



'''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 DetrImageProcessor


class 					snake_case			(unittest.TestCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=7  ,UpperCAmelCase_=3  ,UpperCAmelCase_=30  ,UpperCAmelCase_=400  ,UpperCAmelCase_=True  ,UpperCAmelCase_=None  ,UpperCAmelCase_=True  ,UpperCAmelCase_=1 / 255  ,UpperCAmelCase_=True  ,UpperCAmelCase_=[0.5, 0.5, 0.5]  ,UpperCAmelCase_=[0.5, 0.5, 0.5]  ,UpperCAmelCase_=True  ,)	->				Any:
           lowercase__									=    size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
           lowercase__									=    parent
           lowercase__									=    batch_size
           lowercase__									=    num_channels
           lowercase__									=    min_resolution
           lowercase__									=    max_resolution
           lowercase__									=    do_resize
           lowercase__									=    size
           lowercase__									=    do_rescale
           lowercase__									=    rescale_factor
           lowercase__									=    do_normalize
           lowercase__									=    image_mean
           lowercase__									=    image_std
           lowercase__									=    do_pad



       def 				_a   (					self   )	->				Union[str, Any]:
           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  ,UpperCAmelCase_  ,UpperCAmelCase_=False   )	->				Optional[int]:



           if not batched:
               lowercase__									=    image_inputs[0]
               if isinstance(lowercase__  ,Image.Image   ):
                   lowercase__							,							lowercase__									=    image.size
               else:
                   lowercase__							,							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__							,							lowercase__									=    self.get_expected_values([image]   )
                   expected_values.append((expected_height, expected_width)   )
               lowercase__									=    max(lowercase__  ,key=lambda UpperCAmelCase_   : item[0]   )[0]
               lowercase__									=    max(lowercase__  ,key=lambda UpperCAmelCase_   : item[1]   )[1]

           return expected_height, expected_width





@require_torch
@require_vision
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:int     =       DetrImageProcessor if is_vision_available() else None



       def 				_a   (					self   )	->				List[str]:
           lowercase__									=    DetrImageProcessingTester(self   )



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



       def 				_a   (					self   )	->				Tuple:
           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_rescale"   )   )
           self.assertTrue(hasattr(lowercase__  ,"rescale_factor"   )   )
           self.assertTrue(hasattr(lowercase__  ,"do_resize"   )   )
           self.assertTrue(hasattr(lowercase__  ,"size"   )   )
           self.assertTrue(hasattr(lowercase__  ,"do_pad"   )   )



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    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  ,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   )	->				List[Any]:
           pass



       def 				_a   (					self   )	->				Union[str, Any]:
           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__							,							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__							,							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   )	->				List[str]:
           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__							,							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__							,							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   )	->				Tuple:
           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__							,							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__							,							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   )	->				List[Any]:
           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": 39_769, "annotations": target}

           # encode them
           lowercase__									=    DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"   )
           lowercase__									=    image_processing(images=lowercase__  ,annotations=lowercase__  ,return_tensors="pt"   )

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

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

           # verify area
           lowercase__									=    torch.tensor([58_87.96_00, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
           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([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,lowercase__  ,atol=1E-3   )   )
           # verify image_id
           lowercase__									=    torch.tensor([39_769]   )
           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, 1_066]   )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,lowercase__   )   )







       @slow
       def 				_a   (					self   )	->				Optional[Any]:
           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": 39_769, "segments_info": target}

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

           # encode them
           lowercase__									=    DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"   )
           lowercase__									=    image_processing(images=lowercase__  ,annotations=lowercase__  ,masks_path=lowercase__  ,return_tensors="pt"   )

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

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

           # verify area
           lowercase__									=    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 58_79.65_62, 76_34.11_47]   )
           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([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,lowercase__  ,atol=1E-3   )   )
           # verify image_id
           lowercase__									=    torch.tensor([39_769]   )
           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__									=    822_873
           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, 1_066]   )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,lowercase__   )   )







import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


