





'''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, is_vision_available, logging


if is_vision_available():
			import PIL


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Tuple 			=		['''pixel_values''']




		def __init__(self      :	List[str]							,			A__      :	bool = True							,			A__      :	Dict[str, int] = None							,			A__      :	float = None							,			A__      :	PILImageResampling = PILImageResampling.BILINEAR							,			A__      :	bool = True							,			A__      :	Union[int, float] = 1 / 2_5_5							,			A__      :	bool = True							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			**A__      :	Union[str, Any]							,			)					-> None:
						super().__init__(**A__				)
						lowercase         = size if size is not None else {"shortest_edge": 3_8_4}
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)

						lowercase         = do_resize
						lowercase         = size
						# Default value set here for backwards compatibility where the value in config is None
						lowercase         = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
						lowercase         = resample
						lowercase         = do_rescale
						lowercase         = rescale_factor
						lowercase         = do_normalize
						lowercase         = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowercase         = image_std if image_std is not None else IMAGENET_STANDARD_STD




		def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	np.ndarray							,			A__      :	Dict[str, int]							,			A__      :	float							,			A__      :	PILImageResampling = PILImageResampling.BICUBIC							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Optional[int]							,			)					-> np.ndarray:
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)
						if "shortest_edge" not in size:
										raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'				)
						lowercase         = size["shortest_edge"]

						if shortest_edge < 3_8_4:
										# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
										lowercase         = int(shortest_edge / crop_pct				)
										lowercase         = get_resize_output_image_size(A__							,			size=A__							,			default_to_square=A__				)
										lowercase         = resize(image=A__							,			size=A__							,			resample=A__							,			data_format=A__							,			**A__				)
										# then crop to (shortest_edge, shortest_edge)
										return center_crop(image=A__							,			size=(shortest_edge, shortest_edge)							,			data_format=A__							,			**A__				)
						else:
										# warping (no cropping) when evaluated at 384 or larger
										return resize(
										    A__							,			size=(shortest_edge, shortest_edge)							,			resample=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	np.ndarray							,			A__      :	Union[int, float]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	List[str]							,			)					-> Union[str, Any]:
						return rescale(A__							,			scale=A__							,			data_format=A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	np.ndarray							,			A__      :	Union[float, List[float]]							,			A__      :	Union[float, List[float]]							,			A__      :	Optional[Union[str, ChannelDimension]] = None							,			**A__      :	Dict							,			)					-> np.ndarray:
						return normalize(A__							,			mean=A__							,			std=A__							,			data_format=A__							,			**A__				)





		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	ImageInput							,			A__      :	bool = None							,			A__      :	Dict[str, int] = None							,			A__      :	float = None							,			A__      :	PILImageResampling = None							,			A__      :	bool = None							,			A__      :	float = None							,			A__      :	bool = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[float, List[float]]] = None							,			A__      :	Optional[Union[str, TensorType]] = None							,			A__      :	ChannelDimension = ChannelDimension.FIRST							,			**A__      :	Tuple							,			)					-> PIL.Image.Image:
						lowercase         = do_resize if do_resize is not None else self.do_resize
						lowercase         = crop_pct if crop_pct is not None else self.crop_pct
						lowercase         = resample if resample is not None else self.resample
						lowercase         = do_rescale if do_rescale is not None else self.do_rescale
						lowercase         = rescale_factor if rescale_factor is not None else self.rescale_factor
						lowercase         = do_normalize if do_normalize is not None else self.do_normalize
						lowercase         = image_mean if image_mean is not None else self.image_mean
						lowercase         = image_std if image_std is not None else self.image_std

						lowercase         = size if size is not None else self.size
						lowercase         = get_size_dict(A__							,			default_to_square=A__				)

						lowercase         = make_list_of_images(A__				)

						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."				)

						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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
										raise ValueError("crop_pct must be specified if size < 384."				)

						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.
						lowercase         = [to_numpy_array(A__				) for image in images]

						if do_resize:
										lowercase         = [self.resize(image=A__							,			size=A__							,			crop_pct=A__							,			resample=A__				) for image in images]

						if do_rescale:
										lowercase         = [self.rescale(image=A__							,			scale=A__				) for image in images]

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

						lowercase         = [to_channel_dimension_format(A__							,			A__				) for image in images]

						lowercase         = {"pixel_values": images}
						return BatchFeature(data=A__							,			tensor_type=A__				)











'''simple docstring'''

# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__lowerCamelCase : Any											=			open  # noqa: we just need to have a builtin inside this module to test it properly




