






'''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_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


lowerCamelCase_				: Optional[Any]    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Any					     =					["pixel_values"]






			def __init__(   self							:						Dict ,      lowercase							:						bool = True ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						float = None ,      lowercase							:						PILImageResampling = PILImageResampling.BILINEAR ,      lowercase							:						bool = True ,      lowercase							:						Union[int, float] = 1 / 2_5_5 ,      lowercase							:						bool = True ,      lowercase							:						Optional[Union[float, List[float]]] = None ,      lowercase							:						Optional[Union[float, List[float]]] = None ,      **lowercase							:						Union[str, Any] ,      )							->    None:

									'''simple docstring'''







									super().__init__(**lowercase			)
									UpperCamelCase__         = size if size is not None else {"""shortest_edge""": 3_8_4}
									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)

									UpperCamelCase__         = do_resize
									UpperCamelCase__         = size
									# Default value set here for backwards compatibility where the value in config is None
									UpperCamelCase__         = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
									UpperCamelCase__         = resample
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = rescale_factor
									UpperCamelCase__         = do_normalize
									UpperCamelCase__         = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCamelCase__         = image_std if image_std is not None else IMAGENET_STANDARD_STD






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

									'''simple docstring'''







									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)
									if "shortest_edge" not in size:
															raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"			)
									UpperCamelCase__         = size["""shortest_edge"""]

									if shortest_edge < 3_8_4:
															# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
															UpperCamelCase__         = int(shortest_edge / crop_pct			)
															UpperCamelCase__         = get_resize_output_image_size(lowercase ,      size=lowercase ,      default_to_square=lowercase			)
															UpperCamelCase__         = resize(image=lowercase ,      size=lowercase ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)
															# then crop to (shortest_edge, shortest_edge)
															return center_crop(image=lowercase ,      size=(shortest_edge, shortest_edge) ,      data_format=lowercase ,      **lowercase			)
									else:
															# warping (no cropping) when evaluated at 384 or larger
															return resize(
															    lowercase ,      size=(shortest_edge, shortest_edge) ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						np.ndarray ,      lowercase							:						Union[int, float] ,      lowercase							:						Optional[Union[str, ChannelDimension]] = None ,      **lowercase							:						Optional[int] ,      )							->    List[Any]:

									'''simple docstring'''







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






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

									'''simple docstring'''







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







			def 				A      (   self							:						Dict ,      lowercase							:						ImageInput ,      lowercase							:						bool = None ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						float = None ,      lowercase							:						PILImageResampling = 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							:						int ,      )							->    PIL.Image.Image:

									'''simple docstring'''







									UpperCamelCase__         = do_resize if do_resize is not None else self.do_resize
									UpperCamelCase__         = crop_pct if crop_pct is not None else self.crop_pct
									UpperCamelCase__         = resample if resample is not None else self.resample
									UpperCamelCase__         = do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase__         = rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCamelCase__         = do_normalize if do_normalize is not None else self.do_normalize
									UpperCamelCase__         = image_mean if image_mean is not None else self.image_mean
									UpperCamelCase__         = image_std if image_std is not None else self.image_std

									UpperCamelCase__         = size if size is not None else self.size
									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)

									UpperCamelCase__         = 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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
															raise ValueError("""crop_pct must be specified if size < 384."""			)

									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__         = [to_numpy_array(lowercase			) for image in images]

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

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

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

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

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








'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						pyspark.sql.DataFrame ,      lowercase							:						Optional[NamedSplit] = None ,      lowercase							:						Optional[Features] = None ,      lowercase							:						bool = True ,      lowercase							:						str = None ,      lowercase							:						bool = False ,      lowercase							:						str = None ,      lowercase							:						bool = True ,      lowercase							:						str = "arrow" ,      **lowercase							:						Any ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(
									    split=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      keep_in_memory=lowercase ,      streaming=lowercase ,      **lowercase ,      )
									UpperCamelCase__         = load_from_cache_file
									UpperCamelCase__         = file_format
									UpperCamelCase__         = Spark(
									    df=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      working_dir=lowercase ,      **lowercase ,      )







			def 				A      (   self							:						int			)							->    Any:

									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									UpperCamelCase__         = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowercase ,      file_format=self._file_format ,      )
									return self.builder.as_dataset(split=self.split			)
