       =		["""pixel_values"""]







						def __init__(self   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 0.9   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 1 / 2_55   ,		lowercase__ = True   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = None   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )
											snake_case_					:		Tuple 					=					size if size is not None else {"""shortest_edge""": 2_24}
											snake_case_					:		Union[str, Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		str 					=					crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
											snake_case_					:		Dict 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		Union[str, Any] 					=					do_resize
											snake_case_					:		List[str] 					=					size
											snake_case_					:		str 					=					crop_pct
											snake_case_					:		str 					=					resample
											snake_case_					:		Optional[Any] 					=					do_center_crop
											snake_case_					:		Dict 					=					crop_size
											snake_case_					:		int 					=					do_rescale
											snake_case_					:		Optional[int] 					=					rescale_factor
											snake_case_					:		str 					=					do_normalize
											snake_case_					:		str 					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											snake_case_					:		List[str] 					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		Tuple 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
											if crop_pct is not None:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					int(size["""shortest_edge"""] / crop_pct       )
																elif "height" in size and "width" in size:
																					if size["height"] == size["width"]:
																										snake_case_					:		Dict 					=					int(size["""height"""] / crop_pct       )
																					else:
																										snake_case_					:		List[str] 					=					(int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

																snake_case_					:		List[Any] 					=					get_resize_output_image_size(lowercase__   ,		size=lowercase__   ,		default_to_square=lowercase__       )
											else:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					get_resize_output_image_size(lowercase__   ,		size=size["""shortest_edge"""]   ,		default_to_square=lowercase__       )
																elif "height" in size and "width" in size:
																					snake_case_					:		int 					=					(size["""height"""], size["""width"""])
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

											return resize(lowercase__   ,		size=lowercase__   ,		resample=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		int 					=					get_size_dict(lowercase__       )
											if "height" not in size or "width" not in size:
																raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
											return center_crop(lowercase__   ,		size=(size["""height"""], size["""width"""])   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return rescale(lowercase__   ,		scale=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return normalize(lowercase__   ,		mean=lowercase__   ,		std=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = ChannelDimension.FIRST   ,		**lowercase__   ,		):
											snake_case_					:		str 					=					do_resize if do_resize is not None else self.do_resize
											snake_case_					:		Any 					=					crop_pct if crop_pct is not None else self.crop_pct
											snake_case_					:		List[Any] 					=					resample if resample is not None else self.resample
											snake_case_					:		str 					=					do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case_					:		str 					=					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_					:		Any 					=					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_					:		str 					=					do_normalize if do_normalize is not None else self.do_normalize
											snake_case_					:		List[Any] 					=					image_mean if image_mean is not None else self.image_mean
											snake_case_					:		int 					=					image_std if image_std is not None else self.image_std

											snake_case_					:		List[Any] 					=					size if size is not None else self.size
											snake_case_					:		Optional[Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		List[Any] 					=					crop_size if crop_size is not None else self.crop_size
											snake_case_					:		int 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		List[str] 					=					make_list_of_images(lowercase__       )

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

											if do_resize and size is None or resample is None:
																raise ValueError("""Size and resample must be specified if do_resize is True."""       )

											if do_center_crop and crop_pct is None:
																raise ValueError("""Crop_pct must be specified if do_center_crop is True."""       )

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

											if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

											# All transformations expect numpy arrays.
											snake_case_					:		int 					=					[to_numpy_array(lowercase__       ) for image in images]

											if do_resize:
																snake_case_					:		str 					=					[self.resize(image=lowercase__   ,		size=lowercase__   ,		crop_pct=lowercase__   ,		resample=lowercase__       ) for image in images]

											if do_center_crop:
																snake_case_					:		Optional[int] 					=					[self.center_crop(image=lowercase__   ,		size=lowercase__       ) for image in images]

											if do_rescale:
																snake_case_					:		List[Any] 					=					[self.rescale(image=lowercase__   ,		scale=lowercase__       ) for image in images]

											if do_normalize:
																snake_case_					:		Optional[Any] 					=					[self.normalize(image=lowercase__   ,		mean=lowercase__   ,		std=lowercase__       ) for image in images]

											snake_case_					:		List[Any] 					=					[to_channel_dimension_format(lowercase__   ,		lowercase__       ) for image in images]

											snake_case_					:		Dict 					=					{"""pixel_values""": images}
											return BatchFeature(data=lowercase__   ,		tensor_type=lowercase__       )




