



"""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 DeformableDetrImageProcessor






class      UpperCAmelCase__		(      unittest.TestCase     ):



		def __init__( self  :    Union[str, Any]      ,							snake_case  :    Dict      ,							snake_case  :    Tuple=7      ,							snake_case  :    Optional[Any]=3      ,							snake_case  :    List[str]=30      ,							snake_case  :    List[str]=400      ,							snake_case  :    List[str]=True      ,							snake_case  :    Any=None      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Any=[0.5, 0.5, 0.5]      ,							snake_case  :    int=[0.5, 0.5, 0.5]      ,							snake_case  :    Dict=True      ,							snake_case  :    Optional[Any]=1 / 255      ,							snake_case  :    Optional[int]=True      ,							)			->						Optional[Any]:






									'''simple docstring'''

									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_normalize
									A         =					image_mean
									A         =					image_std
									A         =					do_rescale
									A         =					rescale_factor
									A         =					do_pad



		def 					A_		( self  :    str				)			->						Any:






									'''simple docstring'''

									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 					A_		( self  :    List[str]      ,							snake_case  :    Tuple      ,							snake_case  :    Optional[Any]=False				)			->						Optional[Any]:






									'''simple docstring'''

									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      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	List[Any] 							=     DeformableDetrImageProcessor if is_vision_available() else None



		def 					A_		( self  :    Tuple				)			->						str:






									'''simple docstring'''

									A         =					DeformableDetrImageProcessingTester(self				)



		@property
		def 					A_		( self  :    str				)			->						int:






									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()



		def 					A_		( self  :    str				)			->						Optional[int]:






									'''simple docstring'''

									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 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									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      ,							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 					A_		( self  :    List[Any]				)			->						Any:






									'''simple docstring'''

									pass



		def 					A_		( self  :    Any				)			->						Any:






									'''simple docstring'''

									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 					A_		( self  :    str				)			->						int:






									'''simple docstring'''

									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 					A_		( self  :    Any				)			->						Optional[int]:






									'''simple docstring'''

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






									'''simple docstring'''

									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         =					DeformableDetrImageProcessor()
									A         =					image_processing(images=snake_case      ,							annotations=snake_case      ,							return_tensors='pt'				)

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

									A         =					torch.tensor([0.2796, 0.3138, 0.3481]				)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,							snake_case      ,							atol=1E-4				)				)

									# verify area
									A         =					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]				)
									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([0.5503, 0.2765, 0.0604, 0.2215]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,							snake_case      ,							atol=1E-3				)				)
									# verify image_id
									A         =					torch.tensor([39_769]				)
									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([480, 640]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,							snake_case				)				)
									# verify size
									A         =					torch.tensor([800, 1_066]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,							snake_case				)				)



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






									'''simple docstring'''

									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         =					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, 800, 1_066]				)
									self.assertEqual(encoding['pixel_values'].shape      ,							snake_case				)

									A         =					torch.tensor([0.2796, 0.3138, 0.3481]				)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,							snake_case      ,							atol=1E-4				)				)

									# verify area
									A         =					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]				)
									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([0.2625, 0.5437, 0.4688, 0.8625]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,							snake_case      ,							atol=1E-3				)				)
									# verify image_id
									A         =					torch.tensor([39_769]				)
									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         =					822_873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,							snake_case				)
									# verify orig_size
									A         =					torch.tensor([480, 640]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,							snake_case				)				)
									# verify size
									A         =					torch.tensor([800, 1_066]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,							snake_case				)				)








"""simple docstring"""







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Tuple 							=     LayoutLMTokenizer
		lowerCAmelCase_       :	Any 							=     LayoutLMTokenizerFast
		lowerCAmelCase_       :	Optional[int] 							=     True
		lowerCAmelCase_       :	List[Any] 							=     True



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






									'''simple docstring'''

									super().setUp()

									A         =					[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									A         =					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['vocab_file']				)
									with open(self.vocab_file      ,							'w'      ,							encoding='utf-8'				) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)



		def 					A_		( self  :    str      ,							**snake_case  :    Tuple				)			->						Union[str, Any]:






									'''simple docstring'''

									return LayoutLMTokenizer.from_pretrained(self.tmpdirname      ,							**snake_case				)



		def 					A_		( self  :    List[str]      ,							snake_case  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					'UNwant\u00E9d,running'
									A         =					'unwanted, running'
									return input_text, output_text



		def 					A_		( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.tokenizer_class(self.vocab_file				)

									A         =					tokenizer.tokenize('UNwant\u00E9d,running'				)
									self.assertListEqual(snake_case      ,							['un', '##want', '##ed', ',', 'runn', '##ing']				)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case				)      ,							[7, 4, 5, 10, 8, 9]				)



		def 					A_		( self  :    Any				)			->						List[Any]:






									'''simple docstring'''

									pass



