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

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

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

   return expected_height, expected_width
@require_torch
@require_vision
class    A_ (       a_			,       unittest.TestCase			):
  _SCREAMING_SNAKE_CASE    				=			DeformableDetrImageProcessor if is_vision_available() else None

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  DeformableDetrImageProcessingTester(self       )

  @property
  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   return self.image_processor_tester.prepare_image_processor_dict()

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_mean"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_std"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_normalize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_resize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_rescale"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_pad"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"size"       )       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  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     ,							__SCREAMING_SNAKE_CASE       )

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

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      Any       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							Image.Image       )

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

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

   # Test batched
   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,							batched=__SCREAMING_SNAKE_CASE       )

   __a       =  image_processing(__SCREAMING_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 		_UpperCAmelCase   (		self :      str       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							numpify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							np.ndarray       )

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

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

   # Test batched
   __a       =  image_processing(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).pixel_values

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,							batched=__SCREAMING_SNAKE_CASE       )

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

  def 		_UpperCAmelCase   (		self :      int       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							torchify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							torch.Tensor       )

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

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

   # Test batched
   __a       =  image_processing(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).pixel_values

   __a       ,     __a       =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,							batched=__SCREAMING_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 		_UpperCAmelCase   (		self :      List[str]       ):
   # prepare image and target
   __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
   with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,							"r"       ) as f:
    __a       =  json.loads(f.read()       )

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

   # encode them
   __a       =  DeformableDetrImageProcessor()
   __a       =  image_processing(images=__SCREAMING_SNAKE_CASE     ,							annotations=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )

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

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

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

  @slow
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   # prepare image, target and masks_path
   __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
   with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,							"r"       ) as f:
    __a       =  json.loads(f.read()       )

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

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

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

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

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

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



import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
  import ctypes
  import msvcrt  # noqa
  class    A_ (       ctypes.Structure ):
    # _fields is a specific attr expected by ctypes
    _SCREAMING_SNAKE_CASE    				=			[("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]



def   __A		(			):



 """simple docstring"""


 if os.name == "nt":
  __a       =  CursorInfo()
  __a       =  ctypes.windll.kernelaa.GetStdHandle(-11					)
  ctypes.windll.kernelaa.GetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
  __a       =  False
  ctypes.windll.kernelaa.SetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
 elif os.name == "posix":
  sys.stdout.write("\033[?25l"					)
  sys.stdout.flush()



def   __A		(			):



 """simple docstring"""


 if os.name == "nt":
  __a       =  CursorInfo()
  __a       =  ctypes.windll.kernelaa.GetStdHandle(-11					)
  ctypes.windll.kernelaa.GetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
  __a       =  True
  ctypes.windll.kernelaa.SetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
 elif os.name == "posix":
  sys.stdout.write("\033[?25h"					)
  sys.stdout.flush()






@contextmanager
def   __A		(			):



 """simple docstring"""


 try:
  hide_cursor()
  yield
 finally:
  show_cursor()


