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 DeformableDetrImageProcessor


class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






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




    '''simple docstring'''




    __UpperCAmelCase						    =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
    __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:			Tuple   ) -> 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 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[int]=False   ) -> Tuple:




    '''simple docstring'''




    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__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[Any]    		= DeformableDetrImageProcessor if is_vision_available() else None






  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       DeformableDetrImageProcessingTester(self   )






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




    '''simple docstring'''




    return self.image_processor_tester.prepare_image_processor_dict()






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




    '''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				,				"do_rescale"   )   )
    self.assertTrue(hasattr(__lowerCAmelCase				,				"do_pad"   )   )
    self.assertTrue(hasattr(__lowerCAmelCase				,				"size"   )   )






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.image_processing_class.from_dict(self.image_processor_dict   )
    self.assertEqual(image_processor.size				,				{"shortest_edge": 18, "longest_edge": 1_333}   )
    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:			Any   ) -> int:




    '''simple docstring'''




    pass






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> Union[str, Any]:




    '''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    ,		__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:			List[Any]   ) -> Optional[Any]:




    '''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    ,		__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:			int   ) -> str:




    '''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    ,		__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:			int   ) -> str:




    '''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": 39_769, "annotations": target}

    # encode them
    __UpperCAmelCase						    =       DeformableDetrImageProcessor()
    __UpperCAmelCase						    =       image_processing(images=__lowerCAmelCase				,				annotations=__lowerCAmelCase				,				return_tensors="pt"   )

    # verify pixel values
    __UpperCAmelCase						    =       torch.Size([1, 3, 800, 1_066]   )
    self.assertEqual(encoding["pixel_values"].shape				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       torch.tensor([0.2796, 0.3138, 0.3481]   )
    self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,				__lowerCAmelCase				,				atol=1E-4   )   )

    # verify area
    __UpperCAmelCase						    =       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
    __UpperCAmelCase						    =       torch.Size([6, 4]   )
    self.assertEqual(encoding["labels"][0]["boxes"].shape				,				__lowerCAmelCase   )
    __UpperCAmelCase						    =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
    self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,				__lowerCAmelCase				,				atol=1E-3   )   )
    # verify image_id
    __UpperCAmelCase						    =       torch.tensor([39_769]   )
    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, 1_066]   )
    self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,				__lowerCAmelCase   )   )






  @slow
  def 	_UpperCAmelCase				(				self:			Dict   ) -> 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": 39_769, "segments_info": target}

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

    # encode them
    __UpperCAmelCase						    =       DeformableDetrImageProcessor(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, 1_066]   )
    self.assertEqual(encoding["pixel_values"].shape				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       torch.tensor([0.2796, 0.3138, 0.3481]   )
    self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,				__lowerCAmelCase				,				atol=1E-4   )   )

    # verify area
    __UpperCAmelCase						    =       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
    __UpperCAmelCase						    =       torch.Size([6, 4]   )
    self.assertEqual(encoding["labels"][0]["boxes"].shape				,				__lowerCAmelCase   )
    __UpperCAmelCase						    =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
    self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,				__lowerCAmelCase				,				atol=1E-3   )   )
    # verify image_id
    __UpperCAmelCase						    =       torch.tensor([39_769]   )
    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						    =       822_873
    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, 1_066]   )
    self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,				__lowerCAmelCase   )   )




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


a_							=      logging.get_logger(__name__)

a_							=      {
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Dict    		= 'vit_mae'






  def __init__(				self:			List[Any]				,				__lowerCAmelCase:			Any=768				,				__lowerCAmelCase:			List[str]=12				,				__lowerCAmelCase:			Optional[int]=12				,				__lowerCAmelCase:			Tuple=3_072				,				__lowerCAmelCase:			List[Any]="gelu"				,				__lowerCAmelCase:			Dict=0.0				,				__lowerCAmelCase:			Tuple=0.0				,				__lowerCAmelCase:			Any=0.02				,				__lowerCAmelCase:			List[Any]=1E-12				,				__lowerCAmelCase:			List[str]=224				,				__lowerCAmelCase:			Optional[Any]=16				,				__lowerCAmelCase:			Union[str, Any]=3				,				__lowerCAmelCase:			Tuple=True				,				__lowerCAmelCase:			Union[str, Any]=16				,				__lowerCAmelCase:			Optional[int]=512				,				__lowerCAmelCase:			int=8				,				__lowerCAmelCase:			int=2_048				,				__lowerCAmelCase:			str=0.75				,				__lowerCAmelCase:			Union[str, Any]=False				,				**__lowerCAmelCase:			List[Any]				,				) -> Optional[int]:




    '''simple docstring'''




    super().__init__(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       num_hidden_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       intermediate_size
    __UpperCAmelCase						    =       hidden_act
    __UpperCAmelCase						    =       hidden_dropout_prob
    __UpperCAmelCase						    =       attention_probs_dropout_prob
    __UpperCAmelCase						    =       initializer_range
    __UpperCAmelCase						    =       layer_norm_eps
    __UpperCAmelCase						    =       image_size
    __UpperCAmelCase						    =       patch_size
    __UpperCAmelCase						    =       num_channels
    __UpperCAmelCase						    =       qkv_bias
    __UpperCAmelCase						    =       decoder_num_attention_heads
    __UpperCAmelCase						    =       decoder_hidden_size
    __UpperCAmelCase						    =       decoder_num_hidden_layers
    __UpperCAmelCase						    =       decoder_intermediate_size
    __UpperCAmelCase						    =       mask_ratio
    __UpperCAmelCase						    =       norm_pix_loss



