import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    str:



      '''simple docstring'''

      with open(__UpperCAmelCase		) as metadata_file:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
      _lowerCamelCase			    =				LukeConfig(use_entity_aware_attention=__UpperCAmelCase ,						**metadata['''model_config''']		)

      # Load in the weights from the checkpoint_path
      _lowerCamelCase			    =				torch.load(__UpperCAmelCase ,						map_location='''cpu'''		)['''module''']

      # Load the entity vocab file
      _lowerCamelCase			    =				load_original_entity_vocab(__UpperCAmelCase		)
      # add an entry for [MASK2]
      _lowerCamelCase			    =				max(entity_vocab.values()		) + 1
      config.entity_vocab_size += 1

      _lowerCamelCase			    =				XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']		)

      # Add special tokens to the token vocabulary for downstream tasks
      _lowerCamelCase			    =				AddedToken('''<ent>''' ,						lstrip=__UpperCAmelCase ,						rstrip=__UpperCAmelCase		)
      _lowerCamelCase			    =				AddedToken('''<ent2>''' ,						lstrip=__UpperCAmelCase ,						rstrip=__UpperCAmelCase		)
      tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}		)
      config.vocab_size += 2

      print(F'Saving tokenizer to {pytorch_dump_folder_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)
      with open(os.path.join(__UpperCAmelCase ,						'''tokenizer_config.json'''		) ,						'''r'''		) as f:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
      _lowerCamelCase			    =				'''MLukeTokenizer'''
      with open(os.path.join(__UpperCAmelCase ,						'''tokenizer_config.json'''		) ,						'''w'''		) as f:
            json.dump(__UpperCAmelCase ,						__UpperCAmelCase		)

      with open(os.path.join(__UpperCAmelCase ,						MLukeTokenizer.vocab_files_names['''entity_vocab_file''']		) ,						'''w'''		) as f:
            json.dump(__UpperCAmelCase ,						__UpperCAmelCase		)

      _lowerCamelCase			    =				MLukeTokenizer.from_pretrained(__UpperCAmelCase		)

      # Initialize the embeddings of the special tokens
      _lowerCamelCase			    =				tokenizer.convert_tokens_to_ids(['''@''']		)[0]
      _lowerCamelCase			    =				tokenizer.convert_tokens_to_ids(['''#''']		)[0]

      _lowerCamelCase			    =				state_dict['''embeddings.word_embeddings.weight''']
      _lowerCamelCase			    =				word_emb[ent_init_index].unsqueeze(0		)
      _lowerCamelCase			    =				word_emb[enta_init_index].unsqueeze(0		)
      _lowerCamelCase			    =				torch.cat([word_emb, ent_emb, enta_emb]		)
      # add special tokens for 'entity_predictions.bias'
      for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
            _lowerCamelCase			    =				state_dict[bias_name]
            _lowerCamelCase			    =				decoder_bias[ent_init_index].unsqueeze(0		)
            _lowerCamelCase			    =				decoder_bias[enta_init_index].unsqueeze(0		)
            _lowerCamelCase			    =				torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]		)

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers		):
            for matrix_name in ["query.weight", "query.bias"]:
                  _lowerCamelCase			    =				F'encoder.layer.{layer_index}.attention.self.'
                  _lowerCamelCase			    =				state_dict[prefix + matrix_name]
                  _lowerCamelCase			    =				state_dict[prefix + matrix_name]
                  _lowerCamelCase			    =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      _lowerCamelCase			    =				state_dict['''entity_embeddings.entity_embeddings.weight''']
      _lowerCamelCase			    =				entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0		)
      _lowerCamelCase			    =				torch.cat([entity_emb, entity_mask_emb]		)
      # add [MASK2] for 'entity_predictions.bias'
      _lowerCamelCase			    =				state_dict['''entity_predictions.bias''']
      _lowerCamelCase			    =				entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0		)
      _lowerCamelCase			    =				torch.cat([entity_prediction_bias, entity_mask_bias]		)

      _lowerCamelCase			    =				LukeForMaskedLM(config=__UpperCAmelCase		).eval()

      state_dict.pop('''entity_predictions.decoder.weight'''		)
      state_dict.pop('''lm_head.decoder.weight'''		)
      state_dict.pop('''lm_head.decoder.bias'''		)
      _lowerCamelCase			    =				OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('''lm_head'''		) or key.startswith('''entity_predictions'''		)):
                  _lowerCamelCase			    =				state_dict[key]
            else:
                  _lowerCamelCase			    =				state_dict[key]

      _lowerCamelCase    ,					_lowerCamelCase			    =				model.load_state_dict(__UpperCAmelCase ,						strict=__UpperCAmelCase		)

      if set(__UpperCAmelCase		) != {"luke.embeddings.position_ids"}:
            raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'		)
      if set(__UpperCAmelCase		) != {
          "lm_head.decoder.weight",
          "lm_head.decoder.bias",
          "entity_predictions.decoder.weight",
      }:
            raise ValueError(F'Unexpected missing_keys: {missing_keys}'		)

      model.tie_weights()
      assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
      assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

      # Check outputs
      _lowerCamelCase			    =				MLukeTokenizer.from_pretrained(__UpperCAmelCase ,						task='''entity_classification'''		)

      _lowerCamelCase			    =				'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
      _lowerCamelCase			    =				(0, 9)
      _lowerCamelCase			    =				tokenizer(__UpperCAmelCase ,						entity_spans=[span] ,						return_tensors='''pt'''		)

      _lowerCamelCase			    =				model(**__UpperCAmelCase		)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            _lowerCamelCase			    =				torch.Size((1, 33, 768)		)
            _lowerCamelCase			    =				torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]		)

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'		)
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,						__UpperCAmelCase ,						atol=1E-4		):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            _lowerCamelCase			    =				torch.Size((1, 1, 768)		)
            _lowerCamelCase			    =				torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]		)

      if not (outputs.entity_last_hidden_state.shape == expected_shape):
            raise ValueError(
                F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
                F' {expected_shape}'		)
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,						__UpperCAmelCase ,						atol=1E-4		):
            raise ValueError

      # Verify masked word/entity prediction
      _lowerCamelCase			    =				MLukeTokenizer.from_pretrained(__UpperCAmelCase		)
      _lowerCamelCase			    =				'''Tokyo is the capital of <mask>.'''
      _lowerCamelCase			    =				(24, 30)
      _lowerCamelCase			    =				tokenizer(__UpperCAmelCase ,						entity_spans=[span] ,						return_tensors='''pt'''		)

      _lowerCamelCase			    =				model(**__UpperCAmelCase		)

      _lowerCamelCase			    =				encoding['''input_ids'''][0].tolist()
      _lowerCamelCase			    =				input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''		)		)
      _lowerCamelCase			    =				outputs.logits[0][mask_position_id].argmax(dim=-1		)
      assert "Japan" == tokenizer.decode(__UpperCAmelCase		)

      _lowerCamelCase			    =				outputs.entity_logits[0][0].argmax().item()
      _lowerCamelCase			    =				[
          entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
      ]
      assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''		)][0] == "en:Japan"

      # Finally, save our PyTorch model and tokenizer
      print('''Saving PyTorch model to {}'''.format(__UpperCAmelCase		)		)
      model.save_pretrained(__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				['''[MASK]''', '''[PAD]''', '''[UNK]''']

      _lowerCamelCase			    =				[json.loads(__UpperCAmelCase		) for line in open(__UpperCAmelCase		)]

      _lowerCamelCase			    =				{}
      for entry in data:
            _lowerCamelCase			    =				entry['''id''']
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        _lowerCamelCase			    =				entity_id
                        break
                  _lowerCamelCase			    =				F'{language}:{entity_name}'
                  _lowerCamelCase			    =				entity_id
      return new_mapping


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
     parser.add_argument(
         '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
     )
     parser.add_argument(
         '--entity_vocab_path',
         default=None,
         type=str,
         help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
     )
     parser.add_argument(
         '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
     )
     snake_case__				= parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )
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 YolosImageProcessor



class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=7				,				A_=3				,				A_=30				,				A_=4_00				,				A_=True				,				A_=None				,				A_=True				,				A_=[0.5, 0.5, 0.5]				,				A_=[0.5, 0.5, 0.5]				,				A_=True				,				A_=1 / 2_55				,				A_=True				,				) ->				List[Any]:






           """simple docstring"""
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           _lowerCamelCase			    =				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				num_channels
           _lowerCamelCase			    =				min_resolution
           _lowerCamelCase			    =				max_resolution
           _lowerCamelCase			    =				do_resize
           _lowerCamelCase			    =				size
           _lowerCamelCase			    =				do_normalize
           _lowerCamelCase			    =				image_mean
           _lowerCamelCase			    =				image_std
           _lowerCamelCase			    =				do_rescale
           _lowerCamelCase			    =				rescale_factor
           _lowerCamelCase			    =				do_pad



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """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 		UpperCamelCase_						(					self				,				A_				,				A_=False						) ->				List[str]:






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

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

           return expected_height, expected_width





@require_torch
@require_vision
class  UpperCamelCase				( __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  YolosImageProcessor if is_vision_available() else None



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				YolosImageProcessingTester(self						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return self.image_processor_tester.prepare_image_processor_dict()



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				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_resize'''						)						)
           self.assertTrue(hasattr(A_				,				'''size'''						)						)



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






           """simple docstring"""
           _lowerCamelCase			    =				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_						)

           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PIL images
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				Image.Image						)

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

           _lowerCamelCase    ,					_lowerCamelCase			    =				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
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Any:






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

           _lowerCamelCase    ,					_lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Optional[int]:






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

           _lowerCamelCase    ,					_lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           # Initialize image_processings
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           _lowerCamelCase			    =				self.image_processing_class(do_resize=A_				,				do_normalize=A_				,				do_rescale=A_						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_				,				torchify=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				torch.Tensor						)

           # Test whether the method "pad" and calling the image processor return the same tensors
           _lowerCamelCase			    =				image_processing_a.pad(A_				,				return_tensors='''pt'''						)
           _lowerCamelCase			    =				image_processing_a(A_				,				return_tensors='''pt'''						)

           self.assertTrue(
               torch.allclose(encoded_images_with_method['''pixel_values''']				,				encoded_images['''pixel_values''']				,				atol=1E-4						)						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






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

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

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				return_tensors='''pt'''						)

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

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

           # verify area
           _lowerCamelCase			    =				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([75, 75, 63, 65, 17, 17]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           # prepare image, target and masks_path
           _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,				'''r'''						) as f:
                 _lowerCamelCase			    =				json.loads(f.read()						)

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

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

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor(format='''coco_panoptic'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				masks_path=A_				,				return_tensors='''pt'''						)

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

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

           # verify area
           _lowerCamelCase			    =				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([17, 17, 63, 75, 75, 93]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify masks
           _lowerCamelCase			    =				82_28_73
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,				A_						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)