





"""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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


__magic_name__       :					str						=			logging.get_logger(__name__)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if isinstance(SCREAMING_SNAKE_CASE							,     (list, tuple)     ) and isinstance(videos[0]							,     (list, tuple)     ) and is_valid_image(videos[0][0]     ):
												return videos

						elif isinstance(SCREAMING_SNAKE_CASE							,     (list, tuple)     ) and is_valid_image(videos[0]     ):
												return [videos]

						elif is_valid_image(SCREAMING_SNAKE_CASE     ):
												return [[videos]]

						raise ValueError(f"""Could not make batched video from {videos}"""     )






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				int										=  ["""pixel_values"""]







			def __init__(      self						,   _A = True						,   _A = None						,   _A = PILImageResampling.BILINEAR						,   _A = True						,   _A = None						,   _A = True						,   _A = 1 / 2_5_5						,   _A = True						,   _A = None						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )
									UpperCamelCase				:		Optional[Any]				   =			size if size is not None else {"""shortest_edge""": 2_2_4}
									UpperCamelCase				:		List[Any]				   =			get_size_dict(_A						,   default_to_square=_A    )
									UpperCamelCase				:		Dict				   =			crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
									UpperCamelCase				:		Optional[Any]				   =			get_size_dict(_A						,   param_name="""crop_size"""    )

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







			def   _a      (      self						,   _A						,   _A						,   _A = PILImageResampling.BILINEAR						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			get_size_dict(_A						,   default_to_square=_A    )
									if "shortest_edge" in size:
															UpperCamelCase				:		List[Any]				   =			get_resize_output_image_size(_A						,   size["""shortest_edge"""]						,   default_to_square=_A    )
									elif "height" in size and "width" in size:
															UpperCamelCase				:		List[str]				   =			(size["""height"""], size["""width"""])
									else:
															raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""    )
									return resize(_A						,   size=_A						,   resample=_A						,   data_format=_A						,   **_A    )







			def   _a      (      self						,   _A						,   _A						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			get_size_dict(_A    )
									if "height" not in size or "width" not in size:
															raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""    )
									return center_crop(_A						,   size=(size["""height"""], size["""width"""])						,   data_format=_A						,   **_A    )







			def   _a      (      self						,   _A						,   _A						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = ChannelDimension.FIRST						,   ):






									'''simple docstring'''







									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.
									UpperCamelCase				:		List[Any]				   =			to_numpy_array(_A    )

									if do_resize:
															UpperCamelCase				:		Optional[int]				   =			self.resize(image=_A						,   size=_A						,   resample=_A    )

									if do_center_crop:
															UpperCamelCase				:		Optional[Any]				   =			self.center_crop(_A						,   size=_A    )

									if do_rescale:
															UpperCamelCase				:		List[str]				   =			self.rescale(image=_A						,   scale=_A    )

									if do_normalize:
															UpperCamelCase				:		Dict				   =			self.normalize(image=_A						,   mean=_A						,   std=_A    )

									UpperCamelCase				:		Optional[int]				   =			to_channel_dimension_format(_A						,   _A    )
									return image







			def   _a      (      self						,   _A						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = ChannelDimension.FIRST						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			do_resize if do_resize is not None else self.do_resize
									UpperCamelCase				:		Tuple				   =			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				:		str				   =			do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase				:		List[Any]				   =			rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCamelCase				:		Any				   =			do_normalize if do_normalize is not None else self.do_normalize
									UpperCamelCase				:		str				   =			image_mean if image_mean is not None else self.image_mean
									UpperCamelCase				:		Dict				   =			image_std if image_std is not None else self.image_std

									UpperCamelCase				:		List[str]				   =			size if size is not None else self.size
									UpperCamelCase				:		Optional[Any]				   =			get_size_dict(_A						,   default_to_square=_A    )
									UpperCamelCase				:		List[str]				   =			crop_size if crop_size is not None else self.crop_size
									UpperCamelCase				:		List[Any]				   =			get_size_dict(_A						,   param_name="""crop_size"""    )

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

									UpperCamelCase				:		List[Any]				   =			make_batched(_A    )

									UpperCamelCase				:		Optional[int]				   =			[
									    [
									        self._preprocess_image(
									            image=_A						,   do_resize=_A						,   size=_A						,   resample=_A						,   do_center_crop=_A						,   crop_size=_A						,   do_rescale=_A						,   rescale_factor=_A						,   do_normalize=_A						,   image_mean=_A						,   image_std=_A						,   data_format=_A						,   )
									        for img in video
									    ]
									    for video in videos
									]

									UpperCamelCase				:		List[str]				   =			{"""pixel_values""": videos}
									return BatchFeature(data=_A						,   tensor_type=_A    )










"""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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = None						,   _A = None						,   _A = True						,   _A = None						,   _A = False						,   _A = None						,   _A = True						,   _A = "arrow"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    split=_A						,   features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   streaming=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[Any]				   =			load_from_cache_file
									UpperCamelCase				:		List[str]				   =			file_format
									UpperCamelCase				:		Optional[Any]				   =			Spark(
									    df=_A						,   features=_A						,   cache_dir=_A						,   working_dir=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







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



