





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 A_   :




   def __init__(self      :Dict       ,  _UpperCamelCase      :int       ,  _UpperCamelCase      :Any=sys.maxsize					)->   Dict:
         __A			  =							'''bilinear'''
         __A			  =							max_size
         __A			  =							short_edge_length






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

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

         return img_augs





class A_   :




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




   def _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :str					)->   Tuple:
         __A			  =							tuple(max(__SCREAMING_SNAKE_CASE					) for s in zip(*[img.shape for img in images]					)					)
         __A			  =							[im.shape[-2:] for im in images]
         __A			  =							[
             nn.functional.pad(
                 __SCREAMING_SNAKE_CASE       ,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,  value=self.pad_value       ,  )
             for size, im in zip(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE					)
         ]

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






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

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





def _a							(      lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Tuple					)      ->		List[str]:







      '''simple docstring'''






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







def _a							(      lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Union[str, Any]					)      ->		str:







      '''simple docstring'''






      assert torch.isfinite(lowerCamelCase_					).all(), "Box tensor contains infinite or NaN!"
      __A  ,		__A			  =							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

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ViltImageProcessor

class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def __init__(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=7	,       __SCREAMING_SNAKE_CASE     :					Dict=3	,       __SCREAMING_SNAKE_CASE     :					Any=18	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=30	,       __SCREAMING_SNAKE_CASE     :					int=400	,       __SCREAMING_SNAKE_CASE     :					int=True	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=None	,       __SCREAMING_SNAKE_CASE     :					Any=2	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					int=[0.5, 0.5, 0.5]	,       __SCREAMING_SNAKE_CASE     :					List[str]=[0.5, 0.5, 0.5]	,       ):






					"""simple docstring"""
					_snake_case														=   size if size is not None else {"shortest_edge": 30}
					_snake_case														=   parent
					_snake_case														=   batch_size
					_snake_case														=   num_channels
					_snake_case														=   image_size
					_snake_case														=   min_resolution
					_snake_case														=   max_resolution
					_snake_case														=   do_resize
					_snake_case														=   size
					_snake_case														=   size_divisor
					_snake_case														=   do_normalize
					_snake_case														=   image_mean
					_snake_case														=   image_std



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}







		def 					_a					(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False ):






					"""simple docstring"""
					if not batched:
								_snake_case														=   self.size["shortest_edge"]
								_snake_case														=   image_inputs[0]
								if isinstance(__SCREAMING_SNAKE_CASE	,       Image.Image ):
											_snake_case							,			_snake_case														=   image.size
								else:
											_snake_case							,			_snake_case														=   image.shape[1], image.shape[2]
								_snake_case														=   size / min(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
								if h < w:
											_snake_case							,			_snake_case														=   size, scale * w
								else:
											_snake_case							,			_snake_case														=   scale * h, size

								_snake_case														=   int((1_333 / 800) * size )
								if max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) > max_size:
											_snake_case														=   max_size / max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   newh * scale
											_snake_case														=   neww * scale

								_snake_case							,			_snake_case														=   int(newh + 0.5 ), int(neww + 0.5 )
								_snake_case							,			_snake_case														=   (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								_snake_case														=   []
								for image in image_inputs:
											_snake_case							,			_snake_case														=   self.get_expected_values([image] )
											expected_values.append((expected_height, expected_width) )
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[0] )[0]
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[1] )[1]

					return expected_height, expected_width

@require_torch
@require_vision
class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   ViltImageProcessor if is_vision_available() else None



		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   ViltImageProcessingTester(self )



		@property
		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_mean" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_std" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_normalize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_resize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size_divisor" ) )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 30} )

					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict	,       size=42 )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 42} )



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					pass



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       Image.Image )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       numpify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       np.ndarray )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )







		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       torchify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       torch.Tensor )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )

