





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 YolosImageProcessor

class      A       (	unittest.TestCase						):



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

         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 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    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_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Tuple=False       )						->    Tuple:
         '''simple docstring'''

         if not batched:
            UpperCAmelCase_											=image_inputs[0]
            if isinstance(_lowerCAmelCase		,  Image.Image       ):
               UpperCAmelCase_											=image.size
            else:
               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_											=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      A       (	__A		,      unittest.TestCase						):
      _snake_case							     =YolosImageProcessor if is_vision_available() else None



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=YolosImageProcessingTester(self       )



      @property
      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[Any]:
         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Tuple:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						Tuple       )						->    List[Any]:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						List[Any]       )						->    List[str]:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         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_											=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_											=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 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    str:
         '''simple docstring'''

         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_											=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_											=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 	lowerCAmelCase__    (	self:						Any       )						->    int:
         '''simple docstring'''

         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_											=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_											=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 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         UpperCAmelCase_											=self.image_processing_class(do_resize=_lowerCAmelCase		,  do_normalize=_lowerCAmelCase		,  do_rescale=_lowerCAmelCase       )
         # 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 whether the method "pad" and calling the image processor return the same tensors
         UpperCAmelCase_											=image_processing_a.pad(_lowerCAmelCase		,  return_tensors="pt"       )
         UpperCAmelCase_											=image_processing_a(_lowerCAmelCase		,  return_tensors="pt"       )

         self.assertTrue(
             torch.allclose(encoded_images_with_method["pixel_values"]		,  encoded_images["pixel_values"]		,  atol=1e-4       )       )



      @slow
      def 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[Any]:
         '''simple docstring'''

         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_											=YolosImageProcessor.from_pretrained("hustvl/yolos-small"       )
         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([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         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 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         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_											=YolosImageProcessor(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_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         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       )       )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




