






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_						     =  logging.get_logger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	List[str]     =    ['''pixel_values''']

							def __init__(		self			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = PIL.Image.NEAREST			,			lowercase_ = False			,			lowercase_ = None			,			lowercase_ = 1 / 2_5_5			,			lowercase_ = False			,			lowercase_ = True			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = True			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														super().__init__(**lowercase_   )
														lowerCAmelCase_										=     size if size is not None else {'height': 3_4_6, 'width': 3_4_6}
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else {'height': 2_8_9, 'width': 2_8_9}
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     resample
														lowerCAmelCase_										=     do_center_crop
														lowerCAmelCase_										=     crop_size
														lowerCAmelCase_										=     do_rescale
														lowerCAmelCase_										=     rescale_factor
														lowerCAmelCase_										=     rescale_offset
														lowerCAmelCase_										=     do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														lowerCAmelCase_										=     image_std if image_std is not None else IMAGENET_STANDARD_STD
														lowerCAmelCase_										=     include_top

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = PIL.Image.NEAREST			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     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      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     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      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = True			,			lowercase_ = None			,			**lowercase_			,			)					->					Tuple:
														'''simple docstring'''

														if offset:
																					lowerCAmelCase_										=     (image - 1_27.5) * scale
																					if data_format is not None:
																												lowerCAmelCase_										=     to_channel_dimension_format(lowercase_			,			lowercase_   )
																					lowerCAmelCase_										=     rescaled_image.astype(np.floataa   )
														else:
																					lowerCAmelCase_										=     rescale(lowercase_			,			scale=lowercase_			,			data_format=lowercase_			,			**lowercase_   )
														return rescaled_image

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

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






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_=None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = ChannelDimension.FIRST			,			**lowercase_			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase_										=     resample if resample is not None else self.resample
														lowerCAmelCase_										=     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase_										=     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase_										=     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase_										=     rescale_offset if rescale_offset is not None else self.rescale_offset
														lowerCAmelCase_										=     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase_										=     image_std if image_std is not None else self.image_std
														lowerCAmelCase_										=     include_top if include_top is not None else self.include_top

														lowerCAmelCase_										=     size if size is not None else self.size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_										=     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.
														lowerCAmelCase_										=     [to_numpy_array(lowercase_   ) for image in images]

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

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

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

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

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

														lowerCAmelCase_										=     [to_channel_dimension_format(lowercase_			,			lowercase_   ) for image in images]

														lowerCAmelCase_										=     {'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



