





'''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     __lowerCAmelCase					:






       """simple docstring"""





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		int=sys.maxsize					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	'''bilinear'''
            _UpperCamelCase       =	max_size
            _UpperCamelCase       =	short_edge_length







       def __call__(							self       :		Tuple	,			lowerCAmelCase__       :		List[str]					)   ->					Optional[Any]:

            '''simple docstring'''




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

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

            return img_augs

class     __lowerCAmelCase					:






       """simple docstring"""





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Optional[Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,			cfg.INPUT.MAX_SIZE_TEST					)
            _UpperCamelCase       =	cfg.INPUT.FORMAT
            _UpperCamelCase       =	cfg.SIZE_DIVISIBILITY
            _UpperCamelCase       =	cfg.PAD_VALUE
            _UpperCamelCase       =	cfg.INPUT.MAX_SIZE_TEST
            _UpperCamelCase       =	cfg.MODEL.DEVICE
            _UpperCamelCase       =	torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)	,			1	,			1					)
            _UpperCamelCase       =	torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)	,			1	,			1					)
            _UpperCamelCase       =	lambda lowerCAmelCase__					: (x - self.pixel_mean) / self.pixel_std





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		str					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	tuple(max(lowerCAmelCase__					) for s in zip(*[img.shape for img in images]					)					)
            _UpperCamelCase       =	[im.shape[-2:] for im in images]
            _UpperCamelCase       =	[
                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       :		int	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Union[str, Any]=False					)   ->					List[str]:

            '''simple docstring'''




            with torch.no_grad():
                 if not isinstance(lowerCAmelCase__	,			lowerCAmelCase__					):
                      _UpperCamelCase       =	[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
                 _UpperCamelCase       =	torch.tensor([im.shape[:2] for im in images]					)
                 _UpperCamelCase       =	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
                 _UpperCamelCase       =	[self.normalizer(lowerCAmelCase__					) for x in images]
                 # now pad them to do the following operations
                 _UpperCamelCase   ,    _UpperCamelCase       =	self.pad(lowerCAmelCase__					)
                 # Normalize

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







def  a__    (    lowercase :					Any, lowercase :					Union[str, Any]				)		->		Any:





     """simple docstring"""

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







def  a__    (    lowercase :					List[str], lowercase :					Tuple[int, int]				)		->		str:





     """simple docstring"""

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










'''simple docstring'''

import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	['''a''', '''b''', '''c''']

            # Defaults to last layer if both are None
            _UpperCamelCase   ,    _UpperCamelCase       =	get_aligned_output_features_output_indices(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
            self.assertEqual(lowerCAmelCase__	,			['''c''']					)
            self.assertEqual(lowerCAmelCase__	,			[2]					)

            # Out indices set to match out features
            _UpperCamelCase   ,    _UpperCamelCase       =	get_aligned_output_features_output_indices(['''a''', '''c''']	,			lowerCAmelCase__	,			lowerCAmelCase__					)
            self.assertEqual(lowerCAmelCase__	,			['''a''', '''c''']					)
            self.assertEqual(lowerCAmelCase__	,			[0, 2]					)

            # Out features set to match out indices
            _UpperCamelCase   ,    _UpperCamelCase       =	get_aligned_output_features_output_indices(lowerCAmelCase__	,			[0, 2]	,			lowerCAmelCase__					)
            self.assertEqual(lowerCAmelCase__	,			['''a''', '''c''']					)
            self.assertEqual(lowerCAmelCase__	,			[0, 2]					)

            # Out features selected from negative indices
            _UpperCamelCase   ,    _UpperCamelCase       =	get_aligned_output_features_output_indices(lowerCAmelCase__	,			[-3, -1]	,			lowerCAmelCase__					)
            self.assertEqual(lowerCAmelCase__	,			['''a''', '''c''']					)
            self.assertEqual(lowerCAmelCase__	,			[-3, -1]					)





       def snake_case__						(							self       :		Any					)   ->					Union[str, Any]:

            '''simple docstring'''




            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(['''a''', '''b''']	,			(0, 1)	,			lowerCAmelCase__					)

            # Out features must be a list
            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(('''a''', '''b''')	,			(0, 1)	,			['''a''', '''b''']					)

            # Out features must be a subset of stage names
            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(['''a''', '''b''']	,			(0, 1)	,			['''a''']					)

            # Out indices must be a list or tuple
            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(lowerCAmelCase__	,			0	,			['''a''', '''b''']					)

            # Out indices must be a subset of stage names
            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(lowerCAmelCase__	,			(0, 1)	,			['''a''']					)

            # Out features and out indices must be the same length
            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(['''a''', '''b''']	,			(0,)	,			['''a''', '''b''', '''c''']					)

            # Out features should match out indices
            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(['''a''', '''b''']	,			(0, 2)	,			['''a''', '''b''', '''c''']					)

            # Out features and out indices should be in order
            with self.assertRaises(lowerCAmelCase__					):
                 verify_out_features_out_indices(['''b''', '''a''']	,			(0, 1)	,			['''a''', '''b''']					)

            # Check passes with valid inputs
            verify_out_features_out_indices(['''a''', '''b''', '''d''']	,			(0, 1, -1)	,			['''a''', '''b''', '''c''', '''d''']					)







       def snake_case__						(							self       :		Any					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	BackboneMixin()

            _UpperCamelCase       =	['''a''', '''b''', '''c''']
            _UpperCamelCase       =	['''a''', '''c''']
            _UpperCamelCase       =	[0, 2]

            # Check that the output features and indices are set correctly
            self.assertEqual(backbone.out_features	,			['''a''', '''c''']					)
            self.assertEqual(backbone.out_indices	,			[0, 2]					)

            # Check out features and indices are updated correctly
            _UpperCamelCase       =	['''a''', '''b''']
            self.assertEqual(backbone.out_features	,			['''a''', '''b''']					)
            self.assertEqual(backbone.out_indices	,			[0, 1]					)

            _UpperCamelCase       =	[-3, -1]
            self.assertEqual(backbone.out_features	,			['''a''', '''c''']					)
            self.assertEqual(backbone.out_indices	,			[-3, -1]					)



