




"""simple docstring"""






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
			import timm


if is_torch_available():
			from torch import Tensor






class lowerCAmelCase__			(  lowercase,			lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """pixel_values"""
			lowerCamelCase__   =    False
			lowerCamelCase__   =    TimmBackboneConfig
			def __init__(						self						,   lowercase						,   **lowercase						):
							requires_backends(self						,   'timm'						)
							super().__init__(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					config

							if config.backbone is None:
											raise ValueError('backbone is not set in the config. Please set it to a timm model name.'						)

							if config.backbone not in timm.list_models():
											raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''						)

							if hasattr(lowercase						,   'out_features'						) and config.out_features is not None:
											raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'						)

							_lowerCamelCase  :    Tuple					=					getattr(lowercase						,   'use_pretrained_backbone'						,   lowercase						)
							if pretrained is None:
											raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'						)

							# We just take the final layer by default. This matches the default for the transformers models.
							_lowerCamelCase  :    List[Any]					=					config.out_indices if getattr(lowercase						,   'out_indices'						,   lowercase						) is not None else (-1,)

							_lowerCamelCase  :    str					=					timm.create_model(
							    config.backbone						,   pretrained=lowercase						,   features_only=config.features_only						,   in_chans=config.num_channels						,   out_indices=lowercase						,   **lowercase						,   )
							# These are used to control the output of the model when called. If output_hidden_states is True, then
							# return_layers is modified to include all layers.
							_lowerCamelCase  :    Any					=					self._backbone.return_layers
							_lowerCamelCase  :    Dict					=					{layer['module']: str(lowercase						) for i, layer in enumerate(self._backbone.feature_info.info						)}
							super()._init_backbone(lowercase						)
			@classmethod
			def       A_					(						cls						,   lowercase						,   *lowercase						,   **lowercase						):
							requires_backends(cls						,   ['vision', 'timm']						)
							from ...models.timm_backbone import TimmBackboneConfig

							_lowerCamelCase  :    List[Any]					=					kwargs.pop('config'						,   TimmBackboneConfig()						)

							_lowerCamelCase  :    List[Any]					=					kwargs.pop('use_timm_backbone'						,   lowercase						)
							if not use_timm:
											raise ValueError('use_timm_backbone must be True for timm backbones'						)

							_lowerCamelCase  :    List[str]					=					kwargs.pop('num_channels'						,   config.num_channels						)
							_lowerCamelCase  :    Tuple					=					kwargs.pop('features_only'						,   config.features_only						)
							_lowerCamelCase  :    List[Any]					=					kwargs.pop('use_pretrained_backbone'						,   config.use_pretrained_backbone						)
							_lowerCamelCase  :    Union[str, Any]					=					kwargs.pop('out_indices'						,   config.out_indices						)
							_lowerCamelCase  :    List[Any]					=					TimmBackboneConfig(
							    backbone=lowercase						,   num_channels=lowercase						,   features_only=lowercase						,   use_pretrained_backbone=lowercase						,   out_indices=lowercase						,   )
							return super()._from_config(lowercase						,   **lowercase						)
			def       A_					(						self						,   lowercase						):
							pass






			def       A_					(						self						,   lowercase						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   **lowercase						):
							_lowerCamelCase  :    Tuple					=					return_dict if return_dict is not None else self.config.use_return_dict
							_lowerCamelCase  :    Optional[int]					=					(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							_lowerCamelCase  :    int					=					output_attentions if output_attentions is not None else self.config.output_attentions

							if output_attentions:
											raise ValueError('Cannot output attentions for timm backbones at the moment'						)

							if output_hidden_states:
											# We modify the return layers to include all the stages of the backbone
											_lowerCamelCase  :    List[Any]					=					self._all_layers
											_lowerCamelCase  :    str					=					self._backbone(lowercase						,   **lowercase						)
											_lowerCamelCase  :    str					=					self._return_layers
											_lowerCamelCase  :    Tuple					=					tuple(hidden_states[i] for i in self.out_indices						)
							else:
											_lowerCamelCase  :    int					=					self._backbone(lowercase						,   **lowercase						)
											_lowerCamelCase  :    Optional[Any]					=					None

							_lowerCamelCase  :    Dict					=					tuple(lowercase						)
							_lowerCamelCase  :    List[str]					=					tuple(lowercase						) if hidden_states is not None else None

							if not return_dict:
											_lowerCamelCase  :    Union[str, Any]					=					(feature_maps,)
											if output_hidden_states:
															_lowerCamelCase  :    Union[str, Any]					=					output + (hidden_states,)
											return output

							return BackboneOutput(feature_maps=lowercase						,   hidden_states=lowercase						,   attentions=lowercase						)





"""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 DetrImageProcessor






class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase=7						,   lowercase=3						,   lowercase=30						,   lowercase=400						,   lowercase=True						,   lowercase=None						,   lowercase=True						,   lowercase=1 / 255						,   lowercase=True						,   lowercase=[0.5, 0.5, 0.5]						,   lowercase=[0.5, 0.5, 0.5]						,   lowercase=True						,   ):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							_lowerCamelCase  :    Any					=					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
							_lowerCamelCase  :    Optional[int]					=					parent
							_lowerCamelCase  :    Any					=					batch_size
							_lowerCamelCase  :    int					=					num_channels
							_lowerCamelCase  :    Union[str, Any]					=					min_resolution
							_lowerCamelCase  :    List[Any]					=					max_resolution
							_lowerCamelCase  :    Union[str, Any]					=					do_resize
							_lowerCamelCase  :    str					=					size
							_lowerCamelCase  :    List[str]					=					do_rescale
							_lowerCamelCase  :    Optional[int]					=					rescale_factor
							_lowerCamelCase  :    str					=					do_normalize
							_lowerCamelCase  :    Optional[int]					=					image_mean
							_lowerCamelCase  :    Tuple					=					image_std
							_lowerCamelCase  :    Tuple					=					do_pad
			def       A_					(						self						):
							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       A_					(						self						,   lowercase						,   lowercase=False						):
							if not batched:
											_lowerCamelCase  :    Union[str, Any]					=					image_inputs[0]
											if isinstance(lowercase						,   Image.Image						):
															_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					image.size
											else:
															_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					image.shape[1], image.shape[2]
											if w < h:
															_lowerCamelCase  :    List[Any]					=					int(self.size['shortest_edge'] * h / w						)
															_lowerCamelCase  :    Union[str, Any]					=					self.size['shortest_edge']
											elif w > h:
															_lowerCamelCase  :    List[str]					=					self.size['shortest_edge']
															_lowerCamelCase  :    Optional[Any]					=					int(self.size['shortest_edge'] * w / h						)
											else:
															_lowerCamelCase  :    int					=					self.size['shortest_edge']
															_lowerCamelCase  :    Optional[Any]					=					self.size['shortest_edge']

							else:
											_lowerCamelCase  :    Union[str, Any]					=					[]
											for image in image_inputs:
															_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					self.get_expected_values([image]						)
															expected_values.append((expected_height, expected_width)						)
											_lowerCamelCase  :    Tuple					=					max(lowercase						,   key=lambda lowercase						: item[0]						)[0]
											_lowerCamelCase  :    Any					=					max(lowercase						,   key=lambda lowercase						: item[1]						)[1]

							return expected_height, expected_width






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






			'''simple docstring'''



			lowerCamelCase__   =    DetrImageProcessor if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					DetrImageProcessingTester(self						)
			@property
			def       A_					(						self						):
							return self.image_processor_tester.prepare_image_processor_dict()
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					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_rescale'						)						)
							self.assertTrue(hasattr(lowercase						,   'rescale_factor'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_resize'						)						)
							self.assertTrue(hasattr(lowercase						,   'size'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_pad'						)						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size						,   {'shortest_edge': 18, 'longest_edge': 1333}						)
							self.assertEqual(image_processor.do_pad						,   lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					self.image_processing_class.from_dict(
							    self.image_processor_dict						,   size=42						,   max_size=84						,   pad_and_return_pixel_mask=lowercase						)
							self.assertEqual(image_processor.size						,   {'shortest_edge': 42, 'longest_edge': 84}						)
							self.assertEqual(image_processor.do_pad						,   lowercase						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[int]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							_lowerCamelCase  :    List[str]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   Image.Image						)

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

							_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					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
							_lowerCamelCase,				_lowerCamelCase  :    str					=					self.image_processor_tester.get_expected_values(lowercase						,   batched=lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					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       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[int]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							_lowerCamelCase  :    Union[str, Any]					=					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
							_lowerCamelCase  :    Optional[int]					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					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
							_lowerCamelCase  :    str					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					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       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    List[str]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    Optional[Any]					=					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
							_lowerCamelCase  :    int					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					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
							_lowerCamelCase  :    Dict					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					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       A_					(						self						):
							# prepare image and target
							_lowerCamelCase  :    Tuple					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,   'r'						) as f:
											_lowerCamelCase  :    int					=					json.loads(f.read()						)

							_lowerCamelCase  :    str					=					{'image_id': 39769, 'annotations': target}

							# encode them
							_lowerCamelCase  :    str					=					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'						)
							_lowerCamelCase  :    int					=					image_processing(images=lowercase						,   annotations=lowercase						,   return_tensors='pt'						)

							# verify pixel values
							_lowerCamelCase  :    Optional[Any]					=					torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding['pixel_values'].shape						,   lowercase						)

							_lowerCamelCase  :    List[str]					=					torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,   lowercase						,   atol=1E-4						)						)

							# verify area
							_lowerCamelCase  :    Dict					=					torch.tensor([58_87.96_00, 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
							_lowerCamelCase  :    int					=					torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,   lowercase						)
							_lowerCamelCase  :    Tuple					=					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,   lowercase						,   atol=1E-3						)						)
							# verify image_id
							_lowerCamelCase  :    List[Any]					=					torch.tensor([39769]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,   lowercase						)						)
							# verify is_crowd
							_lowerCamelCase  :    str					=					torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,   lowercase						)						)
							# verify class_labels
							_lowerCamelCase  :    Dict					=					torch.tensor([75, 75, 63, 65, 17, 17]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,   lowercase						)						)
							# verify orig_size
							_lowerCamelCase  :    Optional[int]					=					torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,   lowercase						)						)
							# verify size
							_lowerCamelCase  :    List[str]					=					torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,   lowercase						)						)






			@slow
			def       A_					(						self						):
							# prepare image, target and masks_path
							_lowerCamelCase  :    Optional[int]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,   'r'						) as f:
											_lowerCamelCase  :    List[Any]					=					json.loads(f.read()						)

							_lowerCamelCase  :    str					=					{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

							# encode them
							_lowerCamelCase  :    List[Any]					=					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'						)
							_lowerCamelCase  :    Dict					=					image_processing(images=lowercase						,   annotations=lowercase						,   masks_path=lowercase						,   return_tensors='pt'						)

							# verify pixel values
							_lowerCamelCase  :    Optional[int]					=					torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding['pixel_values'].shape						,   lowercase						)

							_lowerCamelCase  :    List[Any]					=					torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,   lowercase						,   atol=1E-4						)						)

							# verify area
							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,   lowercase						)						)
							# verify boxes
							_lowerCamelCase  :    Dict					=					torch.Size([6, 4]						)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,   lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,   lowercase						,   atol=1E-3						)						)
							# verify image_id
							_lowerCamelCase  :    List[str]					=					torch.tensor([39769]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,   lowercase						)						)
							# verify is_crowd
							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,   lowercase						)						)
							# verify class_labels
							_lowerCamelCase  :    List[str]					=					torch.tensor([17, 17, 63, 75, 75, 93]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,   lowercase						)						)
							# verify masks
							_lowerCamelCase  :    List[str]					=					822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,   lowercase						)
							# verify orig_size
							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,   lowercase						)						)
							# verify size
							_lowerCamelCase  :    List[str]					=					torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,   lowercase						)						)