





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 DetaImageProcessor


class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def __init__(    self			:		List[Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Optional[int]=7 ,				lowerCamelCase_			:		Optional[int]=3 ,				lowerCamelCase_			:		Union[str, Any]=30 ,				lowerCamelCase_			:		str=4_00 ,				lowerCamelCase_			:		Tuple=True ,				lowerCamelCase_			:		str=None ,				lowerCamelCase_			:		Dict=True ,				lowerCamelCase_			:		List[str]=[0.5, 0.5, 0.5] ,				lowerCamelCase_			:		Optional[Any]=[0.5, 0.5, 0.5] ,				lowerCamelCase_			:		str=True ,				lowerCamelCase_			:		List[str]=1 / 2_55 ,				lowerCamelCase_			:		List[Any]=True ,				):





										'''simple docstring'''







										_snake_case :			Tuple  						=   size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
										_snake_case :			Union[str, Any]  						=   parent
										_snake_case :			Optional[Any]  						=   batch_size
										_snake_case :			Any  						=   num_channels
										_snake_case :			int  						=   min_resolution
										_snake_case :			Dict  						=   max_resolution
										_snake_case :			List[str]  						=   do_resize
										_snake_case :			str  						=   size
										_snake_case :			Optional[Any]  						=   do_normalize
										_snake_case :			Dict  						=   image_mean
										_snake_case :			Optional[Any]  						=   image_std
										_snake_case :			Union[str, Any]  						=   do_rescale
										_snake_case :			Union[str, Any]  						=   rescale_factor
										_snake_case :			List[str]  						=   do_pad







						def 		__UpperCAmelCase       (    self			:		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 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Tuple=False					):





										'''simple docstring'''







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

										else:
														_snake_case :			List[str]  						=   []
														for image in image_inputs:
																		_snake_case						,			_snake_case :			Tuple  						=   self.get_expected_values([image]					)
																		expected_values.append((expected_height, expected_width)					)
														_snake_case :			Dict  						=   max(lowerCamelCase_ ,				key=lambda lowerCamelCase_					: item[0]					)[0]
														_snake_case :			Optional[Any]  						=   max(lowerCamelCase_ ,				key=lambda lowerCamelCase_					: item[1]					)[1]

										return expected_height, expected_width



@require_torch
@require_vision
class 			lowercase				(							a_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Dict								=	DetaImageProcessor if is_vision_available() else None







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   DetaImageProcessingTester(self					)







						@property
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										return self.image_processor_tester.prepare_image_processor_dict()







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(lowerCamelCase_ ,				'image_mean'					)					)
										self.assertTrue(hasattr(lowerCamelCase_ ,				'image_std'					)					)
										self.assertTrue(hasattr(lowerCamelCase_ ,				'do_normalize'					)					)
										self.assertTrue(hasattr(lowerCamelCase_ ,				'do_resize'					)					)
										self.assertTrue(hasattr(lowerCamelCase_ ,				'do_rescale'					)					)
										self.assertTrue(hasattr(lowerCamelCase_ ,				'do_pad'					)					)
										self.assertTrue(hasattr(lowerCamelCase_ ,				'size'					)					)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Dict  						=   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 ,				lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										pass







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										_snake_case :			Tuple  						=   prepare_image_inputs(self.image_processor_tester ,				equal_resolution=lowerCamelCase_					)
										for image in image_inputs:
														self.assertIsInstance(lowerCamelCase_ ,				Image.Image					)

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

										_snake_case						,			_snake_case :			Optional[Any]  						=   self.image_processor_tester.get_expected_values(lowerCamelCase_					)

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

										# Test batched
										_snake_case						,			_snake_case :			List[str]  						=   self.image_processor_tester.get_expected_values(lowerCamelCase_ ,				batched=lowerCamelCase_					)

										_snake_case :			List[str]  						=   image_processing(lowerCamelCase_ ,				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			:		List[Any]					):





										'''simple docstring'''







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

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

										_snake_case						,			_snake_case :			Dict  						=   self.image_processor_tester.get_expected_values(lowerCamelCase_					)

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

										# Test batched
										_snake_case :			Optional[int]  						=   image_processing(lowerCamelCase_ ,				return_tensors='pt'					).pixel_values

										_snake_case						,			_snake_case :			Any  						=   self.image_processor_tester.get_expected_values(lowerCamelCase_ ,				batched=lowerCamelCase_					)

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







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										_snake_case :			int  						=   prepare_image_inputs(self.image_processor_tester ,				equal_resolution=lowerCamelCase_ ,				torchify=lowerCamelCase_					)
										for image in image_inputs:
														self.assertIsInstance(lowerCamelCase_ ,				torch.Tensor					)

										# Test not batched input
										_snake_case :			List[Any]  						=   image_processing(image_inputs[0] ,				return_tensors='pt'					).pixel_values

										_snake_case						,			_snake_case :			str  						=   self.image_processor_tester.get_expected_values(lowerCamelCase_					)

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

										# Test batched
										_snake_case :			Optional[int]  						=   image_processing(lowerCamelCase_ ,				return_tensors='pt'					).pixel_values

										_snake_case						,			_snake_case :			int  						=   self.image_processor_tester.get_expected_values(lowerCamelCase_ ,				batched=lowerCamelCase_					)

										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			:		str					):





										'''simple docstring'''







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

										_snake_case :			List[str]  						=   {'image_id': 3_97_69, 'annotations': target}

										# encode them
										_snake_case :			Union[str, Any]  						=   DetaImageProcessor()
										_snake_case :			Optional[Any]  						=   image_processing(images=lowerCamelCase_ ,				annotations=lowerCamelCase_ ,				return_tensors='pt'					)

										# verify pixel values
										_snake_case :			int  						=   torch.Size([1, 3, 8_00, 10_66]					)
										self.assertEqual(encoding['pixel_values'].shape ,				lowerCamelCase_					)

										_snake_case :			Tuple  						=   torch.tensor([0.2796, 0.3138, 0.3481]					)
										self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,				lowerCamelCase_ ,				atol=1e-4					)					)

										# verify area
										_snake_case :			Tuple  						=   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,				lowerCamelCase_					)					)
										# verify boxes
										_snake_case :			int  						=   torch.Size([6, 4]					)
										self.assertEqual(encoding['labels'][0]['boxes'].shape ,				lowerCamelCase_					)
										_snake_case :			str  						=   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,				lowerCamelCase_ ,				atol=1e-3					)					)
										# verify image_id
										_snake_case :			Any  						=   torch.tensor([3_97_69]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				lowerCamelCase_					)					)
										# verify is_crowd
										_snake_case :			Optional[int]  						=   torch.tensor([0, 0, 0, 0, 0, 0]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				lowerCamelCase_					)					)
										# verify class_labels
										_snake_case :			Union[str, Any]  						=   torch.tensor([75, 75, 63, 65, 17, 17]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				lowerCamelCase_					)					)
										# verify orig_size
										_snake_case :			Optional[Any]  						=   torch.tensor([4_80, 6_40]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				lowerCamelCase_					)					)
										# verify size
										_snake_case :			List[Any]  						=   torch.tensor([8_00, 10_66]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,				lowerCamelCase_					)					)







						@slow
						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			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 :			Union[str, Any]  						=   json.loads(f.read()					)

										_snake_case :			int  						=   {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

										_snake_case :			List[str]  						=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'					)

										# encode them
										_snake_case :			Optional[int]  						=   DetaImageProcessor(format='coco_panoptic'					)
										_snake_case :			Any  						=   image_processing(images=lowerCamelCase_ ,				annotations=lowerCamelCase_ ,				masks_path=lowerCamelCase_ ,				return_tensors='pt'					)

										# verify pixel values
										_snake_case :			str  						=   torch.Size([1, 3, 8_00, 10_66]					)
										self.assertEqual(encoding['pixel_values'].shape ,				lowerCamelCase_					)

										_snake_case :			int  						=   torch.tensor([0.2796, 0.3138, 0.3481]					)
										self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,				lowerCamelCase_ ,				atol=1e-4					)					)

										# verify area
										_snake_case :			Dict  						=   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,				lowerCamelCase_					)					)
										# verify boxes
										_snake_case :			Union[str, Any]  						=   torch.Size([6, 4]					)
										self.assertEqual(encoding['labels'][0]['boxes'].shape ,				lowerCamelCase_					)
										_snake_case :			str  						=   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,				lowerCamelCase_ ,				atol=1e-3					)					)
										# verify image_id
										_snake_case :			List[str]  						=   torch.tensor([3_97_69]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				lowerCamelCase_					)					)
										# verify is_crowd
										_snake_case :			Union[str, Any]  						=   torch.tensor([0, 0, 0, 0, 0, 0]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				lowerCamelCase_					)					)
										# verify class_labels
										_snake_case :			Tuple  						=   torch.tensor([17, 17, 63, 75, 75, 93]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				lowerCamelCase_					)					)
										# verify masks
										_snake_case :			Any  						=   82_28_73
										self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,				lowerCamelCase_					)
										# verify orig_size
										_snake_case :			Dict  						=   torch.tensor([4_80, 6_40]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				lowerCamelCase_					)					)
										# verify size
										_snake_case :			Dict  						=   torch.tensor([8_00, 10_66]					)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,				lowerCamelCase_					)					)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:				Optional[Any]				=						{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Tuple				=						[
		    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoCBertForCausalLM''',
		    '''RoCBertForMaskedLM''',
		    '''RoCBertForMultipleChoice''',
		    '''RoCBertForPreTraining''',
		    '''RoCBertForQuestionAnswering''',
		    '''RoCBertForSequenceClassification''',
		    '''RoCBertForTokenClassification''',
		    '''RoCBertLayer''',
		    '''RoCBertModel''',
		    '''RoCBertPreTrainedModel''',
		    '''load_tf_weights_in_roc_bert''',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		lowercase_		:				Tuple				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






