






'''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      ):


    '''simple docstring'''

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

          '''simple docstring'''




          lowerCamelCase__  =     size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
          lowerCamelCase__  =     parent
          lowerCamelCase__  =     batch_size
          lowerCamelCase__  =     num_channels
          lowerCamelCase__  =     min_resolution
          lowerCamelCase__  =     max_resolution
          lowerCamelCase__  =     do_resize
          lowerCamelCase__  =     size
          lowerCamelCase__  =     do_rescale
          lowerCamelCase__  =     rescale_factor
          lowerCamelCase__  =     do_normalize
          lowerCamelCase__  =     image_mean
          lowerCamelCase__  =     image_std
          lowerCamelCase__  =     do_pad

    def    a__				(					self     :     str     )							->							str:

          '''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    a__				(					self     :     Dict				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     Dict=False     )							->							Tuple:

          '''simple docstring'''




          if not batched:
                lowerCamelCase__  =     image_inputs[0]
                if isinstance(__lowerCamelCase				,     Image.Image     ):
                      lowerCamelCase__  ,	lowerCamelCase__  =     image.size
                else:
                      lowerCamelCase__  ,	lowerCamelCase__  =     image.shape[1], image.shape[2]
                if w < h:
                      lowerCamelCase__  =     int(self.size["shortest_edge"] * h / w     )
                      lowerCamelCase__  =     self.size["shortest_edge"]
                elif w > h:
                      lowerCamelCase__  =     self.size["shortest_edge"]
                      lowerCamelCase__  =     int(self.size["shortest_edge"] * w / h     )
                else:
                      lowerCamelCase__  =     self.size["shortest_edge"]
                      lowerCamelCase__  =     self.size["shortest_edge"]

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

          return expected_height, expected_width
@require_torch
@require_vision
class 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							DetrImageProcessor if is_vision_available() else None

    def    a__				(					self     :     Optional[int]     )							->							Union[str, Any]:

          '''simple docstring'''




          lowerCamelCase__  =     DetrImageProcessingTester(self     )

    @property
    def    a__				(					self     :     Optional[Any]     )							->							List[str]:

          '''simple docstring'''




          return self.image_processor_tester.prepare_image_processor_dict()

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

          '''simple docstring'''




          lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "image_mean"     )     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "image_std"     )     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "do_normalize"     )     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "do_rescale"     )     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "rescale_factor"     )     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "do_resize"     )     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "size"     )     )
          self.assertTrue(hasattr(__lowerCamelCase				,     "do_pad"     )     )

    def    a__				(					self     :     str     )							->							int:

          '''simple docstring'''




          lowerCamelCase__  =     self.image_processing_class.from_dict(self.image_processor_dict     )
          self.assertEqual(image_processor.size				,     {"shortest_edge": 18, "longest_edge": 1333}     )
          self.assertEqual(image_processor.do_pad				,     __lowerCamelCase     )

          lowerCamelCase__  =     self.image_processing_class.from_dict(
              self.image_processor_dict				,     size=42				,     max_size=84				,     pad_and_return_pixel_mask=__lowerCamelCase     )
          self.assertEqual(image_processor.size				,     {"shortest_edge": 42, "longest_edge": 84}     )
          self.assertEqual(image_processor.do_pad				,     __lowerCamelCase     )

    def    a__				(					self     :     Any     )							->							int:

          '''simple docstring'''




          pass

    def    a__				(					self     :     int     )							->							Any:

          '''simple docstring'''




          lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
          # create random PIL images
          lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase     )
          for image in image_inputs:
                self.assertIsInstance(__lowerCamelCase				,     Image.Image     )

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

          lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

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

          # Test batched
          lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

          lowerCamelCase__  =     image_processing(__lowerCamelCase				,     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    a__				(					self     :     Tuple     )							->							Optional[Any]:

          '''simple docstring'''




          lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
          # create random numpy tensors
          lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase				,     numpify=__lowerCamelCase     )
          for image in image_inputs:
                self.assertIsInstance(__lowerCamelCase				,     np.ndarray     )

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

          lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

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

          # Test batched
          lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

          lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

          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]     )							->							Optional[int]:

          '''simple docstring'''




          lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
          # create random PyTorch tensors
          lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase				,     torchify=__lowerCamelCase     )
          for image in image_inputs:
                self.assertIsInstance(__lowerCamelCase				,     torch.Tensor     )

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

          lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

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

          # Test batched
          lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

          lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

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

    @slow
    def    a__				(					self     :     Union[str, Any]     )							->							Any:

          '''simple docstring'''




          lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,     "r"     ) as f:
                lowerCamelCase__  =     json.loads(f.read()     )

          lowerCamelCase__  =     {"image_id": 39769, "annotations": target}

          # encode them
          lowerCamelCase__  =     DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"     )
          lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     return_tensors="pt"     )

          # verify pixel values
          lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
          self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

          lowerCamelCase__  =     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]				,     __lowerCamelCase				,     atol=1E-4     )     )

          # verify area
          lowerCamelCase__  =     torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     __lowerCamelCase     )     )
          # verify boxes
          lowerCamelCase__  =     torch.Size([6, 4]     )
          self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
          lowerCamelCase__  =     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]				,     __lowerCamelCase				,     atol=1E-3     )     )
          # verify image_id
          lowerCamelCase__  =     torch.tensor([39769]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
          # verify is_crowd
          lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
          # verify class_labels
          lowerCamelCase__  =     torch.tensor([75, 75, 63, 65, 17, 17]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
          # verify orig_size
          lowerCamelCase__  =     torch.tensor([480, 640]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
          # verify size
          lowerCamelCase__  =     torch.tensor([800, 1066]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )




    @slow
    def    a__				(					self     :     str     )							->							List[str]:

          '''simple docstring'''




          lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
          with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,     "r"     ) as f:
                lowerCamelCase__  =     json.loads(f.read()     )

          lowerCamelCase__  =     {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

          lowerCamelCase__  =     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

          # encode them
          lowerCamelCase__  =     DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"     )
          lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     masks_path=__lowerCamelCase				,     return_tensors="pt"     )

          # verify pixel values
          lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
          self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

          lowerCamelCase__  =     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]				,     __lowerCamelCase				,     atol=1E-4     )     )

          # verify area
          lowerCamelCase__  =     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     __lowerCamelCase     )     )
          # verify boxes
          lowerCamelCase__  =     torch.Size([6, 4]     )
          self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
          lowerCamelCase__  =     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]				,     __lowerCamelCase				,     atol=1E-3     )     )
          # verify image_id
          lowerCamelCase__  =     torch.tensor([39769]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
          # verify is_crowd
          lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
          # verify class_labels
          lowerCamelCase__  =     torch.tensor([17, 17, 63, 75, 75, 93]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
          # verify masks
          lowerCamelCase__  =     822873
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,     __lowerCamelCase     )
          # verify orig_size
          lowerCamelCase__  =     torch.tensor([480, 640]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
          # verify size
          lowerCamelCase__  =     torch.tensor([800, 1066]     )
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )









'''simple docstring'''





from ..utils import DummyObject, requires_backends
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     List[str]				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     str				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     Any     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     str				,     *__lowerCamelCase     :     List[Any]				,     **__lowerCamelCase     :     int     )							->							List[str]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Dict				,     *__lowerCamelCase     :     Any				,     **__lowerCamelCase     :     str     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Dict     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     Dict				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     List[Any]     )							->							str:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Optional[Any]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     List[str]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     int				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Tuple				,     **__lowerCamelCase     :     str     )							->							List[str]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     List[Any]				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     List[Any]     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )

