





'''simple docstring'''
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__							:							List[Any] =logging.get_logger(__name__)

a__							:							List[Any] ={
    '''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_text_model"






 def __init__(       self       :							str							,	__A       :							List[Any]=2_5_0_0_0_2							,	__A       :							Any=1_0_2_4							,	__A       :							int=2_4							,	__A       :							Dict=1_6							,	__A       :							Optional[Any]=4_0_9_6							,	__A       :							Union[str, Any]="gelu"							,	__A       :							Dict=0.1							,	__A       :							Dict=0.1							,	__A       :							List[str]=5_1_4							,	__A       :							Optional[int]=1							,	__A       :							int=0.02							,	__A       :							Optional[Any]=0.02							,	__A       :							Optional[Any]=1e-05							,	__A       :							Dict=1							,	__A       :							List[Any]=0							,	__A       :							int=2							,	__A       :							Tuple="absolute"							,	__A       :							Optional[Any]=True							,	__A       :							Optional[int]=7_6_8							,	**__A       :							List[str]							,	):
        super().__init__(pad_token_id=__A							,	bos_token_id=__A							,	eos_token_id=__A							,	**__A	)

        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  type_vocab_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  position_embedding_type
        __UpperCamelCase       =  use_cache
        __UpperCamelCase       =  project_dim






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_vision_model"






 def __init__(       self       :							List[Any]							,	__A       :							Union[str, Any]=7_6_8							,	__A       :							Optional[int]=3_0_7_2							,	__A       :							Optional[Any]=5_1_2							,	__A       :							Tuple=1_2							,	__A       :							Union[str, Any]=1_2							,	__A       :							Optional[int]=3							,	__A       :							Dict=2_2_4							,	__A       :							Tuple=3_2							,	__A       :							str="quick_gelu"							,	__A       :							Dict=1e-5							,	__A       :							Optional[int]=0.0							,	__A       :							List[Any]=0.02							,	__A       :							int=1.0							,	**__A       :							Optional[int]							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  hidden_act






 @classmethod
 def        _lowerCamelCase					(       cls       :							Optional[Any]							,	__A       :							Union[str, os.PathLike]							,	**__A       :							Optional[Any]	):
        cls._set_token_in_kwargs(__A	)

        __UpperCamelCase       ,						__UpperCamelCase       =  cls.get_config_dict(__A							,	**__A	)

        # get the vision config dict if we are loading from AltCLIPConfig
        if config_dict.get('model_type'	) == "altclip":
               __UpperCamelCase       =  config_dict['vision_config']

        if "model_type" in config_dict and hasattr(cls							,	'model_type'	) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''	)

        return cls.from_dict(__A							,	**__A	)






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					="altclip"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=True






 def __init__(       self       :							Any							,	__A       :							List[str]=None							,	__A       :							List[Any]=None							,	__A       :							List[str]=7_6_8							,	__A       :							List[str]=2.6592							,	**__A       :							Dict	):
        # If `_config_dict` exist, we use them for the backward compatibility.
        # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
        # of confusion!).
        __UpperCamelCase       =  kwargs.pop('text_config_dict'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('vision_config_dict'							,	__A	)

        super().__init__(**__A	)

        # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
        # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
        # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
        if text_config_dict is not None:
               if text_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `text_config_dict`.
               __UpperCamelCase       =  AltCLIPTextConfig(**__A	).to_dict()

               # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
               for key, value in _text_config_dict.items():
                      if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                             # If specified in `text_config_dict`
                             if key in text_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
                                        f'''The value `text_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
                                        f'''value `text_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
               text_config.update(_text_config_dict	)

        if vision_config_dict is not None:
               if vision_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `vision_config_dict`.
               __UpperCamelCase       =  AltCLIPVisionConfig(**__A	).to_dict()
               # convert keys to string instead of integer
               if "id2label" in _vision_config_dict:
                      __UpperCamelCase       =  {
                          str(__A	): value for key, value in _vision_config_dict['id2label'].items()
                      }

               # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
               for key, value in _vision_config_dict.items():
                      if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                             # If specified in `vision_config_dict`
                             if key in vision_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
                                        f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
                                        f'''The value `vision_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
               vision_config.update(_vision_config_dict	)

        if text_config is None:
               __UpperCamelCase       =  {}
               logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.'	)

        if vision_config is None:
               __UpperCamelCase       =  {}
               logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.'	)

        __UpperCamelCase       =  AltCLIPTextConfig(**__A	)
        __UpperCamelCase       =  AltCLIPVisionConfig(**__A	)

        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  logit_scale_init_value
        __UpperCamelCase       =  1.0






 @classmethod
 def        _lowerCamelCase					(       cls       :							Union[str, Any]							,	__A       :							AltCLIPTextConfig							,	__A       :							AltCLIPVisionConfig							,	**__A       :							Optional[Any]	):
        return cls(text_config=text_config.to_dict()							,	vision_config=vision_config.to_dict()							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  copy.deepcopy(self.__dict__	)
        __UpperCamelCase       =  self.text_config.to_dict()
        __UpperCamelCase       =  self.vision_config.to_dict()
        __UpperCamelCase       =  self.__class__.model_type
        return output






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 DeformableDetrImageProcessor


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	int=7					, UpperCAmelCase__      :	Dict=3					, UpperCAmelCase__      :	List[Any]=30					, UpperCAmelCase__      :	Any=400					, UpperCAmelCase__      :	Optional[Any]=True					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	Any=True					, UpperCAmelCase__      :	Optional[Any]=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	List[str]=True					, UpperCAmelCase__      :	Optional[int]=1 / 255					, UpperCAmelCase__      :	Optional[Any]=True					, )						->str:





           '''simple docstring'''

           A__       =    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''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        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int=False)						->Optional[Any]:





           '''simple docstring'''

           if not batched:
                  A__       =    image_inputs[0]
                  if isinstance(UpperCAmelCase__					, Image.Image):
                         A__ ,							A__       =    image.size
                  else:
                         A__ ,							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__ ,							A__       =    self.get_expected_values([image])
                         expected_values.append((expected_height, expected_width))
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[0])[0]
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[1])[1]

           return expected_height, expected_width


@require_torch
@require_vision
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      DeformableDetrImageProcessor if is_vision_available() else None


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





           '''simple docstring'''

           A__       =    DeformableDetrImageProcessingTester(self)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Any:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_mean'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_std'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_normalize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_rescale'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_pad'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''size'''))


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size					, {'''shortest_edge''': 18, '''longest_edge''': 1_333})
           self.assertEqual(image_processor.do_pad					, UpperCAmelCase__)

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


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





           '''simple docstring'''

           pass


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict:





           '''simple docstring'''

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

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

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

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

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

           A__       =    image_processing(UpperCAmelCase__					, 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        SCREAMING_SNAKE_CASE							(			self      :	int)						->Optional[int]:





           '''simple docstring'''

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

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

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

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

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

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

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


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

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

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

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

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

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

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

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


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[str]:





           '''simple docstring'''

           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''': 39_769, '''annotations''': target}

           # encode them
           A__       =    DeformableDetrImageProcessor()
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

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

           # verify area
           A__       =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Optional[int]:





           '''simple docstring'''

           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''': 39_769, '''segments_info''': target}

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

           # encode them
           A__       =    DeformableDetrImageProcessor(format='''coco_panoptic''')
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, masks_path=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

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

           # verify area
           A__       =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify masks
           A__       =    822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					, UpperCAmelCase__)
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))


