
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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=1 / 2_55       ,	SCREAMING_SNAKE_CASE_=True       ,	):


   '''simple docstring'''




   lowercase__	: Tuple     		=   size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
   lowercase__	: Dict     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: Optional[int]     		=   num_channels
   lowercase__	: Union[str, Any]     		=   min_resolution
   lowercase__	: List[Any]     		=   max_resolution
   lowercase__	: Union[str, Any]     		=   do_resize
   lowercase__	: List[str]     		=   size
   lowercase__	: List[str]     		=   do_normalize
   lowercase__	: List[str]     		=   image_mean
   lowercase__	: Union[str, Any]     		=   image_std
   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: str     		=   rescale_factor
   lowercase__	: Dict     		=   do_pad


 def   lowercase__  (       self):


   '''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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=False):


   '''simple docstring'''




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

   else:
     lowercase__	: Tuple     		=   []
     for image in image_inputs:
       lowercase__		,     lowercase__	: Tuple     		=   self.get_expected_values([image])
       expected_values.append((expected_height, expected_width))
     lowercase__	: Optional[int]     		=   max(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: item[0])[0]
     lowercase__	: Dict     		=   max(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: item[1])[1]

   return expected_height, expected_width



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[int]	   =      ConditionalDetrImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   ConditionalDetrImageProcessingTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   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       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   self.image_processing_class.from_dict(
       self.image_processor_dict       ,	size=42       ,	max_size=84       ,	pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_)
   self.assertEqual(image_processor.size       ,	{"""shortest_edge""": 42, """longest_edge""": 84})
   self.assertEqual(image_processor.do_pad       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




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

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

   lowercase__		,     lowercase__	: List[str]     		=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_)

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

   # Test batched
   lowercase__		,     lowercase__	: str     		=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,	batched=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   image_processing(SCREAMING_SNAKE_CASE_       ,	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   lowercase__  (       self):


   '''simple docstring'''




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

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

   lowercase__		,     lowercase__	: Optional[Any]     		=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_)

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

   # Test batched
   lowercase__	: Any     		=   image_processing(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values

   lowercase__		,     lowercase__	: int     		=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,	batched=SCREAMING_SNAKE_CASE_)

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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Optional[int]     		=   prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

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

   lowercase__		,     lowercase__	: Dict     		=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_)

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

   # Test batched
   lowercase__	: Union[str, Any]     		=   image_processing(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values

   lowercase__		,     lowercase__	: Tuple     		=   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,	batched=SCREAMING_SNAKE_CASE_)

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


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
   with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,	"""r""") as f:
     lowercase__	: Optional[Any]     		=   json.loads(f.read())

   lowercase__	: Union[str, Any]     		=   {"""image_id""": 3_97_69, """annotations""": target}

   # encode them
   lowercase__	: Union[str, Any]     		=   ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""")
   lowercase__	: int     		=   image_processing(images=SCREAMING_SNAKE_CASE_       ,	annotations=SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""")

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

   lowercase__	: int     		=   torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
   self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,	SCREAMING_SNAKE_CASE_       ,	atol=1E-4))

   # verify area
   lowercase__	: str     		=   torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify boxes
   lowercase__	: List[Any]     		=   torch.Size([6, 4])
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,	SCREAMING_SNAKE_CASE_       ,	atol=1E-3))
   # verify image_id
   lowercase__	: List[str]     		=   torch.tensor([3_97_69])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify is_crowd
   lowercase__	: Optional[Any]     		=   torch.tensor([0, 0, 0, 0, 0, 0])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify class_labels
   lowercase__	: Tuple     		=   torch.tensor([75, 75, 63, 65, 17, 17])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify orig_size
   lowercase__	: Optional[int]     		=   torch.tensor([4_80, 6_40])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify size
   lowercase__	: Optional[Any]     		=   torch.tensor([8_00, 10_66])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,	SCREAMING_SNAKE_CASE_))



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: Optional[Any]     		=   {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

   lowercase__	: int     		=   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""")

   # encode them
   lowercase__	: str     		=   ConditionalDetrImageProcessor(format="""coco_panoptic""")
   lowercase__	: Any     		=   image_processing(images=SCREAMING_SNAKE_CASE_       ,	annotations=SCREAMING_SNAKE_CASE_       ,	masks_path=SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""")

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

   lowercase__	: str     		=   torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
   self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,	SCREAMING_SNAKE_CASE_       ,	atol=1E-4))

   # verify area
   lowercase__	: Tuple     		=   torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify boxes
   lowercase__	: str     		=   torch.Size([6, 4])
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,	SCREAMING_SNAKE_CASE_       ,	atol=1E-3))
   # verify image_id
   lowercase__	: List[str]     		=   torch.tensor([3_97_69])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify is_crowd
   lowercase__	: int     		=   torch.tensor([0, 0, 0, 0, 0, 0])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify class_labels
   lowercase__	: Union[str, Any]     		=   torch.tensor([17, 17, 63, 75, 75, 93])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify masks
   lowercase__	: Union[str, Any]     		=   82_28_73
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,	SCREAMING_SNAKE_CASE_)
   # verify orig_size
   lowercase__	: List[str]     		=   torch.tensor([4_80, 6_40])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,	SCREAMING_SNAKE_CASE_))
   # verify size
   lowercase__	: Union[str, Any]     		=   torch.tensor([8_00, 10_66])
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,	SCREAMING_SNAKE_CASE_))








lowerCamelCase__     :		List[str]        = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase__     :		List[Any]        = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase__     :		int        = {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






