





"""simple docstring"""



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 						__snake_case   :





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=sys.maxsize    ):



     '''simple docstring'''




     __A       :		List[Any]							=	'''bilinear'''
     __A       :		Optional[int]							=	max_size
     __A       :		Tuple							=	short_edge_length







 def __call__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		int							=	[]
     for img in imgs:
         __A					,			__A       :		List[Any]							=	img.shape[:2]
         # later: provide list and randomly choose index for resize
         __A       :		List[Any]							=	np.random.randint(self.short_edge_length[0]   ,		self.short_edge_length[1] + 1    )
         if size == 0:
             return img
         __A       :		Tuple							=	size * 1.0 / min(__lowerCamelCase   ,		__lowerCamelCase    )
         if h < w:
             __A					,			__A       :		List[Any]							=	size, scale * w
         else:
             __A					,			__A       :		Union[str, Any]							=	scale * h, size
         if max(__lowerCamelCase   ,		__lowerCamelCase    ) > self.max_size:
             __A       :		str							=	self.max_size * 1.0 / max(__lowerCamelCase   ,		__lowerCamelCase    )
             __A       :		Any							=	newh * scale
             __A       :		Dict							=	neww * scale
         __A       :		Optional[int]							=	int(neww + 0.5    )
         __A       :		List[str]							=	int(newh + 0.5    )

         if img.dtype == np.uinta:
             __A       :		Optional[Any]							=	Image.fromarray(__lowerCamelCase    )
             __A       :		int							=	pil_image.resize((neww, newh)   ,		PILImageResampling.BILINEAR    )
             __A       :		str							=	np.asarray(__lowerCamelCase    )
         else:
             __A       :		List[str]							=	img.permute(2   ,		0   ,		1    ).unsqueeze(0    )  # 3, 0, 1)  # hw(c) -> nchw
             __A       :		List[Any]							=	nn.functional.interpolate(
                 __lowerCamelCase   ,		(newh, neww)   ,		mode=self.interp_method   ,		align_corners=__lowerCamelCase    ).squeeze(0    )
         img_augs.append(__lowerCamelCase    )

     return img_augs






class 						__snake_case   :





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		str							=	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]   ,		cfg.INPUT.MAX_SIZE_TEST    )
     __A       :		List[str]							=	cfg.INPUT.FORMAT
     __A       :		int							=	cfg.SIZE_DIVISIBILITY
     __A       :		Any							=	cfg.PAD_VALUE
     __A       :		Union[str, Any]							=	cfg.INPUT.MAX_SIZE_TEST
     __A       :		Any							=	cfg.MODEL.DEVICE
     __A       :		List[str]							=	torch.tensor(cfg.MODEL.PIXEL_STD    ).to(self.device    ).view(len(cfg.MODEL.PIXEL_STD    )   ,		1   ,		1    )
     __A       :		Dict							=	torch.tensor(cfg.MODEL.PIXEL_MEAN    ).to(self.device    ).view(len(cfg.MODEL.PIXEL_STD    )   ,		1   ,		1    )
     __A       :		Dict							=	lambda __lowerCamelCase    : (x - self.pixel_mean) / self.pixel_std

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	tuple(max(__lowerCamelCase    ) for s in zip(*[img.shape for img in images]    )    )
     __A       :		Tuple							=	[im.shape[-2:] for im in images]
     __A       :		List[str]							=	[
         nn.functional.pad(
             __lowerCamelCase   ,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]   ,		value=self.pad_value   ,		)
         for size, im in zip(__lowerCamelCase   ,		__lowerCamelCase    )
     ]

     return torch.stack(__lowerCamelCase    ), torch.tensor(__lowerCamelCase    )







 def __call__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     with torch.no_grad():
         if not isinstance(__lowerCamelCase   ,		__lowerCamelCase    ):
             __A       :		List[str]							=	[images]
         if single_image:
             assert len(__lowerCamelCase    ) == 1
         for i in range(len(__lowerCamelCase    )    ):
             if isinstance(images[i]   ,		torch.Tensor    ):
                 images.insert(__lowerCamelCase   ,		images.pop(__lowerCamelCase    ).to(self.device    ).float()    )
             elif not isinstance(images[i]   ,		torch.Tensor    ):
                 images.insert(
                     __lowerCamelCase   ,		torch.as_tensor(img_tensorize(images.pop(__lowerCamelCase    )   ,		input_format=self.input_format    )    )
                     .to(self.device    )
                     .float()   ,		)
            # resize smallest edge
         __A       :		Union[str, Any]							=	torch.tensor([im.shape[:2] for im in images]    )
         __A       :		int							=	self.aug(__lowerCamelCase    )
         # transpose images and convert to torch tensors
         # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
         # now normalize before pad to avoid useless arithmetic
         __A       :		Optional[int]							=	[self.normalizer(__lowerCamelCase    ) for x in images]
         # now pad them to do the following operations
         __A					,			__A       :		Dict							=	self.pad(__lowerCamelCase    )
         # Normalize

         if self.size_divisibility > 0:
             raise NotImplementedError()
         # pad
         __A       :		List[str]							=	torch.true_divide(__lowerCamelCase   ,		__lowerCamelCase    )
         if single_image:
             return images[0], sizes[0], scales_yx[0]
         else:
             return images, sizes, scales_yx







def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    str		)  ->Dict:



    '''simple docstring'''







    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes







def 						__lowercase	(  snake_case_    :    Tuple							,snake_case_    :    Tuple[int, int]		)  ->Any:



    '''simple docstring'''







    assert torch.isfinite(snake_case_		).all(), "Box tensor contains infinite or NaN!"
    __A					,			__A       :		str							=	box_size
    tensor[:, 0].clamp_(min=0							,max=snake_case_		)
    tensor[:, 1].clamp_(min=0							,max=snake_case_		)
    tensor[:, 2].clamp_(min=0							,max=snake_case_		)
    tensor[:, 3].clamp_(min=0							,max=snake_case_		)











"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a_					=     logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	["""pixel_values"""]

 def __init__(       self   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		__lowerCamelCase = 1 / 255   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase    )
     __A       :		Optional[int]							=	size if size is not None else {'''shortest_edge''': 224}
     __A       :		Optional[int]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     __A       :		Dict							=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     __A       :		str							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase   ,		param_name='''crop_size'''    )

     __A       :		Dict							=	do_resize
     __A       :		Optional[Any]							=	size
     __A       :		Optional[Any]							=	resample
     __A       :		Dict							=	do_center_crop
     __A       :		Any							=	crop_size
     __A       :		Optional[Any]							=	do_rescale
     __A       :		List[Any]							=	rescale_factor
     __A       :		Union[str, Any]							=	do_normalize
     __A       :		Any							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     __A       :		Optional[int]							=	image_std if image_std is not None else OPENAI_CLIP_STD
     __A       :		Dict							=	do_convert_rgb

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
     __A       :		Optional[int]							=	get_resize_output_image_size(__lowerCamelCase   ,		size=size['''shortest_edge''']   ,		default_to_square=__lowerCamelCase    )
     return resize(__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase    )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
     return center_crop(__lowerCamelCase   ,		size=(size['''height'''], size['''width'''])   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     return rescale(__lowerCamelCase   ,		scale=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = ChannelDimension.FIRST   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Tuple							=	do_resize if do_resize is not None else self.do_resize
     __A       :		Any							=	size if size is not None else self.size
     __A       :		List[str]							=	get_size_dict(__lowerCamelCase   ,		param_name='''size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[Any]							=	resample if resample is not None else self.resample
     __A       :		Any							=	do_center_crop if do_center_crop is not None else self.do_center_crop
     __A       :		Dict							=	crop_size if crop_size is not None else self.crop_size
     __A       :		List[Any]							=	get_size_dict(__lowerCamelCase   ,		param_name='''crop_size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[str]							=	do_rescale if do_rescale is not None else self.do_rescale
     __A       :		List[Any]							=	rescale_factor if rescale_factor is not None else self.rescale_factor
     __A       :		Tuple							=	do_normalize if do_normalize is not None else self.do_normalize
     __A       :		str							=	image_mean if image_mean is not None else self.image_mean
     __A       :		int							=	image_std if image_std is not None else self.image_std
     __A       :		Union[str, Any]							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     __A       :		int							=	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_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.'''    )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         __A       :		List[str]							=	[convert_to_rgb(__lowerCamelCase    ) for image in images]

     # All transformations expect numpy arrays.
     __A       :		Optional[int]							=	[to_numpy_array(__lowerCamelCase    ) for image in images]

     if do_resize:
         __A       :		List[str]							=	[self.resize(image=__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase    ) for image in images]

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

     if do_rescale:
         __A       :		str							=	[self.rescale(image=__lowerCamelCase   ,		scale=__lowerCamelCase    ) for image in images]

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

     __A       :		str							=	[to_channel_dimension_format(__lowerCamelCase   ,		__lowerCamelCase    ) for image in images]

     __A       :		Union[str, Any]							=	{'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase   ,		tensor_type=__lowerCamelCase    )




