




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 ConditionalDetrImageProcessor
class 			_UpperCamelCase      (				unittest.TestCase		):
      def __init__(			self   :List[str] ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :Dict=7 ,      lowerCamelCase   :Dict=3 ,      lowerCamelCase   :Union[str, Any]=30 ,      lowerCamelCase   :Any=400 ,      lowerCamelCase   :Dict=True ,      lowerCamelCase   :Optional[Any]=None ,      lowerCamelCase   :Dict=True ,      lowerCamelCase   :Tuple=[0.5, 0.5, 0.5] ,      lowerCamelCase   :Any=[0.5, 0.5, 0.5] ,      lowerCamelCase   :str=True ,      lowerCamelCase   :List[str]=1 / 255 ,      lowerCamelCase   :Dict=True ,      )     ->			Dict:
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       UpperCAmelCase__							 =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
       UpperCAmelCase__							 =						parent
       UpperCAmelCase__							 =						batch_size
       UpperCAmelCase__							 =						num_channels
       UpperCAmelCase__							 =						min_resolution
       UpperCAmelCase__							 =						max_resolution
       UpperCAmelCase__							 =						do_resize
       UpperCAmelCase__							 =						size
       UpperCAmelCase__							 =						do_normalize
       UpperCAmelCase__							 =						image_mean
       UpperCAmelCase__							 =						image_std
       UpperCAmelCase__							 =						do_rescale
       UpperCAmelCase__							 =						rescale_factor
       UpperCAmelCase__							 =						do_pad
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Union[str, Any]:
       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_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }







      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[str]=False      )     ->			Union[str, Any]:
       if not batched:
        UpperCAmelCase__							 =						image_inputs[0]
        if isinstance(lowerCamelCase ,      Image.Image      ):
         UpperCAmelCase__ , UpperCAmelCase__							 =						image.size
        else:
         UpperCAmelCase__ , UpperCAmelCase__							 =						image.shape[1], image.shape[2]
        if w < h:
         UpperCAmelCase__							 =						int(self.size["shortest_edge"] * h / w      )
         UpperCAmelCase__							 =						self.size["shortest_edge"]
        elif w > h:
         UpperCAmelCase__							 =						self.size["shortest_edge"]
         UpperCAmelCase__							 =						int(self.size["shortest_edge"] * w / h      )
        else:
         UpperCAmelCase__							 =						self.size["shortest_edge"]
         UpperCAmelCase__							 =						self.size["shortest_edge"]

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

       return expected_height, expected_width



@require_torch
@require_vision
class 			_UpperCamelCase      (				lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		ConditionalDetrImageProcessor if is_vision_available() else None
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			str:
       UpperCAmelCase__							 =						ConditionalDetrImageProcessingTester(self      )
      @property
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Any:
       UpperCAmelCase__							 =						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_resize"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "size"      )      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						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      )

       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :str      )     ->			List[Any]:
       pass
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Union[str, Any]:
       # Initialize image_processing
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCAmelCase__							 =						prepare_image_inputs(self.image_processor_tester ,      equal_resolution=lowerCamelCase      )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase ,      Image.Image      )

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

       UpperCAmelCase__ , UpperCAmelCase__							 =						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
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.image_processor_tester.get_expected_values(lowerCamelCase ,      batched=lowerCamelCase      )

       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       # Initialize image_processing
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       UpperCAmelCase__							 =						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
       UpperCAmelCase__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						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
       UpperCAmelCase__							 =						image_processing(lowerCamelCase ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[Any]:
       # Initialize image_processing
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       UpperCAmelCase__							 =						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
       UpperCAmelCase__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						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
       UpperCAmelCase__							 =						image_processing(lowerCamelCase ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       # prepare image and target
       UpperCAmelCase__							 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,      "r"      ) as f:
        UpperCAmelCase__							 =						json.loads(f.read()      )

       UpperCAmelCase__							 =						{"image_id": 3_9769, "annotations": target}

       # encode them
       UpperCAmelCase__							 =						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"      )
       UpperCAmelCase__							 =						image_processing(images=lowerCamelCase ,      annotations=lowerCamelCase ,      return_tensors="pt"      )

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

       UpperCAmelCase__							 =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,      lowerCamelCase ,      atol=1e-4      )      )

       # verify area
       UpperCAmelCase__							 =						torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,      lowerCamelCase      )      )
       # verify boxes
       UpperCAmelCase__							 =						torch.Size([6, 4]      )
       self.assertEqual(encoding["labels"][0]["boxes"].shape ,      lowerCamelCase      )
       UpperCAmelCase__							 =						torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,      lowerCamelCase ,      atol=1e-3      )      )
       # verify image_id
       UpperCAmelCase__							 =						torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,      lowerCamelCase      )      )
       # verify is_crowd
       UpperCAmelCase__							 =						torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,      lowerCamelCase      )      )
       # verify class_labels
       UpperCAmelCase__							 =						torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,      lowerCamelCase      )      )
       # verify orig_size
       UpperCAmelCase__							 =						torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,      lowerCamelCase      )      )
       # verify size
       UpperCAmelCase__							 =						torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,      lowerCamelCase      )      )







      @slow
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			int:
       # prepare image, target and masks_path
       UpperCAmelCase__							 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,      "r"      ) as f:
        UpperCAmelCase__							 =						json.loads(f.read()      )

       UpperCAmelCase__							 =						{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

       # encode them
       UpperCAmelCase__							 =						ConditionalDetrImageProcessor(format="coco_panoptic"      )
       UpperCAmelCase__							 =						image_processing(images=lowerCamelCase ,      annotations=lowerCamelCase ,      masks_path=lowerCamelCase ,      return_tensors="pt"      )

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

       UpperCAmelCase__							 =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,      lowerCamelCase ,      atol=1e-4      )      )

       # verify area
       UpperCAmelCase__							 =						torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,      lowerCamelCase      )      )
       # verify boxes
       UpperCAmelCase__							 =						torch.Size([6, 4]      )
       self.assertEqual(encoding["labels"][0]["boxes"].shape ,      lowerCamelCase      )
       UpperCAmelCase__							 =						torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,      lowerCamelCase ,      atol=1e-3      )      )
       # verify image_id
       UpperCAmelCase__							 =						torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,      lowerCamelCase      )      )
       # verify is_crowd
       UpperCAmelCase__							 =						torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,      lowerCamelCase      )      )
       # verify class_labels
       UpperCAmelCase__							 =						torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,      lowerCamelCase      )      )
       # verify masks
       UpperCAmelCase__							 =						82_2873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,      lowerCamelCase      )
       # verify orig_size
       UpperCAmelCase__							 =						torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,      lowerCamelCase      )      )
       # verify size
       UpperCAmelCase__							 =						torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,      lowerCamelCase      )      )











import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_lowerCAmelCase						:   int		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Optional[Any] ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :Tuple      )     ->			None:
       warnings.warn(
           "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use YolosImageProcessor instead." ,      lowerCamelCase ,      )
       super().__init__(*lowerCamelCase ,      **lowerCamelCase      )





