



"""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


_lowerCAmelCase				:					Any							=							logging.get_logger(__name__)






class 	UpperCAmelCase_  (				a_						):
      __SCREAMING_SNAKE_CASE						:   List[str]      = ['''pixel_values''']



      def __init__(     self     :	Tuple     ,	A     :	Any = True     ,	A     :	str = None     ,	A     :	Dict = PIL.Image.NEAREST     ,	A     :	str = False     ,	A     :	Optional[Any] = None     ,	A     :	List[Any] = 1 / 2_5_5     ,	A     :	Union[str, Any] = False     ,	A     :	List[Any] = True     ,	A     :	Tuple = True     ,	A     :	Optional[Any] = None     ,	A     :	List[Any] = None     ,	A     :	Dict = True     ,	**A     :	Optional[Any]     ,	):
           super().__init__(**lowercase_						)
           _UpperCAmelCase			:	Optional[int]								=    size if size is not None else {"height": 3_4_6, "width": 3_4_6}
           _UpperCAmelCase			:	Union[str, Any]								=    get_size_dict(lowercase_						)
           _UpperCAmelCase			:	Union[str, Any]								=    crop_size if crop_size is not None else {"height": 2_8_9, "width": 2_8_9}
           _UpperCAmelCase			:	Any								=    get_size_dict(lowercase_     ,	param_name="crop_size"						)

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



      def 						snake_case_					(     self     :	List[str]     ,	A     :	Any     ,	A     :	Optional[Any]     ,	A     :	int = PIL.Image.NEAREST     ,	A     :	int = None     ,	**A     :	Tuple     ,	):
           _UpperCAmelCase			:	Tuple								=    get_size_dict(lowercase_						)
           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(
               lowercase_     ,	size=(size["height"], size["width"])     ,	resample=lowercase_     ,	data_format=lowercase_     ,	**lowercase_						)



      def 						snake_case_					(     self     :	Union[str, Any]     ,	A     :	Optional[int]     ,	A     :	Any     ,	A     :	Union[str, Any] = None     ,	**A     :	str     ,	):
           _UpperCAmelCase			:	Union[str, Any]								=    get_size_dict(lowercase_						)
           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(lowercase_     ,	size=(size["height"], size["width"])     ,	data_format=lowercase_     ,	**lowercase_						)



      def 						snake_case_					(     self     :	Tuple     ,	A     :	Union[str, Any]     ,	A     :	Dict     ,	A     :	List[str] = True     ,	A     :	Union[str, Any] = None     ,	**A     :	Optional[Any]     ,	):

           if offset:
                _UpperCAmelCase			:	Any								=    (image - 1_2_7.5) * scale
                if data_format is not None:
                     _UpperCAmelCase			:	Union[str, Any]								=    to_channel_dimension_format(lowercase_     ,	lowercase_						)
                _UpperCAmelCase			:	List[Any]								=    rescaled_image.astype(np.floataa						)
           else:
                _UpperCAmelCase			:	Optional[int]								=    rescale(lowercase_     ,	scale=lowercase_     ,	data_format=lowercase_     ,	**lowercase_						)
           return rescaled_image



      def 						snake_case_					(     self     :	str     ,	A     :	List[str]     ,	A     :	str     ,	A     :	Any     ,	A     :	Optional[int] = None     ,	**A     :	str     ,	):
           return normalize(lowercase_     ,	mean=lowercase_     ,	std=lowercase_     ,	data_format=lowercase_     ,	**lowercase_						)



      def 						snake_case_					(     self     :	Optional[Any]     ,	A     :	int     ,	A     :	List[Any] = None     ,	A     :	str = None     ,	A     :	Any=None     ,	A     :	Dict = None     ,	A     :	Dict = None     ,	A     :	int = None     ,	A     :	Any = None     ,	A     :	List[Any] = None     ,	A     :	Union[str, Any] = None     ,	A     :	str = None     ,	A     :	Any = None     ,	A     :	Optional[Any] = None     ,	A     :	Tuple = None     ,	A     :	List[str] = ChannelDimension.FIRST     ,	**A     :	Tuple     ,	):
           _UpperCAmelCase			:	Tuple								=    do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase			:	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			:	Any								=    do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase			:	str								=    rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase			:	int								=    rescale_offset if rescale_offset is not None else self.rescale_offset
           _UpperCAmelCase			:	Tuple								=    do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase			:	Dict								=    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			:	Any								=    include_top if include_top is not None else self.include_top

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

           _UpperCAmelCase			:	int								=    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_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			:	Union[str, Any]								=    [to_numpy_array(lowercase_						) for image in images]

           if do_resize:
                _UpperCAmelCase			:	Optional[int]								=    [self.resize(image=lowercase_     ,	size=lowercase_     ,	resample=lowercase_						) for image in images]

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

           if do_rescale:
                _UpperCAmelCase			:	Any								=    [self.rescale(image=lowercase_     ,	scale=lowercase_     ,	offset=lowercase_						) for image in images]

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

           if include_top:
                _UpperCAmelCase			:	Union[str, Any]								=    [self.normalize(image=lowercase_     ,	mean=[0, 0, 0]     ,	std=lowercase_						) for image in images]

           _UpperCAmelCase			:	List[str]								=    [to_channel_dimension_format(lowercase_     ,	lowercase_						) for image in images]

           _UpperCAmelCase			:	List[str]								=    {"pixel_values": images}
           return BatchFeature(data=lowercase_     ,	tensor_type=lowercase_						)








from dataclasses import dataclass
from typing import Callable, Optional

import datasets




@dataclass
class 							a_ (						datasets.BuilderConfig		):


							'''simple docstring'''






							__a:	Optional[Callable]     =    None
							__a:	Optional[dict]     =    None
							__a:	Optional[datasets.Features]     =    None

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														assert self.generator is not None, "generator must be specified"

														if self.gen_kwargs is None:
																					lowerCAmelCase_										=     {}




class 							a_ (						datasets.GeneratorBasedBuilder		):


							'''simple docstring'''






							__a:	Optional[Any]     =    GeneratorConfig

							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														return datasets.DatasetInfo(features=self.config.features   )

							def      _lowercase       (		self			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs=self.config.gen_kwargs   )]






							def      _lowercase       (		self			,			**lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														for idx, ex in enumerate(self.config.generator(**lowercase_   )   ):
																					yield idx, ex



