"""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 DetaImageProcessor
class 			UpperCamelCase__(	unittest.TestCase       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=7						,__UpperCAmelCase=3						,__UpperCAmelCase=30						,__UpperCAmelCase=4_00						,__UpperCAmelCase=True						,__UpperCAmelCase=None						,__UpperCAmelCase=True						,__UpperCAmelCase=[0.5, 0.5, 0.5]						,__UpperCAmelCase=[0.5, 0.5, 0.5]						,__UpperCAmelCase=True						,__UpperCAmelCase=1 / 2_55						,__UpperCAmelCase=True						,)    -> Dict:
													# 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  snake_case__		(				self   )    -> Optional[int]:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> Optional[Any]:
													if not batched:
																				A__		   =							image_inputs[0]
																				if isinstance(lowercase_						,Image.Image   ):
																											A__		   =							image.size
																				else:
																											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__		   =							self.get_expected_values([image]   )
																											expected_values.append((expected_height, expected_width)   )
																				A__		   =							max(lowercase_						,key=lambda __UpperCAmelCase   : item[0]   )[0]
																				A__		   =							max(lowercase_						,key=lambda __UpperCAmelCase   : item[1]   )[1]

													return expected_height, expected_width






@require_torch
@require_vision
class 			UpperCamelCase__(	a_						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Tuple  							=						DetaImageProcessor if is_vision_available() else None





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							DetaImageProcessingTester(self   )





						@property
						def  snake_case__		(				self   )    -> int:
													return self.image_processor_tester.prepare_image_processor_dict()





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							self.image_processing_class(**self.image_processor_dict   )
													self.assertTrue(hasattr(lowercase_						,'image_mean'   )   )
													self.assertTrue(hasattr(lowercase_						,'image_std'   )   )
													self.assertTrue(hasattr(lowercase_						,'do_normalize'   )   )
													self.assertTrue(hasattr(lowercase_						,'do_resize'   )   )
													self.assertTrue(hasattr(lowercase_						,'do_rescale'   )   )
													self.assertTrue(hasattr(lowercase_						,'do_pad'   )   )
													self.assertTrue(hasattr(lowercase_						,'size'   )   )





						def  snake_case__		(				self   )    -> Tuple:
													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						,lowercase_   )





						def  snake_case__		(				self   )    -> Any:
													pass





						def  snake_case__		(				self   )    -> 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=lowercase_   )
													for image in image_inputs:
																				self.assertIsInstance(lowercase_						,Image.Image   )

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

													A__		   =							self.image_processor_tester.get_expected_values(lowercase_   )

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

													# Test batched
													A__		   =							self.image_processor_tester.get_expected_values(lowercase_						,batched=lowercase_   )

													A__		   =							image_processing(lowercase_						,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  snake_case__		(				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=lowercase_						,numpify=lowercase_   )
													for image in image_inputs:
																				self.assertIsInstance(lowercase_						,np.ndarray   )

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

													A__		   =							self.image_processor_tester.get_expected_values(lowercase_   )

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

													# Test batched
													A__		   =							image_processing(lowercase_						,return_tensors='pt'   ).pixel_values

													A__		   =							self.image_processor_tester.get_expected_values(lowercase_						,batched=lowercase_   )

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





						def  snake_case__		(				self   )    -> 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=lowercase_						,torchify=lowercase_   )
													for image in image_inputs:
																				self.assertIsInstance(lowercase_						,torch.Tensor   )

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

													A__		   =							self.image_processor_tester.get_expected_values(lowercase_   )

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

													# Test batched
													A__		   =							image_processing(lowercase_						,return_tensors='pt'   ).pixel_values

													A__		   =							self.image_processor_tester.get_expected_values(lowercase_						,batched=lowercase_   )

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





						@slow
						def  snake_case__		(				self   )    -> 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__		   =							DetaImageProcessor()
													A__		   =							image_processing(images=lowercase_						,annotations=lowercase_						,return_tensors='pt'   )

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

													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]						,lowercase_						,atol=1e-4   )   )

													# verify area
													A__		   =							torch.tensor([5_8_8_7.9_6_0_0, 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']						,lowercase_   )   )
													# verify boxes
													A__		   =							torch.Size([6, 4]   )
													self.assertEqual(encoding['labels'][0]['boxes'].shape						,lowercase_   )
													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]						,lowercase_						,atol=1e-3   )   )
													# verify image_id
													A__		   =							torch.tensor([3_97_69]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,lowercase_   )   )
													# verify is_crowd
													A__		   =							torch.tensor([0, 0, 0, 0, 0, 0]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,lowercase_   )   )
													# verify class_labels
													A__		   =							torch.tensor([75, 75, 63, 65, 17, 17]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,lowercase_   )   )
													# verify orig_size
													A__		   =							torch.tensor([4_80, 6_40]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,lowercase_   )   )
													# verify size
													A__		   =							torch.tensor([8_00, 10_66]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,lowercase_   )   )





						@slow
						def  snake_case__		(				self   )    -> 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__		   =							DetaImageProcessor(format='coco_panoptic'   )
													A__		   =							image_processing(images=lowercase_						,annotations=lowercase_						,masks_path=lowercase_						,return_tensors='pt'   )

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

													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]						,lowercase_						,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, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,lowercase_   )   )
													# verify boxes
													A__		   =							torch.Size([6, 4]   )
													self.assertEqual(encoding['labels'][0]['boxes'].shape						,lowercase_   )
													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]						,lowercase_						,atol=1e-3   )   )
													# verify image_id
													A__		   =							torch.tensor([3_97_69]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,lowercase_   )   )
													# verify is_crowd
													A__		   =							torch.tensor([0, 0, 0, 0, 0, 0]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,lowercase_   )   )
													# verify class_labels
													A__		   =							torch.tensor([17, 17, 63, 75, 75, 93]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,lowercase_   )   )
													# verify masks
													A__		   =							82_28_73
													self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,lowercase_   )
													# verify orig_size
													A__		   =							torch.tensor([4_80, 6_40]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,lowercase_   )   )
													# verify size
													A__		   =							torch.tensor([8_00, 10_66]   )
													self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,lowercase_   )   )








"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




