



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      :  List[str]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  List[Any]=7						, __UpperCamelCase      :  Dict=3						, __UpperCamelCase      :  str=30						, __UpperCamelCase      :  int=400						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  Optional[Any]=1 / 255						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  Any=[0.5, 0.5, 0.5]						, __UpperCamelCase      :  Tuple=[0.5, 0.5, 0.5]						, __UpperCamelCase      :  Tuple=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': 1_333}
											A      						=    parent
											A      						=    batch_size
											A      						=    num_channels
											A      						=    min_resolution
											A      						=    max_resolution
											A      						=    do_resize
											A      						=    size
											A      						=    do_rescale
											A      						=    rescale_factor
											A      						=    do_normalize
											A      						=    image_mean
											A      						=    image_std
											A      						=    do_pad


				def __UpperCamelCase						(							self      :  List[str]       )		->     str:
											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 __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  Dict=False       )		->     Union[str, Any]:
											if not batched:
																		A      						=    image_inputs[0]
																		if isinstance(__UpperCamelCase						, 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(__UpperCamelCase						, key=lambda __UpperCamelCase       : item[0]       )[0]
																		A      						=    max(__UpperCamelCase						, key=lambda __UpperCamelCase       : item[1]       )[1]

											return expected_height, expected_width





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


				def __UpperCamelCase						(							self      :  int       )		->     Any:
											A      						=    DetrImageProcessingTester(self       )


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


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    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 __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											A      						=    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						, __UpperCamelCase       )

											A      						=    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 __UpperCamelCase						(							self      :  List[str]       )		->     Optional[Any]:
											pass


				def __UpperCamelCase						(							self      :  List[Any]       )		->     str:
											# 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=__UpperCamelCase       )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCamelCase						, 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(__UpperCamelCase       )

											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(__UpperCamelCase						, batched=__UpperCamelCase       )

											A      						=    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 __UpperCamelCase						(							self      :  Tuple       )		->     Optional[int]:
											# 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=__UpperCamelCase						, numpify=__UpperCamelCase       )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCamelCase						, 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(__UpperCamelCase       )

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

											# Test batched
											A      						=    image_processing(__UpperCamelCase						, return_tensors='pt'       ).pixel_values

											A						,   A      						=    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 __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											# 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=__UpperCamelCase						, torchify=__UpperCamelCase       )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCamelCase						, 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(__UpperCamelCase       )

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

											# Test batched
											A      						=    image_processing(__UpperCamelCase						, return_tensors='pt'       ).pixel_values

											A						,   A      						=    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 __UpperCamelCase						(							self      :  List[str]       )		->     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': 39_769, 'annotations': target}

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

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

											A      						=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						, __UpperCamelCase						, atol=1e-4       )       )

											# verify area
											A      						=    torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']						, __UpperCamelCase       )       )
											# verify boxes
											A      						=    torch.Size([6, 4]       )
											self.assertEqual(encoding['labels'][0]['boxes'].shape						, __UpperCamelCase       )
											A      						=    torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						, __UpperCamelCase						, atol=1e-3       )       )
											# verify image_id
											A      						=    torch.tensor([39_769]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						, __UpperCamelCase       )       )
											# verify is_crowd
											A      						=    torch.tensor([0, 0, 0, 0, 0, 0]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						, __UpperCamelCase       )       )
											# verify class_labels
											A      						=    torch.tensor([75, 75, 63, 65, 17, 17]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						, __UpperCamelCase       )       )
											# verify orig_size
											A      						=    torch.tensor([480, 640]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						, __UpperCamelCase       )       )
											# verify size
											A      						=    torch.tensor([800, 1_066]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']						, __UpperCamelCase       )       )


				@slow
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											# 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': 39_769, 'segments_info': target}

											A      						=    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

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

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

											A      						=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						, __UpperCamelCase						, atol=1e-4       )       )

											# verify area
											A      						=    torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']						, __UpperCamelCase       )       )
											# verify boxes
											A      						=    torch.Size([6, 4]       )
											self.assertEqual(encoding['labels'][0]['boxes'].shape						, __UpperCamelCase       )
											A      						=    torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						, __UpperCamelCase						, atol=1e-3       )       )
											# verify image_id
											A      						=    torch.tensor([39_769]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						, __UpperCamelCase       )       )
											# verify is_crowd
											A      						=    torch.tensor([0, 0, 0, 0, 0, 0]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						, __UpperCamelCase       )       )
											# verify class_labels
											A      						=    torch.tensor([17, 17, 63, 75, 75, 93]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						, __UpperCamelCase       )       )
											# verify masks
											A      						=    822_873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()						, __UpperCamelCase       )
											# verify orig_size
											A      						=    torch.tensor([480, 640]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						, __UpperCamelCase       )       )
											# verify size
											A      						=    torch.tensor([800, 1_066]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']						, __UpperCamelCase       )       )




import argparse
from collections import defaultdict






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	List[str]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    F'''{file}_{class_name}_{test_name}'''
							done_test[_id] += 1

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    F'''class {class_name}('''
							A      						=    F'''{4 * " "}def {test_name}('''
							A      						=    F'''{8 * " "}{correct_line.split()[0]}'''
							A      						=    F'''{16 * " "}{correct_line.split()[0]}'''
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    0
							A      						=    0

							A      						=    []
							for line in lines:
														if line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and in_func and (line.startswith(lowerCAmelCase__				) or line.startswith(lowerCAmelCase__				)):
																					A      						=    len(line.split(correct_line.split()[0]				)[0]				)
																					count += 1

																					if count == done_test[_id]:
																												A      						=    True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												A      						=    True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(F'''{spaces * " "}{correct_line}'''				)
																					A      						=    A      						=    A      						=    A      						=    False
														else:
																					new_lines.append(lowerCAmelCase__				)

							with open(lowerCAmelCase__			,	'w'				) as f:
														for line in new_lines:
																					f.write(lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple=None				)       ->							Union[str, Any]:

							'''simple docstring'''






							if fail is not None:
														with open(lowerCAmelCase__			,	'r'				) as f:
																					A      						=    {l.strip() for l in f.readlines()}
							else:
														A      						=    None

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    defaultdict(lowerCAmelCase__				)
							for line in correct_lines:
														A						,   A						,   A						,   A      						=    line.split(';'				)
														if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
																					overwrite_file(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :List[Any]			       =argparse.ArgumentParser()
				parser.add_argument('--correct_filename', help='filename of tests with expected result')
				parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
				__snake_case     :Optional[int]			       =parser.parse_args()

				main(args.correct_filename, args.fail_filename)