"""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 DeformableDetrImageProcessor





class 				_A		( unittest.TestCase			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=7	, __lowerCAmelCase=3	, __lowerCAmelCase=30	, __lowerCAmelCase=400	, __lowerCAmelCase=True	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=[0.5, 0.5, 0.5]	, __lowerCAmelCase=[0.5, 0.5, 0.5]	, __lowerCAmelCase=True	, __lowerCAmelCase=1 / 255	, __lowerCAmelCase=True	, ):







            """simple docstring"""



            lowercase														=			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			num_channels
            lowercase														=			min_resolution
            lowercase														=			max_resolution
            lowercase														=			do_resize
            lowercase														=			size
            lowercase														=			do_normalize
            lowercase														=			image_mean
            lowercase														=			image_std
            lowercase														=			do_rescale
            lowercase														=			rescale_factor
            lowercase														=			do_pad

      def        A__ (		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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=False		):







            """simple docstring"""



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

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

            return expected_height, expected_width






@require_torch
@require_vision
class 				_A		( __snake_case						,					unittest.TestCase			):
      snake_case__    :     int													=			DeformableDetrImageProcessor if is_vision_available() else None

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			DeformableDetrImageProcessingTester(self		)

      @property
      def        A__ (		self		):







            """simple docstring"""



            return self.image_processor_tester.prepare_image_processor_dict()

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.image_processing_class(**self.image_processor_dict		)
            self.assertTrue(hasattr(_lowercase	, """image_mean"""		)		)
            self.assertTrue(hasattr(_lowercase	, """image_std"""		)		)
            self.assertTrue(hasattr(_lowercase	, """do_normalize"""		)		)
            self.assertTrue(hasattr(_lowercase	, """do_resize"""		)		)
            self.assertTrue(hasattr(_lowercase	, """do_rescale"""		)		)
            self.assertTrue(hasattr(_lowercase	, """do_pad"""		)		)
            self.assertTrue(hasattr(_lowercase	, """size"""		)		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			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	, _lowercase		)

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

      def        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self		):







            """simple docstring"""



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

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

            lowercase														=			self.image_processor_tester.get_expected_values(_lowercase		)

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

            # Test batched
            lowercase														=			self.image_processor_tester.get_expected_values(_lowercase	, batched=_lowercase		)

            lowercase														=			image_processing(_lowercase	, 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		):







            """simple docstring"""



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

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

            lowercase														=			self.image_processor_tester.get_expected_values(_lowercase		)

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

            # Test batched
            lowercase														=			image_processing(_lowercase	, return_tensors="""pt"""		).pixel_values

            lowercase														=			self.image_processor_tester.get_expected_values(_lowercase	, batched=_lowercase		)

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

      def        A__ (		self		):







            """simple docstring"""



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

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

            lowercase														=			self.image_processor_tester.get_expected_values(_lowercase		)

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

            # Test batched
            lowercase														=			image_processing(_lowercase	, return_tensors="""pt"""		).pixel_values

            lowercase														=			self.image_processor_tester.get_expected_values(_lowercase	, batched=_lowercase		)

            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		):







            """simple docstring"""



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

            lowercase														=			{"""image_id""": 3_9769, """annotations""": target}

            # encode them
            lowercase														=			DeformableDetrImageProcessor()
            lowercase														=			image_processing(images=_lowercase	, annotations=_lowercase	, return_tensors="""pt"""		)

            # verify pixel values
            lowercase														=			torch.Size([1, 3, 800, 1066]		)
            self.assertEqual(encoding["""pixel_values"""].shape	, _lowercase		)

            lowercase														=			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]	, _lowercase	, atol=1E-4		)		)

            # verify area
            lowercase														=			torch.tensor([5_8_8_7.9_6_0_0, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	, _lowercase		)		)
            # verify boxes
            lowercase														=			torch.Size([6, 4]		)
            self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	, _lowercase		)
            lowercase														=			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]	, _lowercase	, atol=1E-3		)		)
            # verify image_id
            lowercase														=			torch.tensor([3_9769]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	, _lowercase		)		)
            # verify is_crowd
            lowercase														=			torch.tensor([0, 0, 0, 0, 0, 0]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	, _lowercase		)		)
            # verify class_labels
            lowercase														=			torch.tensor([75, 75, 63, 65, 17, 17]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	, _lowercase		)		)
            # verify orig_size
            lowercase														=			torch.tensor([480, 640]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	, _lowercase		)		)
            # verify size
            lowercase														=			torch.tensor([800, 1066]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	, _lowercase		)		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



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

            lowercase														=			{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

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

            # encode them
            lowercase														=			DeformableDetrImageProcessor(format="""coco_panoptic"""		)
            lowercase														=			image_processing(images=_lowercase	, annotations=_lowercase	, masks_path=_lowercase	, return_tensors="""pt"""		)

            # verify pixel values
            lowercase														=			torch.Size([1, 3, 800, 1066]		)
            self.assertEqual(encoding["""pixel_values"""].shape	, _lowercase		)

            lowercase														=			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]	, _lowercase	, atol=1E-4		)		)

            # verify area
            lowercase														=			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	, _lowercase		)		)
            # verify boxes
            lowercase														=			torch.Size([6, 4]		)
            self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	, _lowercase		)
            lowercase														=			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]	, _lowercase	, atol=1E-3		)		)
            # verify image_id
            lowercase														=			torch.tensor([3_9769]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	, _lowercase		)		)
            # verify is_crowd
            lowercase														=			torch.tensor([0, 0, 0, 0, 0, 0]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	, _lowercase		)		)
            # verify class_labels
            lowercase														=			torch.tensor([17, 17, 63, 75, 75, 93]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	, _lowercase		)		)
            # verify masks
            lowercase														=			82_2873
            self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()	, _lowercase		)
            # verify orig_size
            lowercase														=			torch.tensor([480, 640]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	, _lowercase		)		)
            # verify size
            lowercase														=			torch.tensor([800, 1066]		)
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	, _lowercase		)		)




"""simple docstring"""






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase					: Union[str, Any]        =subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowerCAmelCase					: List[str]        =subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()

__lowerCAmelCase					: List[str]        ="""|""".join(sys.argv[1:])
__lowerCAmelCase					: Dict        =re.compile(RF"""^({joined_dirs}).*?\.py$""")

__lowerCAmelCase					: int        =[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")



