






'''simple docstring'''

import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__     =       2






class     a_						:

  def __init__(				self      ,		*,  # begin keyword-only arguments
      _SCREAMING_SNAKE_CASE="<s>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE="</s>"      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE=None      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = bos, unk, pad, eos
        UpperCamelCase      = []
        UpperCamelCase      = []
        UpperCamelCase      = {}
        UpperCamelCase      = self.add_symbol(lowerCAmelCase__				)
        UpperCamelCase      = self.add_symbol(lowerCAmelCase__				)
        UpperCamelCase      = self.add_symbol(lowerCAmelCase__				)
        UpperCamelCase      = self.add_symbol(lowerCAmelCase__				)
        if extra_special_symbols:
              for s in extra_special_symbols:
                    self.add_symbol(lowerCAmelCase__				)
        UpperCamelCase      = len(self.symbols				)

  def __eq__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






        return self.indices == other.indices

  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        if idx < len(self.symbols				):
              return self.symbols[idx]
        return self.unk_word

  def __len__(				self				)  ->				int:



        """simple docstring"""






        return len(self.symbols				)

  def __contains__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        return sym in self.indices

  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = cls()
        d.add_from_file(lowerCAmelCase__				)
        return d

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Dict:



        """simple docstring"""






        if word in self.indices and not overwrite:
              UpperCamelCase      = self.indices[word]
              UpperCamelCase      = self.count[idx] + n
              return idx
        else:
              UpperCamelCase      = len(self.symbols				)
              UpperCamelCase      = idx
              self.symbols.append(lowerCAmelCase__				)
              self.count.append(lowerCAmelCase__				)
              return idx

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return 0


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        if isinstance(lowerCAmelCase__      ,		lowerCAmelCase__				):
              try:
                    with open(lowerCAmelCase__      ,		"""r"""      ,		encoding="""utf-8"""				) as fd:
                          self.add_from_file(lowerCAmelCase__				)
              except FileNotFoundError as fnfe:
                    raise fnfe
              except UnicodeError:
                    raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(lowerCAmelCase__				)				)
              return

        UpperCamelCase      = f.readlines()
        UpperCamelCase      = self._load_meta(lowerCAmelCase__				)

        for line in lines[indices_start_line:]:
              try:
                    UpperCamelCase      = line.rstrip().rsplit(""" """      ,		1				)
                    if field == "#fairseq:overwrite":
                          UpperCamelCase      = True
                          UpperCamelCase      = line.rsplit(""" """      ,		1				)
                    else:
                          UpperCamelCase      = False
                    UpperCamelCase      = int(lowerCAmelCase__				)
                    UpperCamelCase      = line
                    if word in self and not overwrite:
                          raise RuntimeError(
                              """Duplicate word found when loading Dictionary: '{}'. """
                              """Duplicate words can overwrite earlier ones by adding the """
                              """#fairseq:overwrite flag at the end of the corresponding row """
                              """in the dictionary file. If using the Camembert model, please """
                              """download an updated copy of the model file.""".format(lowerCAmelCase__				)				)
                    self.add_symbol(lowerCAmelCase__      ,		n=lowerCAmelCase__      ,		overwrite=lowerCAmelCase__				)
              except ValueError:
                    raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""				)







def    lowercase__			(				__UpperCamelCase   )->			Any:
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      UpperCamelCase      = dict((re.sub(R"""@@$"""		,						""""""		,						a_   ), v) if k.endswith("""@@"""   ) else (re.sub(R"""$"""		,						"""</w>"""		,						a_   ), v) for k, v in d.items()   )
      UpperCamelCase      = "<s> <pad> </s> <unk>".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F"{k}</w>"]
            UpperCamelCase      = d[k]  # restore
      return da







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			int:
      # prep
      if not os.path.exists(a_   ):
            raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"   )
      os.makedirs(a_		,						exist_ok=a_   )
      print(F"Writing results to {pytorch_dump_folder_path}"   )

      # handle various types of models

      UpperCamelCase      = os.path.join(a_		,						"""checkpoint.pt"""   )
      if not os.path.isfile(a_   ):
            raise ValueError(F"path to the file {checkpoint_file} does not exist!"   )
      UpperCamelCase      = torch.load(a_		,						map_location="""cpu"""   )

      UpperCamelCase      = chkpt["cfg"]["model"]

      # dicts
      UpperCamelCase      = os.path.join(a_		,						"""dict.txt"""   )
      if not os.path.isfile(a_   ):
            raise ValueError(F"path to the file {dict_file} does not exist!"   )
      UpperCamelCase      = Dictionary.load(a_   )
      UpperCamelCase      = rewrite_dict_keys(src_dict.indices   )
      UpperCamelCase      = len(a_   )
      UpperCamelCase      = os.path.join(a_		,						VOCAB_FILES_NAMES["""vocab_file"""]   )
      print(F"Generating {src_vocab_file} of {src_vocab_size} records"   )
      with open(a_		,						"""w"""		,						encoding="""utf-8"""   ) as f:
            f.write(json.dumps(a_		,						ensure_ascii=a_		,						indent=a_   )   )

      # merges_file (bpecodes)
      UpperCamelCase      = os.path.join(a_		,						"""bpecodes"""   )
      if not os.path.isfile(a_   ):
            raise ValueError(F"path to the file {bpecodes_file} does not exist!"   )

      UpperCamelCase      = os.path.join(a_		,						VOCAB_FILES_NAMES["""merges_file"""]   )
      shutil.copyfile(a_		,						a_   )

      # model config
      UpperCamelCase      = os.path.join(a_		,						"""config.json"""   )

      UpperCamelCase      = {
          "activation_dropout": args["activation_dropout"],
          "architectures": ["BioGptForCausalLM"],
          "attention_probs_dropout_prob": args["attention_dropout"],
          "bos_token_id": 0,
          "eos_token_id": 2,
          "hidden_act": args["activation_fn"],
          "hidden_dropout_prob": args["dropout"],
          "hidden_size": args["decoder_embed_dim"],
          "initializer_range": 0.02,
          "intermediate_size": args["decoder_ffn_embed_dim"],
          "layer_norm_eps": 1E-12,
          "layerdrop": args["decoder_layerdrop"],
          "max_position_embeddings": args["max_target_positions"],
          "model_type": "biogpt",
          "num_attention_heads": args["decoder_attention_heads"],
          "num_hidden_layers": args["decoder_layers"],
          "pad_token_id": 1,
          "scale_embedding": not args["no_scale_embedding"],
          "tie_word_embeddings": args["share_decoder_input_output_embed"],
          "vocab_size": src_vocab_size,
      }

      # good hparam defaults to start with

      print(F"Generating {biogpt_model_config_file}"   )
      with open(a_		,						"""w"""		,						encoding="""utf-8"""   ) as f:
            f.write(json.dumps(a_		,						ensure_ascii=a_		,						indent=a_   )   )

      # tokenizer config
      UpperCamelCase      = os.path.join(a_		,						a_   )

      UpperCamelCase      = {
          "bos_token": "<s>",
          "eos_token": "</s>",
          "model_max_length": 1024,
          "pad_token": "<pad>",
          "special_tokens_map_file": None,
          "tokenizer_class": "BioGptTokenizer",
          "unk_token": "<unk>",
      }

      print(F"Generating {biogpt_tokenizer_config_file}"   )
      with open(a_		,						"""w"""		,						encoding="""utf-8"""   ) as f:
            f.write(json.dumps(a_		,						ensure_ascii=a_		,						indent=a_   )   )

      # model
      UpperCamelCase      = chkpt["model"]

      # remove unneeded keys
      UpperCamelCase      = [
          "decoder.version",
      ]
      for k in ignore_keys:
            model_state_dict.pop(a_		,						a_   )

      UpperCamelCase      = list(model_state_dict.keys()   )
      for layer_name in layer_names:
            if layer_name.endswith("""output_projection.weight"""   ):
                  UpperCamelCase      = model_state_dict.pop(a_   )
            else:
                  UpperCamelCase      = model_state_dict.pop(a_   )

      UpperCamelCase      = BioGptConfig.from_pretrained(a_   )
      UpperCamelCase      = BioGptForCausalLM(a_   )

      # check that it loads ok
      model_new.load_state_dict(a_   )

      # save
      UpperCamelCase      = os.path.join(a_		,						a_   )
      print(F"Generating {pytorch_weights_dump_path}"   )
      torch.save(a_		,						a_   )

      print("""Conversion is done!"""   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--biogpt_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help=(
             'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
             ' bpecodes, etc.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)








'''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 ConditionalDetrImageProcessor






class     a_						(      unittest.TestCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=1 / 255      ,		_SCREAMING_SNAKE_CASE=True      ,		)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = min_resolution
        UpperCamelCase      = max_resolution
        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = do_normalize
        UpperCamelCase      = image_mean
        UpperCamelCase      = image_std
        UpperCamelCase      = do_rescale
        UpperCamelCase      = rescale_factor
        UpperCamelCase      = do_pad

  def   A__						(				self				)  ->				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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Optional[int]:



        """simple docstring"""






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

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

        return expected_height, expected_width






@require_torch
@require_vision
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				ConditionalDetrImageProcessor if is_vision_available() else None

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



        """simple docstring"""






        UpperCamelCase      = ConditionalDetrImageProcessingTester(self				)

  @property
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_mean"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_std"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_normalize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_resize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = 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      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.image_processing_class.from_dict(
            self.image_processor_dict      ,		size=42      ,		max_size=84      ,		pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE				)
        self.assertEqual(image_processor.size      ,		{"""shortest_edge""": 42, """longest_edge""": 84}				)
        self.assertEqual(image_processor.do_pad      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        pass

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



        """simple docstring"""






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

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processing(_SCREAMING_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				)  ->				Optional[int]:



        """simple docstring"""






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

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_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				)  ->				List[str]:



        """simple docstring"""






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

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_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				)  ->				Any:



        """simple docstring"""






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

        UpperCamelCase      = {"""image_id""": 39769, """annotations""": target}

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([75, 75, 63, 65, 17, 17]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)


  @slow
  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






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

        UpperCamelCase      = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

        UpperCamelCase      = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor(format="""coco_panoptic"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		masks_path=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([17, 17, 63, 75, 75, 93]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify masks
        UpperCamelCase      = 822873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,		_SCREAMING_SNAKE_CASE				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
