

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
					import jax

					from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor







class 				lowercase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=100   ,     snake_case=13   ,     snake_case=30   ,     snake_case=2   ,     snake_case=3   ,     snake_case=True   ,     snake_case=True   ,     snake_case=32   ,     snake_case=5   ,     snake_case=4   ,     snake_case=37   ,     snake_case="gelu"   ,     snake_case=0.1   ,     snake_case=0.1   ,     snake_case=10   ,     snake_case=0.02   ,     snake_case=3   ,     ):
											snake_case_           =					parent
											snake_case_           =					vocab_size
											snake_case_           =					batch_size
											snake_case_           =					image_size
											snake_case_           =					patch_size
											snake_case_           =					num_channels
											snake_case_           =					is_training
											snake_case_           =					use_labels
											snake_case_           =					hidden_size
											snake_case_           =					num_hidden_layers
											snake_case_           =					num_attention_heads
											snake_case_           =					intermediate_size
											snake_case_           =					hidden_act
											snake_case_           =					hidden_dropout_prob
											snake_case_           =					attention_probs_dropout_prob
											snake_case_           =					type_sequence_label_size
											snake_case_           =					initializer_range

											# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											snake_case_           =					(image_size // patch_size) ** 2
											snake_case_           =					num_patches + 1




				def 					a							(      self ):
											snake_case_           =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

											snake_case_           =					None
											if self.use_labels:
																		snake_case_           =					ids_tensor([self.batch_size]   ,     self.type_sequence_label_size )

											snake_case_           =					BeitConfig(
											    vocab_size=self.vocab_size   ,     image_size=self.image_size   ,     patch_size=self.patch_size   ,     num_channels=self.num_channels   ,     hidden_size=self.hidden_size   ,     num_hidden_layers=self.num_hidden_layers   ,     num_attention_heads=self.num_attention_heads   ,     intermediate_size=self.intermediate_size   ,     hidden_act=self.hidden_act   ,     hidden_dropout_prob=self.hidden_dropout_prob   ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,     is_decoder=snake_case   ,     initializer_range=self.initializer_range   ,     )

											return config, pixel_values, labels




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					FlaxBeitModel(config=snake_case )
											snake_case_           =					model(snake_case )
											self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size) )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					FlaxBeitForMaskedImageModeling(config=snake_case )
											snake_case_           =					model(snake_case )
											self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length - 1, self.vocab_size) )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.type_sequence_label_size
											snake_case_           =					FlaxBeitForImageClassification(config=snake_case )
											snake_case_           =					model(snake_case )
											self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size) )

											# test greyscale images
											snake_case_           =					1
											snake_case_           =					FlaxBeitForImageClassification(snake_case )

											snake_case_           =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
											snake_case_           =					model(snake_case )




				def 					a							(      self ):
											snake_case_           =					self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					
											)           =					config_and_inputs
											snake_case_           =					{'pixel_values': pixel_values}
											return config, inputs_dict







@require_flax
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Tuple     =    (
				    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
				)




				def 					a							(      self ):
											snake_case_           =					FlaxBeitModelTester(self )
											snake_case_           =					ConfigTester(self   ,     config_class=snake_case   ,     has_text_modality=snake_case   ,     hidden_size=37 )




				def 					a							(      self ):
											self.config_tester.run_common_tests()




				def 					a							(      self ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case_           =					model_class(snake_case )
																		snake_case_           =					inspect.signature(model.__call__ )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		snake_case_           =					[*signature.parameters.keys()]

																		snake_case_           =					['pixel_values']
																		self.assertListEqual(arg_names[:1]   ,     snake_case )




				def 					a							(      self ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__ ):
																									snake_case_           =					self._prepare_for_class(snake_case   ,     snake_case )
																									snake_case_           =					model_class(snake_case )

																									@jax.jit
																									def model_jitted(snake_case   ,     **snake_case ):
																																return model(pixel_values=snake_case   ,     **snake_case )

																									with self.subTest('JIT Enabled' ):
																																snake_case_           =					model_jitted(**snake_case ).to_tuple()

																									with self.subTest('JIT Disabled' ):
																																with jax.disable_jit():
																																							snake_case_           =					model_jitted(**snake_case ).to_tuple()

																									self.assertEqual(len(snake_case )   ,     len(snake_case ) )
																									for jitted_output, output in zip(snake_case   ,     snake_case ):
																																self.assertEqual(jitted_output.shape   ,     output.shape )




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case )




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*snake_case )




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case )




				@slow
				def 					a							(      self ):
											for model_class_name in self.all_model_classes:
																		snake_case_           =					model_class_name.from_pretrained('microsoft/beit-base-patch16-224' )
																		snake_case_           =					model(np.ones((1, 3, 224, 224) ) )
																		self.assertIsNotNone(snake_case )





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							return image







@require_vision
@require_flax
class 				lowercase       ( unittest.TestCase    ):




				@cached_property
				def 					a							(      self ):
											return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None




				@slow
				def 					a							(      self ):
											snake_case_           =					FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' )

											snake_case_           =					self.default_image_processor
											snake_case_           =					prepare_img()
											snake_case_           =					image_processor(images=snake_case   ,     return_tensors='np' ).pixel_values

											# prepare bool_masked_pos
											snake_case_           =					np.ones((1, 196)   ,     dtype=snake_case )

											# forward pass
											snake_case_           =					model(pixel_values=snake_case   ,     bool_masked_pos=snake_case )
											snake_case_           =					outputs.logits

											# verify the logits
											snake_case_           =					(1, 196, 8192)
											self.assertEqual(logits.shape   ,     snake_case )

											snake_case_           =					np.array(
											    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] )

											self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]   ,     snake_case   ,     atol=1e-2 ) )




				@slow
				def 					a							(      self ):
											snake_case_           =					FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' )

											snake_case_           =					self.default_image_processor
											snake_case_           =					prepare_img()
											snake_case_           =					image_processor(images=snake_case   ,     return_tensors='np' )

											# forward pass
											snake_case_           =					model(**snake_case )
											snake_case_           =					outputs.logits

											# verify the logits
											snake_case_           =					(1, 1000)
											self.assertEqual(logits.shape   ,     snake_case )

											snake_case_           =					np.array([-1.23_85, -1.09_87, -1.01_08] )

											self.assertTrue(np.allclose(logits[0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											snake_case_           =					281
											self.assertEqual(logits.argmax(-1 ).item()   ,     snake_case )




				@slow
				def 					a							(      self ):
											snake_case_           =					FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' )

											snake_case_           =					self.default_image_processor
											snake_case_           =					prepare_img()
											snake_case_           =					image_processor(images=snake_case   ,     return_tensors='np' )

											# forward pass
											snake_case_           =					model(**snake_case )
											snake_case_           =					outputs.logits

											# verify the logits
											snake_case_           =					(1, 2_1841)
											self.assertEqual(logits.shape   ,     snake_case )

											snake_case_           =					np.array([1.68_81, -0.27_87, 0.59_01] )

											self.assertTrue(np.allclose(logits[0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											snake_case_           =					2396
											self.assertEqual(logits.argmax(-1 ).item()   ,     snake_case )







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 				lowercase       ( unittest.TestCase    ):




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

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

											return expected_height, expected_width







@require_torch
@require_vision
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				str     =    DetrImageProcessor if is_vision_available() else None




				def 					a							(      self ):
											snake_case_           =					DetrImageProcessingTester(self )




				@property
				def 					a							(      self ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					a							(      self ):
											snake_case_           =					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_rescale' ) )
											self.assertTrue(hasattr(snake_case   ,     'rescale_factor' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_resize' ) )
											self.assertTrue(hasattr(snake_case   ,     'size' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_pad' ) )




				def 					a							(      self ):
											snake_case_           =					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   ,     snake_case )

											snake_case_           =					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 ):
											pass




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PIL images
											snake_case_           =					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
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					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
											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											snake_case_           =					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 ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random numpy tensors
											snake_case_           =					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
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					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
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					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 ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PyTorch tensors
											snake_case_           =					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
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					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
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					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 ):
											# prepare image and target
											snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,     'r' ) as f:
																		snake_case_           =					json.loads(f.read() )

											snake_case_           =					{'image_id': 3_9769, 'annotations': target}

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

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					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']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([75, 75, 63, 65, 17, 17] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




				@slow
				def 					a							(      self ):
											# prepare image, target and masks_path
											snake_case_           =					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_           =					json.loads(f.read() )

											snake_case_           =					{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

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

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

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					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']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([17, 17, 63, 75, 75, 93] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify masks
											snake_case_           =					82_2873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,     snake_case )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




