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_			:






   def __init__( self							:	Dict      ,		lowercase_							:	int      ,		lowercase_							:	Tuple=sys.maxsize    )   ->		Optional[Any]:
       lowercase__					:  List[Any]          =							"bilinear"
       lowercase__					:  int          =							max_size
       lowercase__					:  Union[str, Any]          =							short_edge_length







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

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

       return img_augs



class     snake_case_			:






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






   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	str    )   ->		Optional[Any]:
       lowercase__					:  Tuple          =							tuple(max(lowercase_    ) for s in zip(*[img.shape for img in images]    )    )
       lowercase__					:  Tuple          =							[im.shape[-2:] for im in images]
       lowercase__					:  Dict          =							[
           nn.functional.pad(
               lowercase_      ,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,		value=self.pad_value      ,		)
           for size, im in zip(lowercase_      ,		lowercase_    )
       ]

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







   def __call__( self							:	Any      ,		lowercase_							:	int      ,		lowercase_							:	str=False    )   ->		Optional[Any]:
       with torch.no_grad():
           if not isinstance(lowercase_      ,		lowercase_    ):
               lowercase__					:  List[str]          =							[images]
           if single_image:
               assert len(lowercase_    ) == 1
           for i in range(len(lowercase_    )    ):
               if isinstance(images[i]      ,		torch.Tensor    ):
                   images.insert(lowercase_      ,		images.pop(lowercase_    ).to(self.device    ).float()    )
               elif not isinstance(images[i]      ,		torch.Tensor    ):
                   images.insert(
                       lowercase_      ,		torch.as_tensor(img_tensorize(images.pop(lowercase_    )      ,		input_format=self.input_format    )    )
                       .to(self.device    )
                       .float()      ,		)
            # resize smallest edge
           lowercase__					:  Optional[int]          =							torch.tensor([im.shape[:2] for im in images]    )
           lowercase__					:  Dict          =							self.aug(lowercase_    )
           # 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
           lowercase__					:  Optional[Any]          =							[self.normalizer(lowercase_    ) for x in images]
           # now pad them to do the following operations
           lowercase__   ,  lowercase__					:  Tuple          =							self.pad(lowercase_    )
           # Normalize

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



def      lowercase_							(     _lowerCamelCase				:			Dict						,      _lowerCamelCase				:			Tuple):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			Tuple[int, int]):
    assert torch.isfinite(_lowerCamelCase).all(), "Box tensor contains infinite or NaN!"
    lowercase__   ,  lowercase__					:  List[str]          =							box_size
    tensor[:, 0].clamp_(min=0						,      max=_lowerCamelCase)
    tensor[:, 1].clamp_(min=0						,      max=_lowerCamelCase)
    tensor[:, 2].clamp_(min=0						,      max=_lowerCamelCase)
    tensor[:, 3].clamp_(min=0						,      max=_lowerCamelCase)




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
      from PIL import Image



class     snake_case_			(						unittest.TestCase    ,__A			):






   def  __UpperCamelCase	( self							:	int    )   ->		Union[str, Any]:
       lowercase__					:  Any          =							load_tool("image-segmentation"    )
       self.tool.setup()
       lowercase__					:  Optional[int]          =							load_tool("image-segmentation"      ,		remote=lowercase_    )






   def  __UpperCamelCase	( self							:	Any    )   ->		Tuple:
       lowercase__					:  Any          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Tuple          =							self.tool(lowercase_      ,		"cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )






   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		Dict:
       lowercase__					:  List[Any]          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Optional[int]          =							self.remote_tool(lowercase_      ,		"cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Dict:
       lowercase__					:  Optional[Any]          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  int          =							self.tool(image=lowercase_      ,		label="cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )







   def  __UpperCamelCase	( self							:	Dict    )   ->		List[Any]:
       lowercase__					:  Tuple          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Union[str, Any]          =							self.remote_tool(image=lowercase_      ,		label="cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )



