

"""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


snake_case_   :   Union[str, Any]        =       logging.get_logger(__name__)







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						['''pixel_values''']







			def __init__(			self			: str			,  lowercase			: bool = True			,  lowercase			: Dict[str, int] = None			,  lowercase			: int = 0.9			,  lowercase			: PILImageResampling = PILImageResampling.BICUBIC			,  lowercase			: bool = True			,  lowercase			: Dict[str, int] = None			,  lowercase			: Union[int, float] = 1 / 2_55			,  lowercase			: bool = True			,  lowercase			: bool = True			,  lowercase			: Optional[Union[float, List[float]]] = None			,  lowercase			: Optional[Union[float, List[float]]] = None			,  **lowercase			: Optional[Any]			,  ):





								'''simple docstring'''



								super().__init__(**lowercase  )
								UpperCAmelCase			:   List[str]      =				size if size is not None else {"shortest_edge": 2_24}
								UpperCAmelCase			:   List[Any]      =				get_size_dict(lowercase			,  default_to_square=lowercase  )
								UpperCAmelCase			:   int      =				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
								UpperCAmelCase			:   Tuple      =				get_size_dict(lowercase			,  param_name="crop_size"  )

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







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: np.ndarray			,  lowercase			: Dict[str, int]			,  lowercase			: Optional[float] = None			,  lowercase			: PILImageResampling = PILImageResampling.BICUBIC			,  lowercase			: Optional[Union[str, ChannelDimension]] = None			,  **lowercase			: int			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				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:
																		UpperCAmelCase			:   List[str]      =				int(size["shortest_edge"] / crop_pct  )
													elif "height" in size and "width" in size:
																		if size["height"] == size["width"]:
																							UpperCAmelCase			:   Dict      =				int(size["height"] / crop_pct  )
																		else:
																							UpperCAmelCase			:   List[Any]      =				(int(size["height"] / crop_pct  ), int(size["width"] / crop_pct  ))
													else:
																		raise ValueError("Invalid size for resize: {}".format(lowercase  )  )

													UpperCAmelCase			:   str      =				get_resize_output_image_size(lowercase			,  size=lowercase			,  default_to_square=lowercase  )
								else:
													if "shortest_edge" in size:
																		UpperCAmelCase			:   int      =				get_resize_output_image_size(lowercase			,  size=size["shortest_edge"]			,  default_to_square=lowercase  )
													elif "height" in size and "width" in size:
																		UpperCAmelCase			:   Tuple      =				(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 			__lowerCAmelCase				(			self			: str			,  lowercase			: np.ndarray			,  lowercase			: Dict[str, int]			,  lowercase			: Optional[Union[str, ChannelDimension]] = None			,  **lowercase			: Optional[int]			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				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 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: np.ndarray			,  lowercase			: Union[int, float]			,  lowercase			: Optional[Union[str, ChannelDimension]] = None			,  **lowercase			: Union[str, Any]			,  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: np.ndarray			,  lowercase			: Union[float, List[float]]			,  lowercase			: Union[float, List[float]]			,  lowercase			: Optional[Union[str, ChannelDimension]] = None			,  **lowercase			: Optional[int]			,  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: ImageInput			,  lowercase			: bool = None			,  lowercase			: Dict[str, int] = None			,  lowercase			: int = None			,  lowercase			: PILImageResampling = None			,  lowercase			: bool = None			,  lowercase			: Dict[str, int] = None			,  lowercase			: bool = None			,  lowercase			: float = None			,  lowercase			: bool = None			,  lowercase			: Optional[Union[float, List[float]]] = None			,  lowercase			: Optional[Union[float, List[float]]] = None			,  lowercase			: Optional[Union[str, TensorType]] = None			,  lowercase			: ChannelDimension = ChannelDimension.FIRST			,  **lowercase			: Optional[int]			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase			:   List[Any]      =				crop_pct if crop_pct is not None else self.crop_pct
								UpperCAmelCase			:   List[str]      =				resample if resample is not None else self.resample
								UpperCAmelCase			:   List[Any]      =				do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase			:   Dict      =				do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase			:   Union[str, Any]      =				rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase			:   Dict      =				do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase			:   int      =				image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase			:   List[str]      =				image_std if image_std is not None else self.image_std

								UpperCAmelCase			:   Dict      =				size if size is not None else self.size
								UpperCAmelCase			:   Tuple      =				get_size_dict(lowercase			,  default_to_square=lowercase  )
								UpperCAmelCase			:   List[Any]      =				crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase			:   str      =				get_size_dict(lowercase			,  param_name="crop_size"  )

								UpperCAmelCase			:   Optional[Any]      =				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.
								UpperCAmelCase			:   Tuple      =				[to_numpy_array(lowercase  ) for image in images]

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

								if do_center_crop:
													UpperCAmelCase			:   str      =				[self.center_crop(image=lowercase			,  size=lowercase  ) for image in images]

								if do_rescale:
													UpperCAmelCase			:   Tuple      =				[self.rescale(image=lowercase			,  scale=lowercase  ) for image in images]

								if do_normalize:
													UpperCAmelCase			:   Dict      =				[self.normalize(image=lowercase			,  mean=lowercase			,  std=lowercase  ) for image in images]

								UpperCAmelCase			:   Optional[Any]      =				[to_channel_dimension_format(lowercase			,  lowercase  ) for image in images]

								UpperCAmelCase			:   Dict      =				{"pixel_values": images}
								return BatchFeature(data=lowercase			,  tensor_type=lowercase  )





"""simple docstring"""






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   lowercase_	(    _lowercase       :		Optional[int]							,      _lowercase       :		int	):




					'''simple docstring'''






					assert isinstance(_lowercase							,      _lowercase	)
					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   lowercase_	(    _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				tmp_path / "cache"
					UpperCAmelCase			:   str      =				{"text": "string"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCAmelCase			:   Tuple      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase							,      keep_in_memory=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



@pytest.mark.parametrize(
    "features"							,      [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,      )
def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				tmp_path / "cache"
					UpperCAmelCase			:   Any      =				{"text": "string"}
					UpperCAmelCase			:   List[str]      =				features.copy() if features else default_expected_features
					UpperCAmelCase			:   Union[str, Any]      =				(
					    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					UpperCAmelCase			:   str      =				TextDatasetReader(_lowercase							,      features=_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



@pytest.mark.parametrize("split"							,      [None, NamedSplit("train"	), "train", "test"]	)
def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Tuple							,      _lowercase       :		List[str]	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				tmp_path / "cache"
					UpperCAmelCase			:   Any      =				{"text": "string"}
					UpperCAmelCase			:   List[Any]      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase							,      split=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)
					assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"							,      [str, list]	)
def   lowercase_	(    _lowercase       :		Optional[int]							,      _lowercase       :		Any							,      _lowercase       :		Union[str, Any]	):




					'''simple docstring'''






					if issubclass(_lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				text_path
					elif issubclass(_lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[Any]      =				[text_path]
					UpperCAmelCase			:   Union[str, Any]      =				tmp_path / "cache"
					UpperCAmelCase			:   List[Any]      =				{"text": "string"}
					UpperCAmelCase			:   Tuple      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]=("train",)	):




					'''simple docstring'''






					assert isinstance(_lowercase							,      _lowercase	)
					for split in splits:
										UpperCAmelCase			:   str      =				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   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		str							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				tmp_path / "cache"
					UpperCAmelCase			:   Union[str, Any]      =				{"text": "string"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCAmelCase			:   Tuple      =				TextDatasetReader({"train": text_path}							,      cache_dir=_lowercase							,      keep_in_memory=_lowercase	).read()
					_check_text_datasetdict(_lowercase							,      _lowercase	)



@pytest.mark.parametrize(
    "features"							,      [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,      )
def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				tmp_path / "cache"
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					UpperCAmelCase			:   List[str]      =				{"text": "string"}
					UpperCAmelCase			:   str      =				features.copy() if features else default_expected_features
					UpperCAmelCase			:   Optional[int]      =				(
					    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					UpperCAmelCase			:   Optional[int]      =				TextDatasetReader({"train": text_path}							,      features=_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_datasetdict(_lowercase							,      _lowercase	)




@pytest.mark.parametrize("split"							,      [None, NamedSplit("train"	), "train", "test"]	)
def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Tuple							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					if split:
										UpperCAmelCase			:   Optional[Any]      =				{split: text_path}
					else:
										UpperCAmelCase			:   str      =				"train"
										UpperCAmelCase			:   Union[str, Any]      =				{"train": text_path, "test": text_path}
					UpperCAmelCase			:   Any      =				tmp_path / "cache"
					UpperCAmelCase			:   List[Any]      =				{"text": "string"}
					UpperCAmelCase			:   Any      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_datasetdict(_lowercase							,      _lowercase							,      splits=list(path.keys()	)	)
					assert all(dataset[split].split == split for split in path.keys()	)


