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 __snake_case				(						unittest.TestCase		):




   def __init__(    self			:				List[Any]       ,	_lowercase			:				List[str]       ,	_lowercase			:				int=7       ,	_lowercase			:				Union[str, Any]=3       ,	_lowercase			:				Dict=30       ,	_lowercase			:				int=4_00       ,	_lowercase			:				Tuple=True       ,	_lowercase			:				List[Any]=None       ,	_lowercase			:				Any=True       ,	_lowercase			:				List[Any]=[0.5, 0.5, 0.5]       ,	_lowercase			:				Union[str, Any]=[0.5, 0.5, 0.5]       ,	_lowercase			:				List[Any]=True       ,	_lowercase			:				int=1 / 2_55       ,	_lowercase			:				Optional[int]=True       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      num_channels
     SCREAMING_SNAKE_CASE__										=      min_resolution
     SCREAMING_SNAKE_CASE__										=      max_resolution
     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      size
     SCREAMING_SNAKE_CASE__										=      do_normalize
     SCREAMING_SNAKE_CASE__										=      image_mean
     SCREAMING_SNAKE_CASE__										=      image_std
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      rescale_factor
     SCREAMING_SNAKE_CASE__										=      do_pad




   def 	__a						(    self			:				int      ):



     """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 	__a						(    self			:				List[Any]       ,	_lowercase			:				str       ,	_lowercase			:				Optional[int]=False      ):



     """simple docstring"""






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

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

     return expected_height, expected_width







@require_torch
@require_vision
class __snake_case				(						A_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							ConditionalDetrImageProcessor if is_vision_available() else None




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ConditionalDetrImageProcessingTester(self      )




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      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_resize"""      )      )
     self.assertTrue(hasattr(A_       ,	"""size"""      )      )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class.from_dict(self.image_processor_dict      )
     self.assertEqual(image_processor.size       ,	{"""shortest_edge""": 18, """longest_edge""": 13_33}      )
     self.assertEqual(image_processor.do_pad       ,	A_      )

     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				Dict      ):



     """simple docstring"""






     pass




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.image_processor_tester.get_expected_values(A_       ,	batched=A_      )

     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      image_processing(image_inputs[0]       ,	return_tensors="""pt"""      ).pixel_values

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      image_processing(A_       ,	return_tensors="""pt"""      ).pixel_values

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      image_processing(image_inputs[0]       ,	return_tensors="""pt"""      ).pixel_values

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      image_processing(A_       ,	return_tensors="""pt"""      ).pixel_values

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				List[str]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      {"""image_id""": 3_97_69, """annotations""": target}

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

     # verify pixel values
     SCREAMING_SNAKE_CASE__										=      torch.Size([1, 3, 8_00, 10_66]      )
     self.assertEqual(encoding["""pixel_values"""].shape       ,	A_      )

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

     # verify area
     SCREAMING_SNAKE_CASE__										=      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"""]       ,	A_      )      )
     # verify boxes
     SCREAMING_SNAKE_CASE__										=      torch.Size([6, 4]      )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,	A_      )
     SCREAMING_SNAKE_CASE__										=      torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,	A_       ,	atol=1E-3      )      )
     # verify image_id
     SCREAMING_SNAKE_CASE__										=      torch.tensor([3_97_69]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,	A_      )      )
     # verify is_crowd
     SCREAMING_SNAKE_CASE__										=      torch.tensor([0, 0, 0, 0, 0, 0]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,	A_      )      )
     # verify class_labels
     SCREAMING_SNAKE_CASE__										=      torch.tensor([75, 75, 63, 65, 17, 17]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,	A_      )      )
     # verify orig_size
     SCREAMING_SNAKE_CASE__										=      torch.tensor([4_80, 6_40]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,	A_      )      )
     # verify size
     SCREAMING_SNAKE_CASE__										=      torch.tensor([8_00, 10_66]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,	A_      )      )




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



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

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

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

     # verify pixel values
     SCREAMING_SNAKE_CASE__										=      torch.Size([1, 3, 8_00, 10_66]      )
     self.assertEqual(encoding["""pixel_values"""].shape       ,	A_      )

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

     # verify area
     SCREAMING_SNAKE_CASE__										=      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"""]       ,	A_      )      )
     # verify boxes
     SCREAMING_SNAKE_CASE__										=      torch.Size([6, 4]      )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,	A_      )
     SCREAMING_SNAKE_CASE__										=      torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,	A_       ,	atol=1E-3      )      )
     # verify image_id
     SCREAMING_SNAKE_CASE__										=      torch.tensor([3_97_69]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,	A_      )      )
     # verify is_crowd
     SCREAMING_SNAKE_CASE__										=      torch.tensor([0, 0, 0, 0, 0, 0]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,	A_      )      )
     # verify class_labels
     SCREAMING_SNAKE_CASE__										=      torch.tensor([17, 17, 63, 75, 75, 93]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,	A_      )      )
     # verify masks
     SCREAMING_SNAKE_CASE__										=      82_28_73
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,	A_      )
     # verify orig_size
     SCREAMING_SNAKE_CASE__										=      torch.tensor([4_80, 6_40]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,	A_      )      )
     # verify size
     SCREAMING_SNAKE_CASE__										=      torch.tensor([8_00, 10_66]      )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,	A_      )      )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





