
"""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, logging


lowerCamelCase__           =	logging.get_logger(__name__)




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			['pixel_values']

		def __init__(   self ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = 1 / 2_55 ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Tuple		    =   size if size is not None else {'shortest_edge': 2_56}
			__lowerCAmelCase    :		int		    =   get_size_dict(_SCREAMING_SNAKE_CASE ,	default_to_square=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
			__lowerCAmelCase    :		List[str]		    =   get_size_dict(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   do_resize
			__lowerCAmelCase    :		Any		    =   size
			__lowerCAmelCase    :		List[Any]		    =   resample
			__lowerCAmelCase    :		Any		    =   do_center_crop
			__lowerCAmelCase    :		Optional[Any]		    =   crop_size
			__lowerCAmelCase    :		List[str]		    =   do_rescale
			__lowerCAmelCase    :		int		    =   rescale_factor
			__lowerCAmelCase    :		Dict		    =   do_normalize
			__lowerCAmelCase    :		str		    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__lowerCAmelCase    :		str		    =   image_std if image_std is not None else IMAGENET_STANDARD_STD

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		List[Any]		    =   get_size_dict(_SCREAMING_SNAKE_CASE ,	default_to_square=_SCREAMING_SNAKE_CASE				)
			if "shortest_edge" not in size:
				raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
			__lowerCAmelCase    :		Tuple		    =   get_resize_output_image_size(_SCREAMING_SNAKE_CASE ,	size=size['shortest_edge'] ,	default_to_square=_SCREAMING_SNAKE_CASE				)
			return resize(_SCREAMING_SNAKE_CASE ,	size=_SCREAMING_SNAKE_CASE ,	resample=_SCREAMING_SNAKE_CASE ,	data_format=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Dict		    =   get_size_dict(_SCREAMING_SNAKE_CASE				)
			return center_crop(_SCREAMING_SNAKE_CASE ,	size=(size['height'], size['width']) ,	data_format=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE				):
			return rescale(_SCREAMING_SNAKE_CASE ,	scale=_SCREAMING_SNAKE_CASE ,	data_format=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			return normalize(_SCREAMING_SNAKE_CASE ,	mean=_SCREAMING_SNAKE_CASE ,	std=_SCREAMING_SNAKE_CASE ,	data_format=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		List[str]		    =   do_resize if do_resize is not None else self.do_resize
			__lowerCAmelCase    :		List[Any]		    =   size if size is not None else self.size
			__lowerCAmelCase    :		List[Any]		    =   get_size_dict(_SCREAMING_SNAKE_CASE ,	default_to_square=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   resample if resample is not None else self.resample
			__lowerCAmelCase    :		Tuple		    =   do_center_crop if do_center_crop is not None else self.do_center_crop
			__lowerCAmelCase    :		Union[str, Any]		    =   crop_size if crop_size is not None else self.crop_size
			__lowerCAmelCase    :		str		    =   get_size_dict(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   do_rescale if do_rescale is not None else self.do_rescale
			__lowerCAmelCase    :		str		    =   rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowerCAmelCase    :		Union[str, Any]		    =   do_normalize if do_normalize is not None else self.do_normalize
			__lowerCAmelCase    :		List[str]		    =   image_mean if image_mean is not None else self.image_mean
			__lowerCAmelCase    :		Tuple		    =   image_std if image_std is not None else self.image_std

			__lowerCAmelCase    :		List[str]		    =   make_list_of_images(_SCREAMING_SNAKE_CASE				)

			if not valid_images(_SCREAMING_SNAKE_CASE				):
				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:
				raise ValueError('Size 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    :		Union[str, Any]		    =   [to_numpy_array(_SCREAMING_SNAKE_CASE				) for image in images]

			if do_resize:
				__lowerCAmelCase    :		Union[str, Any]		    =   [self.resize(image=_SCREAMING_SNAKE_CASE ,	size=_SCREAMING_SNAKE_CASE ,	resample=_SCREAMING_SNAKE_CASE				) for image in images]

			if do_center_crop:
				__lowerCAmelCase    :		Dict		    =   [self.center_crop(image=_SCREAMING_SNAKE_CASE ,	size=_SCREAMING_SNAKE_CASE				) for image in images]

			if do_rescale:
				__lowerCAmelCase    :		Any		    =   [self.rescale(image=_SCREAMING_SNAKE_CASE ,	scale=_SCREAMING_SNAKE_CASE				) for image in images]

			if do_normalize:
				__lowerCAmelCase    :		Optional[Any]		    =   [self.normalize(image=_SCREAMING_SNAKE_CASE ,	mean=_SCREAMING_SNAKE_CASE ,	std=_SCREAMING_SNAKE_CASE				) for image in images]

			__lowerCAmelCase    :		int		    =   [to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) for image in images]

			__lowerCAmelCase    :		Any		    =   {'pixel_values': images}
			return BatchFeature(data=_SCREAMING_SNAKE_CASE ,	tensor_type=_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip