


'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCAmelCase				: Dict							    =				logging.get_logger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		int	=	['pixel_values']




				def __init__(self       ,			__lowercase = True       ,			__lowercase = 32       ,			__lowercase=PILImageResampling.BILINEAR       ,			__lowercase = True       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				do_resize
										__lowerCAmelCase							       =				do_rescale
										__lowerCAmelCase							       =				size_divisor
										__lowerCAmelCase							       =				resample
										super().__init__(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				get_image_size(__lowercase		)
										# Rounds the height and width down to the closest multiple of size_divisor
										__lowerCAmelCase							       =				height // size_divisor * size_divisor
										__lowerCAmelCase							       =				width // size_divisor * size_divisor
										__lowerCAmelCase							       =				resize(__lowercase       ,			(new_h, new_w)       ,			resample=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)
										return image




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase		):
										return rescale(image=__lowercase       ,			scale=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase=None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = ChannelDimension.FIRST       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase							       =				do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase							       =				size_divisor if size_divisor is not None else self.size_divisor
										__lowerCAmelCase							       =				resample if resample is not None else self.resample

										if do_resize and size_divisor is None:
																raise ValueError('''size_divisor is required for resizing'''		)

										__lowerCAmelCase							       =				make_list_of_images(__lowercase		)

										if not valid_images(__lowercase		):
																raise ValueError('''Invalid image(s)'''		)

										# All transformations expect numpy arrays.
										__lowerCAmelCase							       =				[to_numpy_array(__lowercase		) for img in images]

										if do_resize:
																__lowerCAmelCase							       =				[self.resize(__lowercase       ,			size_divisor=__lowercase       ,			resample=__lowercase		) for image in images]

										if do_rescale:
																__lowerCAmelCase							       =				[self.rescale(__lowercase       ,			scale=1 / 2_55		) 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		)





'''simple docstring'''

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

