



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[Any]			,		A       :  List[Any]			,		A       :  List[str]=7			,		A       :  Optional[Any]=3			,		A       :  Dict=30			,		A       :  Optional[int]=4_00			,		A       :  str=True			,		A       :  Optional[Any]=None			,		A       :  Any=True			,		A       :  Union[str, Any]=1 / 2_55			,		A       :  Tuple=True			,		A       :  Dict=[0.5, 0.5, 0.5]			,		A       :  str=[0.5, 0.5, 0.5]			,		A       :  Dict=True			,		)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       num_channels
						_UpperCAmelCase			=       min_resolution
						_UpperCAmelCase			=       max_resolution
						_UpperCAmelCase			=       do_resize
						_UpperCAmelCase			=       size
						_UpperCAmelCase			=       do_rescale
						_UpperCAmelCase			=       rescale_factor
						_UpperCAmelCase			=       do_normalize
						_UpperCAmelCase			=       image_mean
						_UpperCAmelCase			=       image_std
						_UpperCAmelCase			=       do_pad



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						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    _lowerCamelCase	(						self       :  Any			,		A       :  Optional[Any]			,		A       :  Any=False)       ->   Optional[int]:



						"""simple docstring"""






						if not batched:
									_UpperCAmelCase			=       image_inputs[0]
									if isinstance(A			,		Image.Image):
												_UpperCAmelCase	,					_UpperCAmelCase			=       image.size
									else:
												_UpperCAmelCase	,					_UpperCAmelCase			=       image.shape[1], image.shape[2]
									if w < h:
												_UpperCAmelCase			=       int(self.size['shortest_edge'] * h / w)
												_UpperCAmelCase			=       self.size['shortest_edge']
									elif w > h:
												_UpperCAmelCase			=       self.size['shortest_edge']
												_UpperCAmelCase			=       int(self.size['shortest_edge'] * w / h)
									else:
												_UpperCAmelCase			=       self.size['shortest_edge']
												_UpperCAmelCase			=       self.size['shortest_edge']

						else:
									_UpperCAmelCase			=       []
									for image in image_inputs:
												_UpperCAmelCase	,					_UpperCAmelCase			=       self.get_expected_values([image])
												expected_values.append((expected_height, expected_width))
									_UpperCAmelCase			=       max(A			,		key=lambda A: item[0])[0]
									_UpperCAmelCase			=       max(A			,		key=lambda A: item[1])[1]

						return expected_height, expected_width







@require_torch
@require_vision
class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					DetrImageProcessor if is_vision_available() else None



			def    _lowerCamelCase	(						self       :  Dict)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       DetrImageProcessingTester(self)



			@property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(A			,		'image_mean'))
						self.assertTrue(hasattr(A			,		'image_std'))
						self.assertTrue(hasattr(A			,		'do_normalize'))
						self.assertTrue(hasattr(A			,		'do_rescale'))
						self.assertTrue(hasattr(A			,		'rescale_factor'))
						self.assertTrue(hasattr(A			,		'do_resize'))
						self.assertTrue(hasattr(A			,		'size'))
						self.assertTrue(hasattr(A			,		'do_pad'))



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       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			,		A)

						_UpperCAmelCase			=       self.image_processing_class.from_dict(
						    self.image_processor_dict			,		size=42			,		max_size=84			,		pad_and_return_pixel_mask=A)
						self.assertEqual(image_processor.size			,		{'shortest_edge': 42, 'longest_edge': 84})
						self.assertEqual(image_processor.do_pad			,		A)



			def    _lowerCamelCase	(						self       :  List[str])       ->   Tuple:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						_UpperCAmelCase			=       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A)
						for image in image_inputs:
									self.assertIsInstance(A			,		Image.Image)

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

						_UpperCAmelCase	,					_UpperCAmelCase			=       self.image_processor_tester.get_expected_values(A)

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

						# Test batched
						_UpperCAmelCase	,					_UpperCAmelCase			=       self.image_processor_tester.get_expected_values(A			,		batched=A)

						_UpperCAmelCase			=       image_processing(A			,		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       :  Dict)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase			=       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A			,		numpify=A)
						for image in image_inputs:
									self.assertIsInstance(A			,		np.ndarray)

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

						_UpperCAmelCase	,					_UpperCAmelCase			=       self.image_processor_tester.get_expected_values(A)

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

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

						_UpperCAmelCase	,					_UpperCAmelCase			=       self.image_processor_tester.get_expected_values(A			,		batched=A)

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



			def    _lowerCamelCase	(						self       :  str)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase			=       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A			,		torchify=A)
						for image in image_inputs:
									self.assertIsInstance(A			,		torch.Tensor)

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

						_UpperCAmelCase	,					_UpperCAmelCase			=       self.image_processor_tester.get_expected_values(A)

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

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

						_UpperCAmelCase	,					_UpperCAmelCase			=       self.image_processor_tester.get_expected_values(A			,		batched=A)

						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       :  Union[str, Any])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,		'r') as f:
									_UpperCAmelCase			=       json.loads(f.read())

						_UpperCAmelCase			=       {'image_id': 3_97_69, 'annotations': target}

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

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

						_UpperCAmelCase			=       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]			,		A			,		atol=1E-4))

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



			@slow
			def    _lowerCamelCase	(						self       :  int)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'			,		'r') as f:
									_UpperCAmelCase			=       json.loads(f.read())

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

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

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

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

						_UpperCAmelCase			=       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]			,		A			,		atol=1E-4))

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["LxmertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "LxmertEncoder",
							    "LxmertForPreTraining",
							    "LxmertForQuestionAnswering",
							    "LxmertModel",
							    "LxmertPreTrainedModel",
							    "LxmertVisualFeatureEncoder",
							    "LxmertXLayer",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFLxmertForPreTraining",
							    "TFLxmertMainLayer",
							    "TFLxmertModel",
							    "TFLxmertPreTrainedModel",
							    "TFLxmertVisualFeatureEncoder",
							]


if TYPE_CHECKING:
							from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
							from .tokenization_lxmert import LxmertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_lxmert_fast import LxmertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lxmert import (
														    LxmertEncoder,
														    LxmertForPreTraining,
														    LxmertForQuestionAnswering,
														    LxmertModel,
														    LxmertPreTrainedModel,
														    LxmertVisualFeatureEncoder,
														    LxmertXLayer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_lxmert import (
														    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLxmertForPreTraining,
														    TFLxmertMainLayer,
														    TFLxmertModel,
														    TFLxmertPreTrainedModel,
														    TFLxmertVisualFeatureEncoder,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
