





import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase 				=     logging.get_logger(__name__)

set_seed(770)


_lowerCAmelCase 				=     {
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


_lowerCAmelCase 				=     {
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

_lowerCAmelCase 				=     os.path.dirname(os.path.abspath(__file__))
_lowerCAmelCase 				=     os.path.join(os.path.expanduser("""~"""), """.cache""")
_lowerCAmelCase 				=     os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				Optional[int]			      =	model_type
   if use_small:
      key += "_small"
   return os.path.join(_lowerCAmelCase  ,REMOTE_MODEL_PATHS[key]["""file_name"""]     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   os.makedirs(_lowerCAmelCase  ,exist_ok=_lowerCAmelCase     )
   hf_hub_download(repo_id=_lowerCAmelCase  ,filename=_lowerCAmelCase  ,local_dir=_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=False  ,_lowerCAmelCase="text"     ):
   '''simple docstring'''


   if model_type == "text":
      A_     :				Optional[Any]			      =	BarkSemanticModel
      A_     :				List[str]			      =	BarkSemanticConfig
      A_     :				Tuple			      =	BarkSemanticGenerationConfig
   elif model_type == "coarse":
      A_     :				Union[str, Any]			      =	BarkCoarseModel
      A_     :				Union[str, Any]			      =	BarkCoarseConfig
      A_     :				Any			      =	BarkCoarseGenerationConfig
   elif model_type == "fine":
      A_     :				str			      =	BarkFineModel
      A_     :				Optional[int]			      =	BarkFineConfig
      A_     :				List[Any]			      =	BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   A_     :				int			      =	f"""{model_type}_small""" if use_small else model_type
   A_     :				Tuple			      =	REMOTE_MODEL_PATHS[model_key]
   if not os.path.exists(_lowerCAmelCase     ):
      logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""     )
      _download(model_info["""repo_id"""]  ,model_info["""file_name"""]     )
   A_     :				Optional[int]			      =	torch.load(_lowerCAmelCase  ,map_location=_lowerCAmelCase     )
   # this is a hack
   A_     :				int			      =	checkpoint["""model_args"""]
   if "input_vocab_size" not in model_args:
      A_     :				List[str]			      =	model_args["""vocab_size"""]
      A_     :				Dict			      =	model_args["""vocab_size"""]
      del model_args["vocab_size"]

   # convert Bark model arguments to HF Bark model arguments
   A_     :				List[Any]			      =	model_args.pop("""n_head"""     )
   A_     :				str			      =	model_args.pop("""n_embd"""     )
   A_     :				Union[str, Any]			      =	model_args.pop("""n_layer"""     )

   A_     :				Optional[int]			      =	ConfigClass(**checkpoint["""model_args"""]     )
   A_     :				int			      =	ModelClass(config=_lowerCAmelCase     )
   A_     :				Any			      =	GenerationConfigClass()

   A_     :				List[Any]			      =	model_generation_config
   A_     :				Union[str, Any]			      =	checkpoint["""model"""]
   # fixup checkpoint
   A_     :				List[str]			      =	"""_orig_mod."""
   for k, v in list(state_dict.items()     ):
      if k.startswith(_lowerCAmelCase     ):
         # replace part of the key with corresponding layer name in HF implementation
         A_     :				str			      =	k[len(_lowerCAmelCase     ) :]
         for old_layer_name in new_layer_name_dict:
            A_     :				List[Any]			      =	new_k.replace(_lowerCAmelCase  ,new_layer_name_dict[old_layer_name]     )

         A_     :				Tuple			      =	state_dict.pop(_lowerCAmelCase     )

   A_     :				Optional[Any]			      =	set(state_dict.keys()     ) - set(model.state_dict().keys()     )
   A_     :				Optional[int]			      =	{k for k in extra_keys if not k.endswith(""".attn.bias"""     )}
   A_     :				int			      =	set(model.state_dict().keys()     ) - set(state_dict.keys()     )
   A_     :				int			      =	{k for k in missing_keys if not k.endswith(""".attn.bias"""     )}
   if len(_lowerCAmelCase     ) != 0:
      raise ValueError(f"""extra keys found: {extra_keys}"""     )
   if len(_lowerCAmelCase     ) != 0:
      raise ValueError(f"""missing keys: {missing_keys}"""     )
   model.load_state_dict(_lowerCAmelCase  ,strict=_lowerCAmelCase     )
   A_     :				Union[str, Any]			      =	model.num_parameters(exclude_embeddings=_lowerCAmelCase     )
   A_     :				str			      =	checkpoint["""best_val_loss"""].item()
   logger.info(f"""model loaded: {round(n_params/1e6  ,1     )}M params, {round(_lowerCAmelCase  ,3     )} loss"""     )
   model.eval()
   model.to(_lowerCAmelCase     )
   del checkpoint, state_dict

   return model



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False  ,_lowerCAmelCase="text"     ):
   '''simple docstring'''


   if model_type not in ("text", "coarse", "fine"):
      raise NotImplementedError()

   A_     :				Dict			      =	"""cpu"""  # do conversion on cpu

   A_     :				str			      =	_get_ckpt_path(_lowerCAmelCase  ,use_small=_lowerCAmelCase     )
   A_     :				Union[str, Any]			      =	_load_model(_lowerCAmelCase  ,_lowerCAmelCase  ,model_type=_lowerCAmelCase  ,use_small=_lowerCAmelCase     )

   # load bark initial model
   A_     :				Dict			      =	_bark_load_model(_lowerCAmelCase  ,"""cpu"""  ,model_type=_lowerCAmelCase  ,use_small=_lowerCAmelCase     )

   if model_type == "text":
      A_     :				List[Any]			      =	bark_model["""model"""]

   if model.num_parameters(exclude_embeddings=_lowerCAmelCase     ) != bark_model.get_num_params():
      raise ValueError("""initial and new models don't have the same number of parameters"""     )

   # check if same output as the bark model
   A_     :				Optional[Any]			      =	5
   A_     :				Any			      =	1_0

   if model_type in ["text", "coarse"]:
      A_     :				Any			      =	torch.randint(2_5_6  ,(batch_size, sequence_length)  ,dtype=torch.int     )
      A_     :				List[str]			      =	bark_model(_lowerCAmelCase     )[0]

      A_     :				Tuple			      =	model(_lowerCAmelCase     )

      # take last logits
      A_     :				Optional[Any]			      =	output_new_model_total.logits[:, [-1], :]

   else:
      A_     :				int			      =	3
      A_     :				Tuple			      =	8
      A_     :				Optional[int]			      =	torch.randint(2_5_6  ,(batch_size, sequence_length, n_codes_total)  ,dtype=torch.int     )

      A_     :				Dict			      =	model(_lowerCAmelCase  ,_lowerCAmelCase     )
      A_     :				Dict			      =	bark_model(_lowerCAmelCase  ,_lowerCAmelCase     )

      A_     :				Any			      =	output_new_model_total.logits

   # output difference should come from the difference of self-attention implementation design
   if output_new_model.shape != output_old_model.shape:
      raise ValueError("""initial and new outputs don't have the same shape"""     )
   if (output_new_model - output_old_model).abs().max().item() > 1e-3:
      raise ValueError("""initial and new outputs are not equal"""     )

   Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
   model.save_pretrained(_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	os.path.join(_lowerCAmelCase  ,_lowerCAmelCase     )

   A_     :				Union[str, Any]			      =	BarkSemanticConfig.from_pretrained(os.path.join(_lowerCAmelCase  ,"""config.json"""     )     )
   A_     :				Tuple			      =	BarkCoarseConfig.from_pretrained(os.path.join(_lowerCAmelCase  ,"""config.json"""     )     )
   A_     :				Dict			      =	BarkFineConfig.from_pretrained(os.path.join(_lowerCAmelCase  ,"""config.json"""     )     )
   A_     :				Optional[int]			      =	EncodecConfig.from_pretrained("""facebook/encodec_24khz"""     )

   A_     :				Tuple			      =	BarkSemanticModel.from_pretrained(_lowerCAmelCase     )
   A_     :				int			      =	BarkCoarseModel.from_pretrained(_lowerCAmelCase     )
   A_     :				List[str]			      =	BarkFineModel.from_pretrained(_lowerCAmelCase     )
   A_     :				str			      =	EncodecModel.from_pretrained("""facebook/encodec_24khz"""     )

   A_     :				Any			      =	BarkConfig.from_sub_model_configs(
       _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   A_     :				str			      =	BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config  ,coarseAcoustic.generation_config  ,fineAcoustic.generation_config     )

   A_     :				Optional[Any]			      =	BarkModel(_lowerCAmelCase     )

   A_     :				Dict			      =	semantic
   A_     :				List[str]			      =	coarseAcoustic
   A_     :				str			      =	fineAcoustic
   A_     :				int			      =	codec

   A_     :				Union[str, Any]			      =	bark_generation_config

   Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
   bark.save_pretrained(_lowerCAmelCase  ,repo_id=_lowerCAmelCase  ,push_to_hub=_lowerCAmelCase     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     argparse.ArgumentParser()
      # Required parameters

      parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
      parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

      _lowerCAmelCase 				=     parser.parse_args()

      load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)











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 DetaImageProcessor

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

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

         return expected_height, expected_width






@require_torch
@require_vision
class    _UpperCAmelCase							(		_lowerCamelCase			,       unittest.TestCase     ):
      a						       =							DetaImageProcessor if is_vision_available() else None
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	DetaImageProcessingTester(self       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	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__      ,       """do_rescale"""       )       )
         self.assertTrue(hasattr(a__      ,       """do_pad"""       )       )
         self.assertTrue(hasattr(a__      ,       """size"""       )       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	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      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         pass
      def 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				List[Any]			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_     :				Any			      =	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=a__       )
         for image in image_inputs:
            self.assertIsInstance(a__      ,       Image.Image       )

         # Test not batched input
         A_     :				List[Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Optional[int]			      =	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
         A_      ,				A_     :				Optional[Any]			      =	self.image_processor_tester.get_expected_values(a__      ,       batched=a__       )

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

         A_      ,				A_     :				Tuple			      =	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
         A_     :				Optional[int]			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

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

         A_      ,				A_     :				str			      =	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
         A_     :				str			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

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

         A_     :				Union[str, Any]			      =	{"""image_id""": 39769, """annotations""": target}

         # encode them
         A_     :				str			      =	DetaImageProcessor()
         A_     :				int			      =	image_processing(images=a__      ,       annotations=a__      ,       return_tensors="""pt"""       )

         # verify pixel values
         A_     :				List[str]			      =	torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["""pixel_values"""].shape      ,       a__       )

         A_     :				Tuple			      =	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
         A_     :				int			      =	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       a__       )       )
         # verify boxes
         A_     :				str			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[Any]			      =	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
         A_     :				Optional[Any]			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Optional[int]			      =	torch.tensor([75, 75, 63, 65, 17, 17]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify orig_size
         A_     :				Dict			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Tuple			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






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

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

         A_     :				Tuple			      =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

         # encode them
         A_     :				str			      =	DetaImageProcessor(format="""coco_panoptic"""       )
         A_     :				Tuple			      =	image_processing(images=a__      ,       annotations=a__      ,       masks_path=a__      ,       return_tensors="""pt"""       )

         # verify pixel values
         A_     :				str			      =	torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["""pixel_values"""].shape      ,       a__       )

         A_     :				Tuple			      =	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
         A_     :				Dict			      =	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       a__       )       )
         # verify boxes
         A_     :				List[Any]			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[str]			      =	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
         A_     :				int			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Union[str, Any]			      =	torch.tensor([17, 17, 63, 75, 75, 93]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify masks
         A_     :				Tuple			      =	822873
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,       a__       )
         # verify orig_size
         A_     :				Union[str, Any]			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Union[str, Any]			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






