
'''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


UpperCamelCase_   	=	logging.get_logger(__name__)

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Dict   							=  ['pixel_values']







							def __init__(						self  :  int      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  bool = True      ,				snake_case__  :  Union[int, float] = 1 / 255      ,				snake_case__  :  bool = True      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  bool = True      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )
								__lowerCAmelCase         =							size if size is not None else {"height": 384, "width": 384}
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )

								__lowerCAmelCase         =							do_resize
								__lowerCAmelCase         =							size
								__lowerCAmelCase         =							resample
								__lowerCAmelCase         =							do_rescale
								__lowerCAmelCase         =							rescale_factor
								__lowerCAmelCase         =							do_normalize
								__lowerCAmelCase         =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__lowerCAmelCase         =							image_std if image_std is not None else OPENAI_CLIP_STD
								__lowerCAmelCase         =							do_convert_rgb







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  str      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
								if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}" )
								__lowerCAmelCase         =							(size["height"], size["width"])
								return resize(snake_case__      ,				size=snake_case__      ,				resample=snake_case__      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[int, float]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  int      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







								return normalize(snake_case__      ,				mean=snake_case__      ,				std=snake_case__      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  ImageInput      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Dict[str, int]] = None      ,				snake_case__  :  PILImageResampling = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[float] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				snake_case__  :  bool = None      ,				snake_case__  :  ChannelDimension = ChannelDimension.FIRST      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							do_resize if do_resize is not None else self.do_resize
								__lowerCAmelCase         =							resample if resample is not None else self.resample
								__lowerCAmelCase         =							do_rescale if do_rescale is not None else self.do_rescale
								__lowerCAmelCase         =							rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCAmelCase         =							do_normalize if do_normalize is not None else self.do_normalize
								__lowerCAmelCase         =							image_mean if image_mean is not None else self.image_mean
								__lowerCAmelCase         =							image_std if image_std is not None else self.image_std
								__lowerCAmelCase         =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__lowerCAmelCase         =							size if size is not None else self.size
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )

								__lowerCAmelCase         =							make_list_of_images(snake_case__ )

								if not valid_images(snake_case__ ):
									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_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." )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
									__lowerCAmelCase         =							[convert_to_rgb(snake_case__ ) for image in images]

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

								if do_resize:
									__lowerCAmelCase         =							[self.resize(image=snake_case__      ,				size=snake_case__      ,				resample=snake_case__ ) for image in images]

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

								if do_normalize:
									__lowerCAmelCase         =							[self.normalize(image=snake_case__      ,				mean=snake_case__      ,				std=snake_case__ ) for image in images]

								__lowerCAmelCase         =							[to_channel_dimension_format(snake_case__      ,				snake_case__ ) for image in images]

								__lowerCAmelCase         =							BatchFeature(data={"pixel_values": images}      ,				tensor_type=snake_case__ )

								return encoded_outputs








import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCamelCase_   	=	pytest.mark.integration






@pytest.mark.parametrize("path"			,							["paws", "csv"]  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Tuple  ):






	"""simple docstring"""


	inspect_dataset(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							path + ".py"
	assert script_name in os.listdir(UpperCamelCase  )
	assert "__pycache__" not in os.listdir(UpperCamelCase  )






@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"  )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"  )
@pytest.mark.parametrize("path"			,							["accuracy"]  )
def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     Any  ):






	"""simple docstring"""


	inspect_metric(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							path + ".py"
	assert script_name in os.listdir(UpperCamelCase  )
	assert "__pycache__" not in os.listdir(UpperCamelCase  )






@pytest.mark.parametrize(
    "path, config_name, expected_splits"			,							[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     int			,							UpperCamelCase:     Tuple  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_config_info(UpperCamelCase			,							config_name=UpperCamelCase  )
	assert info.config_name == config_name
	assert list(info.splits.keys()  ) == expected_splits






@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,							[
        ("paws", None, ValueError),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Any			,							UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	with pytest.raises(UpperCamelCase  ):
		get_dataset_config_info(UpperCamelCase			,							config_name=UpperCamelCase  )






@pytest.mark.parametrize(
    "path, expected"			,							[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]			,							UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_config_names(UpperCamelCase  )
	assert expected in config_names






@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"			,							[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_infos(UpperCamelCase  )
	assert list(infos.keys()  ) == expected_configs
	__lowerCAmelCase         =							expected_configs[0]
	assert expected_config in infos
	__lowerCAmelCase         =							infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()  ) == expected_splits_in_first_config






@pytest.mark.parametrize(
    "path, expected_config, expected_splits"			,							[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     int			,							UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_infos(UpperCamelCase  )
	assert expected_config in infos
	__lowerCAmelCase         =							infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()  ) == expected_splits







@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,							[
        ("paws", None, ValueError),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	with pytest.raises(UpperCamelCase  ):
		get_dataset_split_names(UpperCamelCase			,							config_name=UpperCamelCase  )






