


"""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 _lowerCAmelCase				(      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						,	)	->		str:

       '''simple docstring'''




       snake_case    :      int      					=    size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
       snake_case    :      Optional[int]      					=    parent
       snake_case    :      int      					=    batch_size
       snake_case    :      Union[str, Any]      					=    num_channels
       snake_case    :      str      					=    min_resolution
       snake_case    :      str      					=    max_resolution
       snake_case    :      Tuple      					=    do_resize
       snake_case    :      Optional[int]      					=    size
       snake_case    :      Any      					=    do_rescale
       snake_case    :      List[str]      					=    rescale_factor
       snake_case    :      Tuple      					=    do_normalize
       snake_case    :      Any      					=    image_mean
       snake_case    :      str      					=    image_std
       snake_case    :      str      					=    do_pad







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=False						)	->		Any:

       '''simple docstring'''




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

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

       return expected_height, expected_width







@require_torch
@require_vision
class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       str							  =  DetrImageProcessor if is_vision_available() else None







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Any      					=    DetrImageProcessingTester(self						)







   @property
   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      str      					=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"image_mean"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"image_std"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"do_normalize"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"do_rescale"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"rescale_factor"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"do_resize"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"size"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,	"do_pad"						)						)







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      int      					=    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						,	UpperCamelCase__						)

       snake_case    :      List[str]      					=    self.image_processing_class.from_dict(
           self.image_processor_dict						,	size=42						,	max_size=84						,	pad_and_return_pixel_mask=UpperCamelCase__						)
       self.assertEqual(image_processor.size						,	{"shortest_edge": 42, "longest_edge": 84}						)
       self.assertEqual(image_processor.do_pad						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       pass







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




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

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

       snake_case					,snake_case    :      str      					=    self.image_processor_tester.get_expected_values(UpperCamelCase__						)

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

       # Test batched
       snake_case					,snake_case    :      str      					=    self.image_processor_tester.get_expected_values(UpperCamelCase__						,	batched=UpperCamelCase__						)

       snake_case    :      Optional[int]      					=    image_processing(UpperCamelCase__						,	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  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




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

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

       snake_case					,snake_case    :      List[str]      					=    self.image_processor_tester.get_expected_values(UpperCamelCase__						)

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

       # Test batched
       snake_case    :      Tuple      					=    image_processing(UpperCamelCase__						,	return_tensors="pt"						).pixel_values

       snake_case					,snake_case    :      Any      					=    self.image_processor_tester.get_expected_values(UpperCamelCase__						,	batched=UpperCamelCase__						)

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







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




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

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

       snake_case					,snake_case    :      Dict      					=    self.image_processor_tester.get_expected_values(UpperCamelCase__						)

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

       # Test batched
       snake_case    :      Dict      					=    image_processing(UpperCamelCase__						,	return_tensors="pt"						).pixel_values

       snake_case					,snake_case    :      List[Any]      					=    self.image_processor_tester.get_expected_values(UpperCamelCase__						,	batched=UpperCamelCase__						)

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







   @slow
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




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

       snake_case    :      List[Any]      					=    {"image_id": 3_9769, "annotations": target}

       # encode them
       snake_case    :      Optional[int]      					=    DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"						)
       snake_case    :      Union[str, Any]      					=    image_processing(images=UpperCamelCase__						,	annotations=UpperCamelCase__						,	return_tensors="pt"						)

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

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

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







   @slow
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




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

       snake_case    :      Union[str, Any]      					=    {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

       snake_case    :      str      					=    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

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

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

       snake_case    :      Optional[Any]      					=    torch.tensor([0.2796, 0.3138, 0.3481]						)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,	UpperCamelCase__						,	atol=1e-4						)						)

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









"""simple docstring"""



def   __lowerCAmelCase						(						)	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      Dict      					=    []
    snake_case    :      List[Any]      					=    1

    while len(lowercase ) < 1e6:
        constant.append(str(lowercase ) )
        i += 1

    snake_case    :      Tuple      					=    "".join(lowercase )

    return (
        int(constant[0] )
        * int(constant[9] )
        * int(constant[99] )
        * int(constant[999] )
        * int(constant[9999] )
        * int(constant[9_9999] )
        * int(constant[99_9999] )
    )


if __name__ == "__main__":
  print(solution())





