
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	:





     def __init__(self   :					List[str]       ,					_A   :					Optional[Any]       ,					_A   :					List[str]=sys.maxsize     )		->		Optional[Any]:
         snake_case				  =      "bilinear"
         snake_case				  =      max_size
         snake_case				  =      short_edge_length





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

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

         return img_augs







class   lowerCamelCase	:





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





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					Optional[int]     )		->		List[str]:
         snake_case				  =      tuple(max(_A     ) for s in zip(*[img.shape for img in images]     )     )
         snake_case				  =      [im.shape[-2:] for im in images]
         snake_case				  =      [
             nn.functional.pad(
                 _A       ,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,					value=self.pad_value       ,					)
             for size, im in zip(_A       ,					_A     )
         ]

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





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

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



def 		lowercase_    (     A__				,	A__	)							->							int:
    """simple docstring"""

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





def 		lowercase_    (     A__				,	A__	)							->							Optional[Any]:
    """simple docstring"""

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



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 MobileNetVaImageProcessor







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Optional[int]       ,					_A   :					Dict       ,					_A   :					Optional[int]=7       ,					_A   :					Union[str, Any]=3       ,					_A   :					Tuple=1_8       ,					_A   :					str=3_0       ,					_A   :					Union[str, Any]=4_0_0       ,					_A   :					Optional[Any]=True       ,					_A   :					Union[str, Any]=None       ,					_A   :					str=True       ,					_A   :					Optional[int]=None       ,					)		->		int:
         snake_case				  =      size if size is not None else {"shortest_edge": 2_0}
         snake_case				  =      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         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				  =      do_center_crop
         snake_case				  =      crop_size





     def 	UpperCAmelCase(self   :					str     )		->		int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[int]     =	MobileNetVaImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         snake_case				  =      MobileNetVaImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )
         self.assertTrue(hasattr(_A       ,					"do_center_crop"     )     )
         self.assertTrue(hasattr(_A       ,					"crop_size"     )     )





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 2_0}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 1_8, "width": 1_8}     )

         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 4_2}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 8_4, "width": 8_4}     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         pass





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         # 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=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         # 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=_A       ,					numpify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					np.ndarray     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					List[str]     )		->		List[str]:
         # 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=_A       ,					torchify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					torch.Tensor     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

