





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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__UpperCamelCase:  Tuple   =   logging.get_logger(__name__)


if is_vision_available():
			import PIL


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							["pixel_values"]

	def __init__(	self:	Tuple,				lowerCamelCase_:	bool = True,				lowerCamelCase_:	Dict[str, int] = None,				lowerCamelCase_:	PILImageResampling = PILImageResampling.BILINEAR,				lowerCamelCase_:	bool = True,				lowerCamelCase_:	bool = False,				lowerCamelCase_:	bool = True,				lowerCamelCase_:	bool = True,				lowerCamelCase_:	Union[int, float] = 1 / 255,				lowerCamelCase_:	bool = True,				lowerCamelCase_:	Optional[Union[float, List[float]]] = None,				lowerCamelCase_:	Optional[Union[float, List[float]]] = None,				**lowerCamelCase_:	Tuple,				):
			super().__init__(**lowerCamelCase_ )

			lowercase__				:			Union[str, Any]            =		size if size is not None else {'height': 2560, 'width': 1920}
			if isinstance(lowerCamelCase_,				(tuple, list) ):
					# The previous feature extractor size parameter was in (width, height) format
					lowercase__				:			int            =		size[::-1]
			lowercase__				:			str            =		get_size_dict(lowerCamelCase_ )

			lowercase__				:			int            =		do_resize
			lowercase__				:			int            =		size
			lowercase__				:			int            =		resample
			lowercase__				:			int            =		do_thumbnail
			lowercase__				:			Dict            =		do_align_long_axis
			lowercase__				:			Optional[int]            =		do_pad
			lowercase__				:			Any            =		do_rescale
			lowercase__				:			Any            =		rescale_factor
			lowercase__				:			str            =		do_normalize
			lowercase__				:			List[str]            =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowercase__				:			Optional[Any]            =		image_std if image_std is not None else IMAGENET_STANDARD_STD

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	np.ndarray,				lowerCamelCase_:	Dict[str, int],				lowerCamelCase_:	Optional[Union[str, ChannelDimension]] = None ):
			lowercase__      ,							lowercase__				:			Dict            =		get_image_size(lowerCamelCase_ )
			lowercase__      ,							lowercase__				:			Dict            =		size['height'], size['width']

			if (output_width < output_height and input_width > input_height) or (
			    output_width > output_height and input_width < input_height
			):
					lowercase__				:			Optional[int]            =		np.rotaa(lowerCamelCase_,				3 )

			if data_format is not None:
					lowercase__				:			Optional[int]            =		to_channel_dimension_format(lowerCamelCase_,				lowerCamelCase_ )

			return image

	def       snake_case__(	self:	str,				lowerCamelCase_:	np.ndarray,				lowerCamelCase_:	Dict[str, int],				lowerCamelCase_:	bool = False,				lowerCamelCase_:	Optional[Union[str, ChannelDimension]] = None,				):
			lowercase__      ,							lowercase__				:			List[str]            =		size['height'], size['width']
			lowercase__      ,							lowercase__				:			Optional[int]            =		get_image_size(lowerCamelCase_ )

			lowercase__				:			Dict            =		output_width - input_width
			lowercase__				:			Tuple            =		output_height - input_height

			if random_padding:
					lowercase__				:			int            =		np.random.randint(low=0,				high=delta_height + 1 )
					lowercase__				:			List[Any]            =		np.random.randint(low=0,				high=delta_width + 1 )
			else:
					lowercase__				:			str            =		delta_height // 2
					lowercase__				:			List[str]            =		delta_width // 2

			lowercase__				:			int            =		delta_height - pad_top
			lowercase__				:			int            =		delta_width - pad_left

			lowercase__				:			int            =		((pad_top, pad_bottom), (pad_left, pad_right))
			return pad(lowerCamelCase_,				lowerCamelCase_,				data_format=lowerCamelCase_ )

	def       snake_case__(	self:	int,				*lowerCamelCase_:	Optional[Any],				**lowerCamelCase_:	List[str] ):
			logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.' )
			return self.pad_image(*lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	np.ndarray,				lowerCamelCase_:	Dict[str, int],				lowerCamelCase_:	PILImageResampling = PILImageResampling.BICUBIC,				lowerCamelCase_:	Optional[Union[str, ChannelDimension]] = None,				**lowerCamelCase_:	Optional[Any],				):
			lowercase__      ,							lowercase__				:			Any            =		get_image_size(lowerCamelCase_ )
			lowercase__      ,							lowercase__				:			Optional[Any]            =		size['height'], size['width']

			# We always resize to the smallest of either the input or output size.
			lowercase__				:			Union[str, Any]            =		min(lowerCamelCase_,				lowerCamelCase_ )
			lowercase__				:			List[Any]            =		min(lowerCamelCase_,				lowerCamelCase_ )

			if height == input_height and width == input_width:
					return image

			if input_height > input_width:
					lowercase__				:			Optional[Any]            =		int(input_width * height / input_height )
			elif input_width > input_height:
					lowercase__				:			str            =		int(input_height * width / input_width )

			return resize(
			    lowerCamelCase_,				size=(height, width),				resample=lowerCamelCase_,				reducing_gap=2.0,				data_format=lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	str,				lowerCamelCase_:	np.ndarray,				lowerCamelCase_:	Dict[str, int],				lowerCamelCase_:	PILImageResampling = PILImageResampling.BICUBIC,				lowerCamelCase_:	Optional[Union[str, ChannelDimension]] = None,				**lowerCamelCase_:	str,				):
			lowercase__				:			Optional[int]            =		get_size_dict(lowerCamelCase_ )
			lowercase__				:			int            =		min(size['height'],				size['width'] )
			lowercase__				:			Optional[int]            =		get_resize_output_image_size(lowerCamelCase_,				size=lowerCamelCase_,				default_to_square=lowerCamelCase_ )
			lowercase__				:			List[str]            =		resize(lowerCamelCase_,				size=lowerCamelCase_,				resample=lowerCamelCase_,				data_format=lowerCamelCase_,				**lowerCamelCase_ )
			return resized_image

	def       snake_case__(	self:	int,				lowerCamelCase_:	np.ndarray,				lowerCamelCase_:	Union[int, float],				lowerCamelCase_:	Optional[Union[str, ChannelDimension]] = None,				**lowerCamelCase_:	Tuple,				):
			return rescale(lowerCamelCase_,				scale=lowerCamelCase_,				data_format=lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	str,				lowerCamelCase_:	np.ndarray,				lowerCamelCase_:	Union[float, List[float]],				lowerCamelCase_:	Union[float, List[float]],				lowerCamelCase_:	Optional[Union[str, ChannelDimension]] = None,				**lowerCamelCase_:	Union[str, Any],				):
			return normalize(lowerCamelCase_,				mean=lowerCamelCase_,				std=lowerCamelCase_,				data_format=lowerCamelCase_,				**lowerCamelCase_ )





	def       snake_case__(	self:	Dict,				lowerCamelCase_:	ImageInput,				lowerCamelCase_:	bool = None,				lowerCamelCase_:	Dict[str, int] = None,				lowerCamelCase_:	PILImageResampling = None,				lowerCamelCase_:	bool = None,				lowerCamelCase_:	bool = None,				lowerCamelCase_:	bool = None,				lowerCamelCase_:	bool = False,				lowerCamelCase_:	bool = None,				lowerCamelCase_:	float = None,				lowerCamelCase_:	bool = None,				lowerCamelCase_:	Optional[Union[float, List[float]]] = None,				lowerCamelCase_:	Optional[Union[float, List[float]]] = None,				lowerCamelCase_:	Optional[Union[str, TensorType]] = None,				lowerCamelCase_:	Optional[ChannelDimension] = ChannelDimension.FIRST,				**lowerCamelCase_:	str,				):
			lowercase__				:			Tuple            =		do_resize if do_resize is not None else self.do_resize
			lowercase__				:			List[Any]            =		size if size is not None else self.size
			if isinstance(lowerCamelCase_,				(tuple, list) ):
					# Previous feature extractor had size in (width, height) format
					lowercase__				:			Tuple            =		size[::-1]
			lowercase__				:			Optional[int]            =		get_size_dict(lowerCamelCase_ )
			lowercase__				:			int            =		resample if resample is not None else self.resample
			lowercase__				:			List[str]            =		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
			lowercase__				:			Optional[int]            =		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
			lowercase__				:			Optional[Any]            =		do_pad if do_pad is not None else self.do_pad
			lowercase__				:			int            =		do_rescale if do_rescale is not None else self.do_rescale
			lowercase__				:			Dict            =		rescale_factor if rescale_factor is not None else self.rescale_factor
			lowercase__				:			Optional[Any]            =		do_normalize if do_normalize is not None else self.do_normalize
			lowercase__				:			str            =		image_mean if image_mean is not None else self.image_mean
			lowercase__				:			Optional[int]            =		image_std if image_std is not None else self.image_std

			lowercase__				:			Any            =		make_list_of_images(lowerCamelCase_ )

			if not valid_images(lowerCamelCase_ ):
					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_rescale and rescale_factor is None:
					raise ValueError('Rescale factor must be specified if do_rescale is True.' )

			if do_pad and size is None:
					raise ValueError('Size must be specified if do_pad 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__				:			Tuple            =		[to_numpy_array(lowerCamelCase_ ) for image in images]

			if do_align_long_axis:
					lowercase__				:			str            =		[self.align_long_axis(lowerCamelCase_,				size=lowerCamelCase_ ) for image in images]

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

			if do_thumbnail:
					lowercase__				:			Any            =		[self.thumbnail(image=lowerCamelCase_,				size=lowerCamelCase_ ) for image in images]

			if do_pad:
					lowercase__				:			Any            =		[self.pad_image(image=lowerCamelCase_,				size=lowerCamelCase_,				random_padding=lowerCamelCase_ ) for image in images]

			if do_rescale:
					lowercase__				:			Optional[int]            =		[self.rescale(image=lowerCamelCase_,				scale=lowerCamelCase_ ) for image in images]

			if do_normalize:
					lowercase__				:			Optional[int]            =		[self.normalize(image=lowerCamelCase_,				mean=lowerCamelCase_,				std=lowerCamelCase_ ) for image in images]

			lowercase__				:			Optional[int]            =		[to_channel_dimension_format(lowerCamelCase_,				lowerCamelCase_ ) for image in images]

			lowercase__				:			Optional[Any]            =		{'pixel_values': images}
			return BatchFeature(data=lowerCamelCase_,				tensor_type=lowerCamelCase_ )









from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		self.watermark_image_as_pil.resize(
			    (wm_size, wm_size),				PIL_INTERPOLATION['bicubic'],				reducing_gap=lowerCamelCase_ )

			for pil_img in images:
					pil_img.paste(lowerCamelCase_,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y),				mask=wm_img.split()[-1] )

			return images


