


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  snake_case_     (unittest.TestCase   ):
					def __init__(   self     :Optional[int]    ,__snake_case     :Optional[int]    ,__snake_case     :Optional[Any]=7    ,__snake_case     :Any=3    ,__snake_case     :int=30    ,__snake_case     :Union[str, Any]=4_00    ,__snake_case     :str=True    ,__snake_case     :List[str]=None    ,__snake_case     :List[Any]=True    ,__snake_case     :Dict=[0.5, 0.5, 0.5]    ,__snake_case     :Optional[Any]=[0.5, 0.5, 0.5]    ,__snake_case     :int=True    ,__snake_case     :int=1 / 2_55    ,__snake_case     :List[str]=True    ,)      ->					List[Any]:
												# 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 				lowerCamelCase__(   self     :str   )      ->					Tuple:
												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 				lowerCamelCase__(   self     :Dict    ,__snake_case     :Optional[Any]    ,__snake_case     :Tuple=False   )      ->					Tuple:
												if not batched:
																			a__    =  image_inputs[0]
																			if isinstance(__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(__snake_case    ,key=lambda __snake_case   : item[0]   )[0]
																			a__    =  max(__snake_case    ,key=lambda __snake_case   : item[1]   )[1]

												return expected_height, expected_width







@require_torch
@require_vision
class  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Any					   =    DeformableDetrImageProcessor if is_vision_available() else None
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Dict:
												a__    =  DeformableDetrImageProcessingTester(self   )
					@property
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Optional[int]:
												return self.image_processor_tester.prepare_image_processor_dict()
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Dict:
												a__    =  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 				lowerCamelCase__(   self     :int   )      ->					List[str]:
												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    ,__snake_case   )

												a__    =  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 				lowerCamelCase__(   self     :str   )      ->					List[Any]:
												pass
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Dict:
												# 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=__snake_case   )
												for image in image_inputs:
																			self.assertIsInstance(__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(__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(__snake_case    ,batched=__snake_case   )

												a__    =  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 				lowerCamelCase__(   self     :int   )      ->					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=__snake_case    ,numpify=__snake_case   )
												for image in image_inputs:
																			self.assertIsInstance(__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(__snake_case   )

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

												# Test batched
												a__    =  image_processing(__snake_case    ,return_tensors='pt'   ).pixel_values

												a__   ,      a__    =  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 				lowerCamelCase__(   self     :Optional[Any]   )      ->					str:
												# 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=__snake_case    ,torchify=__snake_case   )
												for image in image_inputs:
																			self.assertIsInstance(__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(__snake_case   )

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

												# Test batched
												a__    =  image_processing(__snake_case    ,return_tensors='pt'   ).pixel_values

												a__   ,      a__    =  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 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[Any]:
												# 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=__snake_case    ,annotations=__snake_case    ,return_tensors='pt'   )

												# verify pixel values
												a__    =  torch.Size([1, 3, 8_00, 10_66]   )
												self.assertEqual(encoding['pixel_values'].shape    ,__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]    ,__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']    ,__snake_case   )   )
												# verify boxes
												a__    =  torch.Size([6, 4]   )
												self.assertEqual(encoding['labels'][0]['boxes'].shape    ,__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]    ,__snake_case    ,atol=1E-3   )   )
												# verify image_id
												a__    =  torch.tensor([3_97_69]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,__snake_case   )   )
												# verify is_crowd
												a__    =  torch.tensor([0, 0, 0, 0, 0, 0]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,__snake_case   )   )
												# verify class_labels
												a__    =  torch.tensor([75, 75, 63, 65, 17, 17]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,__snake_case   )   )
												# verify orig_size
												a__    =  torch.tensor([4_80, 6_40]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,__snake_case   )   )
												# verify size
												a__    =  torch.tensor([8_00, 10_66]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,__snake_case   )   )



					@slow
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												# 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=__snake_case    ,annotations=__snake_case    ,masks_path=__snake_case    ,return_tensors='pt'   )

												# verify pixel values
												a__    =  torch.Size([1, 3, 8_00, 10_66]   )
												self.assertEqual(encoding['pixel_values'].shape    ,__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]    ,__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']    ,__snake_case   )   )
												# verify boxes
												a__    =  torch.Size([6, 4]   )
												self.assertEqual(encoding['labels'][0]['boxes'].shape    ,__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]    ,__snake_case    ,atol=1E-3   )   )
												# verify image_id
												a__    =  torch.tensor([3_97_69]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,__snake_case   )   )
												# verify is_crowd
												a__    =  torch.tensor([0, 0, 0, 0, 0, 0]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,__snake_case   )   )
												# verify class_labels
												a__    =  torch.tensor([17, 17, 63, 75, 75, 93]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,__snake_case   )   )
												# verify masks
												a__    =  82_28_73
												self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,__snake_case   )
												# verify orig_size
												a__    =  torch.tensor([4_80, 6_40]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,__snake_case   )   )
												# verify size
												a__    =  torch.tensor([8_00, 10_66]   )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,__snake_case   )   )








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

												# All transformations expect numpy arrays.
												a__    =  [to_numpy_array(__snake_case   ) for img in images]

												if do_resize:
																			a__    =  [self.resize(__snake_case    ,size_divisor=__snake_case    ,resample=__snake_case   ) for image in images]

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




