"""simple docstring"""
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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BlipImageProcessor




class   __snake_case			(	unittest.TestCase							):



      def __init__(		self       ,     lowercase       ,     lowercase=7       ,     lowercase=3       ,     lowercase=18       ,     lowercase=30       ,     lowercase=4_00       ,     lowercase=True       ,     lowercase=None       ,     lowercase=True       ,     lowercase=False       ,     lowercase=[0.48145466, 0.4578275, 0.40821073]       ,     lowercase=[0.26862954, 0.26130258, 0.27577711]       ,     lowercase=True       ,     )      ->     Optional[int]:





        '''simple docstring'''





        a__:	Dict     				=      size if size is not None else {'height': 20, 'width': 20}
        a__:	Dict     				=      parent
        a__:	Union[str, Any]     				=      batch_size
        a__:	Dict     				=      num_channels
        a__:	List[Any]     				=      image_size
        a__:	Optional[int]     				=      min_resolution
        a__:	List[str]     				=      max_resolution
        a__:	Optional[Any]     				=      do_resize
        a__:	Optional[Any]     				=      size
        a__:	Any     				=      do_normalize
        a__:	Union[str, Any]     				=      image_mean
        a__:	Dict     				=      image_std
        a__:	int     				=      do_pad
        a__:	int     				=      do_convert_rgb



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }



      def     lowerCamelCase_  (		self       ,     lowercase=False       ,     lowercase=False       ,     lowercase=False)      ->     str:





        '''simple docstring'''





        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          a__:	List[str]     				=      []
          for i in range(self.batch_size):
            image_inputs.append(
                np.random.randint(
                    2_55       ,     size=(self.num_channels, self.max_resolution, self.max_resolution)       ,     dtype=np.uinta))
        else:
          a__:	List[str]     				=      []
          for i in range(self.batch_size):
            a__				,       a__:	Tuple     				=      np.random.choice(np.arange(self.min_resolution       ,     self.max_resolution)       ,     2)
            image_inputs.append(np.random.randint(2_55       ,     size=(self.num_channels, width, height)       ,     dtype=np.uinta))

        if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          a__:	Union[str, Any]     				=      [Image.fromarray(np.moveaxis(lowercase       ,     0       ,     -1)) for x in image_inputs]

        if torchify:
          a__:	int     				=      [torch.from_numpy(lowercase) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						BlipImageProcessor if is_vision_available() else None



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	int     				=      BlipImageProcessingTester(self)



      @property
      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase       ,     'do_resize'))
        self.assertTrue(hasattr(lowercase       ,     'size'))
        self.assertTrue(hasattr(lowercase       ,     'do_normalize'))
        self.assertTrue(hasattr(lowercase       ,     'image_mean'))
        self.assertTrue(hasattr(lowercase       ,     'image_std'))
        self.assertTrue(hasattr(lowercase       ,     'do_convert_rgb'))



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        pass



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Dict     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:	Any     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     Image.Image)

        # Test not batched input
        a__:	Tuple     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Tuple     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	int     				=      self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        a__:	Optional[Any]     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase       ,     numpify=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     np.ndarray)

        # Test not batched input
        a__:	Dict     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	int     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	int     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        a__:	List[str]     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase       ,     torchify=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     torch.Tensor)

        # Test not batched input
        a__:	Optional[Any]     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Tuple     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )







@require_torch
@require_vision
class   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						BlipImageProcessor if is_vision_available() else None



      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      BlipImageProcessingTester(self       ,     num_channels=4)
        a__:	Union[str, Any]     				=      3



      @property
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	Dict     				=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase       ,     'do_resize'))
        self.assertTrue(hasattr(lowercase       ,     'size'))
        self.assertTrue(hasattr(lowercase       ,     'do_normalize'))
        self.assertTrue(hasattr(lowercase       ,     'image_mean'))
        self.assertTrue(hasattr(lowercase       ,     'image_std'))
        self.assertTrue(hasattr(lowercase       ,     'do_convert_rgb'))



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





        '''simple docstring'''





        pass



      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:	Dict     				=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase       ,     Image.Image)

        # Test not batched input
        a__:	Union[str, Any]     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Dict     				=      image_processor(lowercase       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )










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							, lowercase__							, lowercase__=sys.maxsize						)				->				str:
				__A    =							"bilinear"
				__A    =							max_size
				__A    =							short_edge_length






		def __call__(      self							, lowercase__						)				->				Dict:
				__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(lowercase__							, lowercase__						)
						if h < w:
								__A ,   __A    =							size, scale * w
						else:
								__A ,   __A    =							scale * h, size
						if max(lowercase__							, lowercase__						) > self.max_size:
								__A    =							self.max_size * 1.0 / max(lowercase__							, lowercase__						)
								__A    =							newh * scale
								__A    =							neww * scale
						__A    =							int(neww + 0.5						)
						__A    =							int(newh + 0.5						)

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

				return img_augs





class        a__      :






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				__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 lowercase__						: (x - self.pixel_mean) / self.pixel_std






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[int]:
				__A    =							tuple(max(lowercase__						) for s in zip(*[img.shape for img in images]						)						)
				__A    =							[im.shape[-2:] for im in images]
				__A    =							[
				    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							, lowercase__							, lowercase__=False						)				->				int:
				with torch.no_grad():
						if not isinstance(lowercase__							, lowercase__						):
								__A    =							[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
						__A    =							torch.tensor([im.shape[:2] for im in images]						)
						__A    =							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
						__A    =							[self.normalizer(lowercase__						) for x in images]
						# now pad them to do the following operations
						__A ,   __A    =							self.pad(lowercase__						)
						# Normalize

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''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__	)

