

"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor







class      __lowercase			(    unittest.TestCase					):

  def __init__(  self		:			List[str]					,A		:			Any					,A		:			Union[str, Any]=7					,A		:			List[Any]=3					,A		:			Any=30					,A		:			Tuple=400					,A		:			str=True					,A		:			List[str]=None					,A		:			Any=True					,A		:			List[str]=1 / 255					,A		:			Union[str, Any]=True					,A		:			str=[0.5, 0.5, 0.5]					,A		:			int=[0.5, 0.5, 0.5]					,A		:			Optional[Any]=True					,):






   '''simple docstring'''
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   UpperCAmelCase__					:  List[Any]					   =					size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
   UpperCAmelCase__					:  Optional[int]					   =					parent
   UpperCAmelCase__					:  Optional[int]					   =					batch_size
   UpperCAmelCase__					:  List[Any]					   =					num_channels
   UpperCAmelCase__					:  List[str]					   =					min_resolution
   UpperCAmelCase__					:  Tuple					   =					max_resolution
   UpperCAmelCase__					:  Dict					   =					do_resize
   UpperCAmelCase__					:  str					   =					size
   UpperCAmelCase__					:  Optional[Any]					   =					do_rescale
   UpperCAmelCase__					:  Any					   =					rescale_factor
   UpperCAmelCase__					:  Optional[int]					   =					do_normalize
   UpperCAmelCase__					:  Dict					   =					image_mean
   UpperCAmelCase__					:  List[Any]					   =					image_std
   UpperCAmelCase__					:  str					   =					do_pad

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_pad": self.do_pad,
   }




  def    __lowercase       (  self		:			Tuple					,A		:			Optional[Any]					,A		:			List[Any]=False    ):






   '''simple docstring'''
   if not batched:
    UpperCAmelCase__					:  List[str]					   =					image_inputs[0]
    if isinstance(A					,Image.Image    ):
     UpperCAmelCase__					:  List[Any]					   =					image.size
    else:
     UpperCAmelCase__					:  List[Any]					   =					image.shape[1], image.shape[2]
    if w < h:
     UpperCAmelCase__					:  List[Any]					   =					int(self.size["""shortest_edge"""] * h / w    )
     UpperCAmelCase__					:  Dict					   =					self.size["shortest_edge"]
    elif w > h:
     UpperCAmelCase__					:  List[str]					   =					self.size["shortest_edge"]
     UpperCAmelCase__					:  Tuple					   =					int(self.size["""shortest_edge"""] * w / h    )
    else:
     UpperCAmelCase__					:  List[str]					   =					self.size["shortest_edge"]
     UpperCAmelCase__					:  Tuple					   =					self.size["shortest_edge"]

   else:
    UpperCAmelCase__					:  Optional[int]					   =					[]
    for image in image_inputs:
     UpperCAmelCase__					:  Dict					   =					self.get_expected_values([image]    )
     expected_values.append((expected_height, expected_width)    )
    UpperCAmelCase__					:  Optional[int]					   =					max(A					,key=lambda A    : item[0]    )[0]
    UpperCAmelCase__					:  Optional[int]					   =					max(A					,key=lambda A    : item[1]    )[1]

   return expected_height, expected_width







@require_torch
@require_vision
class      __lowercase			(    _UpperCamelCase	,			unittest.TestCase					):
  snake_case_				       =       DetrImageProcessor if is_vision_available() else None

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					DetrImageProcessingTester(self    )

  @property
  def    __lowercase       (  self		:			Dict    ):






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

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.image_processing_class(**self.image_processor_dict    )
   self.assertTrue(hasattr(A					,"""image_mean"""    )    )
   self.assertTrue(hasattr(A					,"""image_std"""    )    )
   self.assertTrue(hasattr(A					,"""do_normalize"""    )    )
   self.assertTrue(hasattr(A					,"""do_rescale"""    )    )
   self.assertTrue(hasattr(A					,"""rescale_factor"""    )    )
   self.assertTrue(hasattr(A					,"""do_resize"""    )    )
   self.assertTrue(hasattr(A					,"""size"""    )    )
   self.assertTrue(hasattr(A					,"""do_pad"""    )    )

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.image_processing_class.from_dict(self.image_processor_dict    )
   self.assertEqual(image_processor.size					,{"""shortest_edge""": 18, """longest_edge""": 1_333}    )
   self.assertEqual(image_processor.do_pad					,A    )

   UpperCAmelCase__					:  List[str]					   =					self.image_processing_class.from_dict(
       self.image_processor_dict					,size=42					,max_size=84					,pad_and_return_pixel_mask=A    )
   self.assertEqual(image_processor.size					,{"""shortest_edge""": 42, """longest_edge""": 84}    )
   self.assertEqual(image_processor.do_pad					,A    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__					:  str					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   UpperCAmelCase__					:  Optional[Any]					   =					prepare_image_inputs(self.image_processor_tester					,equal_resolution=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,Image.Image    )

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

   UpperCAmelCase__					:  Tuple					   =					self.image_processor_tester.get_expected_values(A    )

   self.assertEqual(
       encoded_images.shape					,(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,)

   # Test batched
   UpperCAmelCase__					:  List[Any]					   =					self.image_processor_tester.get_expected_values(A					,batched=A    )

   UpperCAmelCase__					:  Tuple					   =					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,
           expected_height,
           expected_width,
       )					,)

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__					:  Tuple					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   UpperCAmelCase__					:  List[str]					   =					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
   UpperCAmelCase__					:  Any					   =					image_processing(image_inputs[0]					,return_tensors="""pt"""    ).pixel_values

   UpperCAmelCase__					:  Tuple					   =					self.image_processor_tester.get_expected_values(A    )

   self.assertEqual(
       encoded_images.shape					,(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,)

   # Test batched
   UpperCAmelCase__					:  Tuple					   =					image_processing(A					,return_tensors="""pt"""    ).pixel_values

   UpperCAmelCase__					:  List[Any]					   =					self.image_processor_tester.get_expected_values(A					,batched=A    )

   self.assertEqual(
       encoded_images.shape					,(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )					,)

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__					:  Dict					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   UpperCAmelCase__					:  Tuple					   =					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
   UpperCAmelCase__					:  Any					   =					image_processing(image_inputs[0]					,return_tensors="""pt"""    ).pixel_values

   UpperCAmelCase__					:  Optional[int]					   =					self.image_processor_tester.get_expected_values(A    )

   self.assertEqual(
       encoded_images.shape					,(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,)

   # Test batched
   UpperCAmelCase__					:  List[str]					   =					image_processing(A					,return_tensors="""pt"""    ).pixel_values

   UpperCAmelCase__					:  Tuple					   =					self.image_processor_tester.get_expected_values(A					,batched=A    )

   self.assertEqual(
       encoded_images.shape					,(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )					,)

  @slow
  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   # prepare image and target
   UpperCAmelCase__					:  Optional[int]					   =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
   with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""					,"""r"""    ) as f:
    UpperCAmelCase__					:  List[Any]					   =					json.loads(f.read()    )

   UpperCAmelCase__					:  Optional[int]					   =					{"image_id": 39_769, "annotations": target}

   # encode them
   UpperCAmelCase__					:  Any					   =					DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""    )
   UpperCAmelCase__					:  List[str]					   =					image_processing(images=A					,annotations=A					,return_tensors="""pt"""    )

   # verify pixel values
   UpperCAmelCase__					:  List[Any]					   =					torch.Size([1, 3, 800, 1_066]    )
   self.assertEqual(encoding["""pixel_values"""].shape					,A    )

   UpperCAmelCase__					:  Dict					   =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
   self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]					,A					,atol=1e-4    )    )

   # verify area
   UpperCAmelCase__					:  List[str]					   =					torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,A    )    )
   # verify boxes
   UpperCAmelCase__					:  Tuple					   =					torch.Size([6, 4]    )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,A    )
   UpperCAmelCase__					:  Any					   =					torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,A					,atol=1e-3    )    )
   # verify image_id
   UpperCAmelCase__					:  Any					   =					torch.tensor([39_769]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,A    )    )
   # verify is_crowd
   UpperCAmelCase__					:  Tuple					   =					torch.tensor([0, 0, 0, 0, 0, 0]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,A    )    )
   # verify class_labels
   UpperCAmelCase__					:  Optional[Any]					   =					torch.tensor([75, 75, 63, 65, 17, 17]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,A    )    )
   # verify orig_size
   UpperCAmelCase__					:  List[Any]					   =					torch.tensor([480, 640]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,A    )    )
   # verify size
   UpperCAmelCase__					:  Any					   =					torch.tensor([800, 1_066]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,A    )    )




  @slow
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   # prepare image, target and masks_path
   UpperCAmelCase__					:  List[Any]					   =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
   with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""					,"""r"""    ) as f:
    UpperCAmelCase__					:  Optional[int]					   =					json.loads(f.read()    )

   UpperCAmelCase__					:  Any					   =					{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

   UpperCAmelCase__					:  Tuple					   =					pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""    )

   # encode them
   UpperCAmelCase__					:  str					   =					DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""    )
   UpperCAmelCase__					:  Optional[int]					   =					image_processing(images=A					,annotations=A					,masks_path=A					,return_tensors="""pt"""    )

   # verify pixel values
   UpperCAmelCase__					:  List[str]					   =					torch.Size([1, 3, 800, 1_066]    )
   self.assertEqual(encoding["""pixel_values"""].shape					,A    )

   UpperCAmelCase__					:  Optional[Any]					   =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
   self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]					,A					,atol=1e-4    )    )

   # verify area
   UpperCAmelCase__					:  str					   =					torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,A    )    )
   # verify boxes
   UpperCAmelCase__					:  int					   =					torch.Size([6, 4]    )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,A    )
   UpperCAmelCase__					:  Optional[int]					   =					torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,A					,atol=1e-3    )    )
   # verify image_id
   UpperCAmelCase__					:  List[Any]					   =					torch.tensor([39_769]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,A    )    )
   # verify is_crowd
   UpperCAmelCase__					:  List[Any]					   =					torch.tensor([0, 0, 0, 0, 0, 0]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,A    )    )
   # verify class_labels
   UpperCAmelCase__					:  Dict					   =					torch.tensor([17, 17, 63, 75, 75, 93]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,A    )    )
   # verify masks
   UpperCAmelCase__					:  List[str]					   =					822_873
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,A    )
   # verify orig_size
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([480, 640]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,A    )    )
   # verify size
   UpperCAmelCase__					:  Tuple					   =					torch.tensor([800, 1_066]    )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,A    )    )








"""simple docstring"""




from __future__ import annotations
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					list(range(len(__UpperCamelCase      )      )      )
 UpperCAmelCase__					:  Union[str, Any]					   =					[v / w for v, w in zip(__UpperCamelCase							,    __UpperCamelCase      )]
 index.sort(key=lambda __UpperCamelCase      : ratio[i]							,    reverse=__UpperCamelCase      )

 UpperCAmelCase__					:  float					   =					0
 UpperCAmelCase__					:  list[float]					   =					[0] * len(__UpperCamelCase      )
 for i in index:
  if weight[i] <= capacity:
   UpperCAmelCase__					:  Optional[Any]					   =					1
   max_value += value[i]
   capacity -= weight[i]
  else:
   UpperCAmelCase__					:  Union[str, Any]					   =					capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
      import doctest

      doctest.testmod()





