




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 DetrImageProcessor







class 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a=7		,     _a=3		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=1 / 255		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     _a=True		,     )						->							Dict:
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           _A							:     List[str]			=						size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
           _A							:     int			=						parent
           _A							:     Optional[Any]			=						batch_size
           _A							:     List[str]			=						num_channels
           _A							:     Dict			=						min_resolution
           _A							:     Union[str, Any]			=						max_resolution
           _A							:     Optional[Any]			=						do_resize
           _A							:     Optional[int]			=						size
           _A							:     Optional[Any]			=						do_rescale
           _A							:     Optional[Any]			=						rescale_factor
           _A							:     Optional[int]			=						do_normalize
           _A							:     List[str]			=						image_mean
           _A							:     Optional[Any]			=						image_std
           _A							:     Union[str, Any]			=						do_pad

     def 					a__	(  self				)						->							Dict:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_pad": self.do_pad,
           }



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

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

           return expected_height, expected_width







@require_torch
@require_vision
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						DetrImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							List[str]:
           _A							:     List[str]			=						DetrImageProcessingTester(self				)

     @property
     def 					a__	(  self				)						->							Optional[int]:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Optional[int]			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """do_rescale"""				)				)
           self.assertTrue(hasattr(_a		,     """rescale_factor"""				)				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)
           self.assertTrue(hasattr(_a		,     """do_pad"""				)				)

     def 					a__	(  self				)						->							str:
           _A							:     Optional[Any]			=						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		,     _a				)

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

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

     def 					a__	(  self				)						->							int:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Tuple			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

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

           _A			,   _A							:     int			=						self.image_processor_tester.get_expected_values(_a				)

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

           # Test batched
           _A			,   _A							:     List[Any]			=						self.image_processor_tester.get_expected_values(_a		,     batched=_a				)

           _A							:     Dict			=						image_processing(_a		,     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				)						->							List[Any]:
           # Initialize image_processing
           _A							:     int			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     List[str]			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

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

           _A			,   _A							:     Optional[int]			=						self.image_processor_tester.get_expected_values(_a				)

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

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

           _A			,   _A							:     Dict			=						self.image_processor_tester.get_expected_values(_a		,     batched=_a				)

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

     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     List[str]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     Any			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

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

           _A			,   _A							:     str			=						self.image_processor_tester.get_expected_values(_a				)

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

           # Test batched
           _A							:     Tuple			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values

           _A			,   _A							:     List[str]			=						self.image_processor_tester.get_expected_values(_a		,     batched=_a				)

           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				)						->							Tuple:
           # prepare image and target
           _A							:     Tuple			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
           with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""		,     """r"""				) as f:
                 _A							:     Union[str, Any]			=						json.loads(f.read()				)

           _A							:     List[str]			=						{"""image_id""": 3_9769, """annotations""": target}

           # encode them
           _A							:     List[Any]			=						DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""				)
           _A							:     Any			=						image_processing(images=_a		,     annotations=_a		,     return_tensors="""pt"""				)

           # verify pixel values
           _A							:     Tuple			=						torch.Size([1, 3, 800, 1066]				)
           self.assertEqual(encoding["""pixel_values"""].shape		,     _a				)

           _A							:     str			=						torch.tensor([0.2796, 0.3138, 0.3481]				)
           self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]		,     _a		,     atol=1e-4				)				)

           # verify area
           _A							:     Dict			=						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,     _a				)				)
           # verify boxes
           _A							:     str			=						torch.Size([6, 4]				)
           self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,     _a				)
           _A							:     List[Any]			=						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,     _a		,     atol=1e-3				)				)
           # verify image_id
           _A							:     Tuple			=						torch.tensor([3_9769]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,     _a				)				)
           # verify is_crowd
           _A							:     str			=						torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,     _a				)				)
           # verify class_labels
           _A							:     int			=						torch.tensor([75, 75, 63, 65, 17, 17]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,     _a				)				)
           # verify orig_size
           _A							:     List[Any]			=						torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,     _a				)				)
           # verify size
           _A							:     Optional[Any]			=						torch.tensor([800, 1066]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,     _a				)				)



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

           _A							:     Optional[Any]			=						{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

           _A							:     Tuple			=						pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

           # encode them
           _A							:     Optional[int]			=						DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""				)
           _A							:     Tuple			=						image_processing(images=_a		,     annotations=_a		,     masks_path=_a		,     return_tensors="""pt"""				)

           # verify pixel values
           _A							:     str			=						torch.Size([1, 3, 800, 1066]				)
           self.assertEqual(encoding["""pixel_values"""].shape		,     _a				)

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

           # verify area
           _A							:     List[Any]			=						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,     _a				)				)
           # verify boxes
           _A							:     List[str]			=						torch.Size([6, 4]				)
           self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,     _a				)
           _A							:     Any			=						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,     _a		,     atol=1e-3				)				)
           # verify image_id
           _A							:     Optional[Any]			=						torch.tensor([3_9769]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,     _a				)				)
           # verify is_crowd
           _A							:     Union[str, Any]			=						torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,     _a				)				)
           # verify class_labels
           _A							:     Union[str, Any]			=						torch.tensor([17, 17, 63, 75, 75, 93]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,     _a				)				)
           # verify masks
           _A							:     List[str]			=						82_2873
           self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()		,     _a				)
           # verify orig_size
           _A							:     Union[str, Any]			=						torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,     _a				)				)
           # verify size
           _A							:     Dict			=						torch.tensor([800, 1066]				)
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,     _a				)				)







from __future__ import annotations

import numpy as np







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.maximum(0,snake_case_     )


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]

