





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 DetaImageProcessor



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:      int			  =size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
   lowerCamelCase__:      Tuple			  =parent
   lowerCamelCase__:      Union[str, Any]			  =batch_size
   lowerCamelCase__:      Tuple			  =num_channels
   lowerCamelCase__:      int			  =min_resolution
   lowerCamelCase__:      int			  =max_resolution
   lowerCamelCase__:      Dict			  =do_resize
   lowerCamelCase__:      List[Any]			  =size
   lowerCamelCase__:      Any			  =do_normalize
   lowerCamelCase__:      Optional[Any]			  =image_mean
   lowerCamelCase__:      int			  =image_std
   lowerCamelCase__:      Tuple			  =do_rescale
   lowerCamelCase__:      int			  =rescale_factor
   lowerCamelCase__:      int			  =do_pad

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->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 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   List[str]=False)       ->str:



   '''simple docstring'''



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

   else:
     lowerCamelCase__:      Optional[int]			  =[]
     for image in image_inputs:
       lowerCamelCase__  ,					lowerCamelCase__:      List[Any]			  =self.get_expected_values([image])
       expected_values.append((expected_height, expected_width))
     lowerCamelCase__:      Tuple			  =max(UpperCAmelCase_       , key=lambda UpperCAmelCase_: item[0])[0]
     lowerCamelCase__:      Union[str, Any]			  =max(UpperCAmelCase_       , key=lambda UpperCAmelCase_: item[1])[1]

   return expected_height, expected_width



@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			DetaImageProcessor if is_vision_available() else None

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =DetaImageProcessingTester(self)

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Dict:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_mean"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "image_std"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_normalize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_resize"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_rescale"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "do_pad"))
   self.assertTrue(hasattr(UpperCAmelCase_       , "size"))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =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       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Any:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:      Any			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , Image.Image)

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

   lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =self.image_processor_tester.get_expected_values(UpperCAmelCase_)

   self.assertEqual(
       encoded_images.shape       , (1, self.image_processor_tester.num_channels, expected_height, expected_width)       , )

   # Test batched
   lowerCamelCase__  ,					lowerCamelCase__:      List[str]			  =self.image_processor_tester.get_expected_values(UpperCAmelCase_       , batched=UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =image_processing(UpperCAmelCase_       , 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 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:      Any			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , numpify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , np.ndarray)

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

   lowerCamelCase__  ,					lowerCamelCase__:      List[Any]			  =self.image_processor_tester.get_expected_values(UpperCAmelCase_)

   self.assertEqual(
       encoded_images.shape       , (1, self.image_processor_tester.num_channels, expected_height, expected_width)       , )

   # Test batched
   lowerCamelCase__:      Dict			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values

   lowerCamelCase__  ,					lowerCamelCase__:      int			  =self.image_processor_tester.get_expected_values(UpperCAmelCase_       , batched=UpperCAmelCase_)

   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )       , )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:      str			  =prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , torchify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_       , torch.Tensor)

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

   lowerCamelCase__  ,					lowerCamelCase__:      Optional[int]			  =self.image_processor_tester.get_expected_values(UpperCAmelCase_)

   self.assertEqual(
       encoded_images.shape       , (1, self.image_processor_tester.num_channels, expected_height, expected_width)       , )

   # Test batched
   lowerCamelCase__:      str			  =image_processing(UpperCAmelCase_       , return_tensors="pt").pixel_values

   lowerCamelCase__  ,					lowerCamelCase__:      Optional[Any]			  =self.image_processor_tester.get_expected_values(UpperCAmelCase_       , batched=UpperCAmelCase_)

   self.assertEqual(
       encoded_images.shape       , (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )       , )

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->str:



   '''simple docstring'''



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

   lowerCamelCase__:      Optional[int]			  ={"image_id": 39_769, "annotations": target}

   # encode them
   lowerCamelCase__:      List[str]			  =DetaImageProcessor()
   lowerCamelCase__:      int			  =image_processing(images=UpperCAmelCase_       , annotations=UpperCAmelCase_       , return_tensors="pt")

   # verify pixel values
   lowerCamelCase__:      Dict			  =torch.Size([1, 3, 800, 1_066])
   self.assertEqual(encoding["pixel_values"].shape       , UpperCAmelCase_)

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

   # verify area
   lowerCamelCase__:      int			  =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"]       , UpperCAmelCase_))
   # verify boxes
   lowerCamelCase__:      str			  =torch.Size([6, 4])
   self.assertEqual(encoding["labels"][0]["boxes"].shape       , UpperCAmelCase_)
   lowerCamelCase__:      str			  =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       , UpperCAmelCase_       , atol=1E-3))
   # verify image_id
   lowerCamelCase__:      Optional[Any]			  =torch.tensor([39_769])
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       , UpperCAmelCase_))
   # verify is_crowd
   lowerCamelCase__:      Dict			  =torch.tensor([0, 0, 0, 0, 0, 0])
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       , UpperCAmelCase_))
   # verify class_labels
   lowerCamelCase__:      int			  =torch.tensor([75, 75, 63, 65, 17, 17])
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       , UpperCAmelCase_))
   # verify orig_size
   lowerCamelCase__:      Union[str, Any]			  =torch.tensor([480, 640])
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       , UpperCAmelCase_))
   # verify size
   lowerCamelCase__:      Optional[int]			  =torch.tensor([800, 1_066])
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       , UpperCAmelCase_))







 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Tuple:



   '''simple docstring'''



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

   lowerCamelCase__:      Any			  ={"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

   lowerCamelCase__:      Dict			  =pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

   # encode them
   lowerCamelCase__:      Union[str, Any]			  =DetaImageProcessor(format="coco_panoptic")
   lowerCamelCase__:      Union[str, Any]			  =image_processing(images=UpperCAmelCase_       , annotations=UpperCAmelCase_       , masks_path=UpperCAmelCase_       , return_tensors="pt")

   # verify pixel values
   lowerCamelCase__:      List[Any]			  =torch.Size([1, 3, 800, 1_066])
   self.assertEqual(encoding["pixel_values"].shape       , UpperCAmelCase_)

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

   # verify area
   lowerCamelCase__:      Any			  =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"]       , UpperCAmelCase_))
   # verify boxes
   lowerCamelCase__:      List[str]			  =torch.Size([6, 4])
   self.assertEqual(encoding["labels"][0]["boxes"].shape       , UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       , UpperCAmelCase_       , atol=1E-3))
   # verify image_id
   lowerCamelCase__:      Dict			  =torch.tensor([39_769])
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       , UpperCAmelCase_))
   # verify is_crowd
   lowerCamelCase__:      Optional[int]			  =torch.tensor([0, 0, 0, 0, 0, 0])
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       , UpperCAmelCase_))
   # verify class_labels
   lowerCamelCase__:      Union[str, Any]			  =torch.tensor([17, 17, 63, 75, 75, 93])
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       , UpperCAmelCase_))
   # verify masks
   lowerCamelCase__:      int			  =822_873
   self.assertEqual(encoding["labels"][0]["masks"].sum().item()       , UpperCAmelCase_)
   # verify orig_size
   lowerCamelCase__:      List[Any]			  =torch.tensor([480, 640])
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       , UpperCAmelCase_))
   # verify size
   lowerCamelCase__:      int			  =torch.tensor([800, 1_066])
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       , UpperCAmelCase_))








class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   Optional[Any]       , UpperCAmelCase_				:   int)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =n
   lowerCamelCase__:      Tuple			  =[None] * self.n
   lowerCamelCase__:      str			  =0  # index of the first element
   lowerCamelCase__:      Tuple			  =0
   lowerCamelCase__:      Optional[Any]			  =0

 def __len__(self				:   str)       ->int:



   '''simple docstring'''



   return self.size

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->bool:



   '''simple docstring'''



   return self.size == 0

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->str:



   '''simple docstring'''



   return False if self.is_empty() else self.array[self.front]

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Optional[int])       ->str:



   '''simple docstring'''



   if self.size >= self.n:
     raise Exception("QUEUE IS FULL")

   lowerCamelCase__:      List[Any]			  =data
   lowerCamelCase__:      Dict			  =(self.rear + 1) % self.n
   self.size += 1
   return self







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Tuple:



   '''simple docstring'''



   if self.size == 0:
     raise Exception("UNDERFLOW")

   lowerCamelCase__:      Optional[Any]			  =self.array[self.front]
   lowerCamelCase__:      Optional[int]			  =None
   lowerCamelCase__:      Dict			  =(self.front + 1) % self.n
   self.size -= 1
   return temp

