"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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 			_snake_case  (       UpperCAmelCase__		):
						snake_case__      	=      ["pixel_values"]






						def __init__(							self   :      List[Any]				,       UpperCAmelCase   :      bool = True				,       UpperCAmelCase   :      Dict[str, int] = None				,       UpperCAmelCase   :      PILImageResampling = PIL.Image.BICUBIC				,       UpperCAmelCase   :      bool = True				,       UpperCAmelCase   :      Dict[str, int] = None				,       UpperCAmelCase   :      Union[int, float] = 1 / 255				,       UpperCAmelCase   :      bool = True				,       UpperCAmelCase   :      bool = True				,       UpperCAmelCase   :      Optional[Union[float, List[float]]] = None				,       UpperCAmelCase   :      Optional[Union[float, List[float]]] = None				,       **UpperCAmelCase   :      Optional[Any]				,       ):
													super().__init__(**_UpperCAmelCase     )
													__lowerCamelCase       :		Optional[Any]		 =  size if size is not None else {"height": 256, "width": 256}
													__lowerCamelCase       :		Any		 =  get_size_dict(_UpperCAmelCase     )
													__lowerCamelCase       :		Dict		 =  crop_size if crop_size is not None else {"height": 224, "width": 224}
													__lowerCamelCase       :		Any		 =  get_size_dict(_UpperCAmelCase				,       param_name="crop_size"     )

													__lowerCamelCase       :		List[Any]		 =  do_resize
													__lowerCamelCase       :		Dict		 =  size
													__lowerCamelCase       :		int		 =  resample
													__lowerCamelCase       :		List[str]		 =  do_center_crop
													__lowerCamelCase       :		Optional[int]		 =  crop_size
													__lowerCamelCase       :		Union[str, Any]		 =  do_rescale
													__lowerCamelCase       :		Union[str, Any]		 =  rescale_factor
													__lowerCamelCase       :		Optional[Any]		 =  do_normalize
													__lowerCamelCase       :		Dict		 =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__lowerCamelCase       :		Optional[int]		 =  image_std if image_std is not None else IMAGENET_STANDARD_STD






						def    lowerCamelCase__  (							self   :      Union[str, Any]				,       UpperCAmelCase   :      np.ndarray				,       UpperCAmelCase   :      Dict[str, int]				,       UpperCAmelCase   :      PILImageResampling = PIL.Image.BICUBIC				,       UpperCAmelCase   :      Optional[Union[str, ChannelDimension]] = None				,       **UpperCAmelCase   :      Any				,       ):
													__lowerCamelCase       :		Union[str, Any]		 =  get_size_dict(_UpperCAmelCase     )
													if "height" not in size or "width" not in size:
																				raise ValueError(F"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""     )
													return resize(
													    _UpperCAmelCase				,       size=(size["height"], size["width"])				,       resample=_UpperCAmelCase				,       data_format=_UpperCAmelCase				,       **_UpperCAmelCase     )






						def    lowerCamelCase__  (							self   :      Any				,       UpperCAmelCase   :      np.ndarray				,       UpperCAmelCase   :      Dict[str, int]				,       UpperCAmelCase   :      Optional[Union[str, ChannelDimension]] = None				,       **UpperCAmelCase   :      Tuple				,       ):
													__lowerCamelCase       :		Dict		 =  get_size_dict(_UpperCAmelCase     )
													if "height" not in size or "width" not in size:
																				raise ValueError(F"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""     )
													return center_crop(_UpperCAmelCase				,       size=(size["height"], size["width"])				,       data_format=_UpperCAmelCase				,       **_UpperCAmelCase     )






						def    lowerCamelCase__  (							self   :      str				,       UpperCAmelCase   :      np.ndarray				,       UpperCAmelCase   :      Union[int, float]				,       UpperCAmelCase   :      Optional[Union[str, ChannelDimension]] = None				,       **UpperCAmelCase   :      int				,       ):
													return rescale(_UpperCAmelCase				,       scale=_UpperCAmelCase				,       data_format=_UpperCAmelCase				,       **_UpperCAmelCase     )






						def    lowerCamelCase__  (							self   :      str				,       UpperCAmelCase   :      np.ndarray				,       UpperCAmelCase   :      Union[float, List[float]]				,       UpperCAmelCase   :      Union[float, List[float]]				,       UpperCAmelCase   :      Optional[Union[str, ChannelDimension]] = None				,       **UpperCAmelCase   :      str				,       ):
													return normalize(_UpperCAmelCase				,       mean=_UpperCAmelCase				,       std=_UpperCAmelCase				,       data_format=_UpperCAmelCase				,       **_UpperCAmelCase     )







						def    lowerCamelCase__  (							self   :      str				,       UpperCAmelCase   :      ImageInput				,       UpperCAmelCase   :      bool = None				,       UpperCAmelCase   :      Dict[str, int] = None				,       UpperCAmelCase   :      Optional[int]=None				,       UpperCAmelCase   :      bool = None				,       UpperCAmelCase   :      Dict[str, int] = None				,       UpperCAmelCase   :      bool = None				,       UpperCAmelCase   :      float = None				,       UpperCAmelCase   :      bool = None				,       UpperCAmelCase   :      Optional[Union[float, List[float]]] = None				,       UpperCAmelCase   :      Optional[Union[float, List[float]]] = None				,       UpperCAmelCase   :      Optional[Union[str, TensorType]] = None				,       UpperCAmelCase   :      ChannelDimension = ChannelDimension.FIRST				,       **UpperCAmelCase   :      Any				,       ):
													__lowerCamelCase       :		List[Any]		 =  do_resize if do_resize is not None else self.do_resize
													__lowerCamelCase       :		Tuple		 =  resample if resample is not None else self.resample
													__lowerCamelCase       :		List[Any]		 =  do_center_crop if do_center_crop is not None else self.do_center_crop
													__lowerCamelCase       :		str		 =  do_rescale if do_rescale is not None else self.do_rescale
													__lowerCamelCase       :		List[str]		 =  rescale_factor if rescale_factor is not None else self.rescale_factor
													__lowerCamelCase       :		int		 =  do_normalize if do_normalize is not None else self.do_normalize
													__lowerCamelCase       :		Optional[int]		 =  image_mean if image_mean is not None else self.image_mean
													__lowerCamelCase       :		int		 =  image_std if image_std is not None else self.image_std

													__lowerCamelCase       :		Optional[Any]		 =  size if size is not None else self.size
													__lowerCamelCase       :		str		 =  get_size_dict(_UpperCAmelCase     )
													__lowerCamelCase       :		Dict		 =  crop_size if crop_size is not None else self.crop_size
													__lowerCamelCase       :		Optional[int]		 =  get_size_dict(_UpperCAmelCase				,       param_name="crop_size"     )

													__lowerCamelCase       :		Dict		 =  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_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_size is None:
																				raise ValueError("Crop size 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.
													__lowerCamelCase       :		Tuple		 =  [to_numpy_array(_UpperCAmelCase     ) for image in images]

													if do_resize:
																				__lowerCamelCase       :		List[str]		 =  [self.resize(image=_UpperCAmelCase				,       size=_UpperCAmelCase				,       resample=_UpperCAmelCase     ) for image in images]

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

													if do_rescale:
																				__lowerCamelCase       :		str		 =  [self.rescale(image=_UpperCAmelCase				,       scale=_UpperCAmelCase     ) for image in images]

													if do_normalize:
																				__lowerCamelCase       :		Any		 =  [self.normalize(image=_UpperCAmelCase				,       mean=_UpperCAmelCase				,       std=_UpperCAmelCase     ) for image in images]

													__lowerCamelCase       :		Union[str, Any]		 =  [to_channel_dimension_format(_UpperCAmelCase				,       _UpperCAmelCase     ) for image in images]

													__lowerCamelCase       :		Optional[Any]		 =  {"pixel_values": images}
													return BatchFeature(data=_UpperCAmelCase				,       tensor_type=_UpperCAmelCase     )



from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		NestedDataStructureLike[PathLike] ,   _UpperCAmelCase       :		Optional[NamedSplit] = None ,   _UpperCAmelCase       :		Optional[Features] = None ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		Optional[int] = None ,   **_UpperCAmelCase       :		Optional[int] ,   )		->  List[str]:




							"""simple docstring"""


							super().__init__(
							    _UpperCAmelCase ,   split=_UpperCAmelCase ,   features=_UpperCAmelCase ,   cache_dir=_UpperCAmelCase ,   keep_in_memory=_UpperCAmelCase ,   streaming=_UpperCAmelCase ,   num_proc=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						path_or_paths if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else {self.split: path_or_paths}
							lowercase__	     =						Text(
							    cache_dir=_UpperCAmelCase ,   data_files=_UpperCAmelCase ,   features=_UpperCAmelCase ,   **_UpperCAmelCase ,   )


					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							if self.streaming:
									lowercase__	     =						self.builder.as_streaming_dataset(split=self.split						)
							# Build regular (map-style) dataset
							else:
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase ,   download_mode=_UpperCAmelCase ,   verification_mode=_UpperCAmelCase ,   base_path=_UpperCAmelCase ,   num_proc=self.num_proc ,   )
									lowercase__	     =						self.builder.as_dataset(
									    split=self.split ,   verification_mode=_UpperCAmelCase ,   in_memory=self.keep_in_memory						)
							return dataset

