





'''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  UpperCAmelCase     (			unittest.TestCase   ):




      def __init__(			self			: List[Any]			,			__snake_case			: Optional[int]			,			__snake_case			: int=7			,			__snake_case			: int=3			,			__snake_case			: Union[str, Any]=30			,			__snake_case			: int=4_00			,			__snake_case			: int=True			,			__snake_case			: Tuple=None			,			__snake_case			: List[str]=True			,			__snake_case			: int=[0.5, 0.5, 0.5]			,			__snake_case			: str=[0.5, 0.5, 0.5]			,			__snake_case			: Optional[Any]=True			,			__snake_case			: Dict=1 / 2_55			,			__snake_case			: List[Any]=True			,			)				->      Optional[int]:
            # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
            _lowerCAmelCase							     =							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
            _lowerCAmelCase							     =							parent
            _lowerCAmelCase							     =							batch_size
            _lowerCAmelCase							     =							num_channels
            _lowerCAmelCase							     =							min_resolution
            _lowerCAmelCase							     =							max_resolution
            _lowerCAmelCase							     =							do_resize
            _lowerCAmelCase							     =							size
            _lowerCAmelCase							     =							do_normalize
            _lowerCAmelCase							     =							image_mean
            _lowerCAmelCase							     =							image_std
            _lowerCAmelCase							     =							do_rescale
            _lowerCAmelCase							     =							rescale_factor
            _lowerCAmelCase							     =							do_pad




      def 				lowercase__    (			self			: List[str]  )				->      Optional[Any]:
            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			: List[Any]			,			__snake_case			: List[str]			,			__snake_case			: Tuple=False  )				->      Any:
            if not batched:
                  _lowerCAmelCase							     =							image_inputs[0]
                  if isinstance(__snake_case			,			Image.Image  ):
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							image.size
                  else:
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							image.shape[1], image.shape[2]
                  if w < h:
                        _lowerCAmelCase							     =							int(self.size["""shortest_edge"""] * h / w  )
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]
                  elif w > h:
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]
                        _lowerCAmelCase							     =							int(self.size["""shortest_edge"""] * w / h  )
                  else:
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]

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

            return expected_height, expected_width




@require_torch
@require_vision
class  UpperCAmelCase     (			snake_case_							,		unittest.TestCase   ):
      _lowercase:			Any						     =   DeformableDetrImageProcessor if is_vision_available() else None




      def 				lowercase__    (			self			: Dict  )				->      str:
            _lowerCAmelCase							     =							DeformableDetrImageProcessingTester(self  )




      @property
      def 				lowercase__    (			self			: str  )				->      Tuple:
            return self.image_processor_tester.prepare_image_processor_dict()




      def 				lowercase__    (			self			: List[Any]  )				->      List[str]:
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__snake_case			,			"""image_mean"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""image_std"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_normalize"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_resize"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_rescale"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_pad"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""size"""  )  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      Optional[Any]:
            _lowerCAmelCase							     =							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			,			__snake_case  )

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




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




      def 				lowercase__    (			self			: Tuple  )				->      List[Any]:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random PIL images
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			Image.Image  )

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

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case  )

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

            # Test batched
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case			,			batched=__snake_case  )

            _lowerCAmelCase							     =							image_processing(__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			: Dict  )				->      Any:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random numpy tensors
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case			,			numpify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			np.ndarray  )

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

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case  )

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

            # Test batched
            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case			,			batched=__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			: Optional[Any]  )				->      Any:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random PyTorch tensors
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case			,			torchify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			torch.Tensor  )

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

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case  )

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

            # Test batched
            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case			,			batched=__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			: Optional[int]  )				->      Tuple:
            # prepare image and target
            _lowerCAmelCase							     =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
            with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""			,			"""r"""  ) as f:
                  _lowerCAmelCase							     =							json.loads(f.read()  )

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

            # encode them
            _lowerCAmelCase							     =							DeformableDetrImageProcessor()
            _lowerCAmelCase							     =							image_processing(images=__snake_case			,			annotations=__snake_case			,			return_tensors="""pt"""  )

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

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

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




      @slow
      def 				lowercase__    (			self			: Optional[Any]  )				->      int:
            # prepare image, target and masks_path
            _lowerCAmelCase							     =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
            with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""			,			"""r"""  ) as f:
                  _lowerCAmelCase							     =							json.loads(f.read()  )

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

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

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

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

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

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









from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

