"""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 YolosImageProcessor


class 							a					(		unittest.TestCase ):


    def __init__(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Optional[int]=7 ,					__lowerCAmelCase						:	Dict=3 ,					__lowerCAmelCase						:	Optional[int]=30 ,					__lowerCAmelCase						:	Optional[Any]=400 ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	Optional[Any]=None ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Any=[0.5, 0.5, 0.5] ,					__lowerCAmelCase						:	Optional[int]=[0.5, 0.5, 0.5] ,					__lowerCAmelCase						:	Union[str, Any]=True ,					__lowerCAmelCase						:	Optional[Any]=1 / 255 ,					__lowerCAmelCase						:	Union[str, Any]=True ,					):
      # 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   lowerCAmelCase_	(       self						:	Tuple       ):
      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						:	Tuple ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Union[str, Any]=False       ):
      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 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Union[str, Any]  				=				YolosImageProcessor if is_vision_available() else None


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   YolosImageProcessingTester(self       )


    @property
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def   lowerCAmelCase_	(       self						:	Union[str, 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   lowerCAmelCase_	(       self						:	List[str]       ):
      _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						:	Optional[Any]       ):
      pass


    def   lowerCAmelCase_	(       self						:	Optional[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   lowerCAmelCase_	(       self						:	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   lowerCAmelCase_	(       self						:	Union[str, 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,
          ) ,					)


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      # Initialize image_processings
      _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						:	Union[str, Any]       ):
      # 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    			=   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([0.2_796, 0.3_138, 0.3_481]       )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )

      # verify area
      _UpperCAmelCase    			=   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"""] ,					__lowerCAmelCase       )       )
      # verify boxes
      _UpperCAmelCase    			=   torch.Size([6, 4]       )
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]       )
      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						:	Union[str, Any]       ):
      # 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    			=   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([0.2_796, 0.3_138, 0.3_481]       )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,					__lowerCAmelCase ,					atol=1e-4       )       )

      # verify area
      _UpperCAmelCase    			=   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"""] ,					__lowerCAmelCase       )       )
      # verify boxes
      _UpperCAmelCase    			=   torch.Size([6, 4]       )
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]       )
      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       )       )

"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__			=    logging.get_logger(__name__)


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[Any]  				=				'timm_backbone'


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	str=None ,					__lowerCAmelCase						:	Optional[int]=3 ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	Optional[Any]=None ,					**__lowerCAmelCase						:	Optional[Any] ,					):
      super().__init__(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   backbone
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   features_only
      _UpperCAmelCase    			=   use_pretrained_backbone
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   out_indices if out_indices is not None else (-1,)
