
'''simple docstring'''


import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


snake_case_ :  Union[str, Any]     =					'base_with_context'



def  __snake_case     (    _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']))
   UpperCamelCase       =  nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']),      requires_grad=_UpperCAmelCase)
   for lyr_num, lyr in enumerate(model.encoders):
      UpperCamelCase       =  weights[f'layers_{lyr_num}']
      UpperCamelCase       =  nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']))

      UpperCamelCase       =  ly_weight['''attention''']
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T))

      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']))

      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T))

   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']))
   return model



def  __snake_case     (    _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       int):
   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T))

   UpperCamelCase       =  nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']),      requires_grad=_UpperCAmelCase)

   for lyr_num, lyr in enumerate(model.encoders):
      UpperCamelCase       =  weights[f'layers_{lyr_num}']
      UpperCamelCase       =  ly_weight['''attention''']

      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']))

      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']))

   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']))

   return model



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Dict):
   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T))
   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T))

   UpperCamelCase       =  nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']),      requires_grad=_UpperCAmelCase)

   UpperCamelCase       =  nn.Parameter(
       torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T))

   for lyr_num, lyr in enumerate(model.decoders):
      UpperCamelCase       =  weights[f'layers_{lyr_num}']
      UpperCamelCase       =  nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']))

      UpperCamelCase       =  nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T))

      UpperCamelCase       =  ly_weight['''self_attention''']
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T))

      UpperCamelCase       =  ly_weight['''MultiHeadDotProductAttention_0''']
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']))

      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']))
      UpperCamelCase       =  nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T))
      UpperCamelCase       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T))

   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']))

   UpperCamelCase       =  nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T))

   return model



def  __snake_case     (    _UpperCAmelCase					:       Optional[Any]):
   UpperCamelCase       =  checkpoints.load_tax_checkpoint(args.checkpoint_path)
   UpperCamelCase       =  jnp.tree_util.tree_map(onp.array,      _UpperCAmelCase)

   UpperCamelCase       =  [
       '''from __gin__ import dynamic_registration''',
       '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
       '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
       '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
   ]

   UpperCamelCase       =  os.path.join(args.checkpoint_path,      '''..''',      '''config.gin''')
   UpperCamelCase       =  inference.parse_training_gin_file(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  inference.InferenceModel(args.checkpoint_path,      _UpperCAmelCase)

   UpperCamelCase       =  DDPMScheduler(beta_schedule='''squaredcos_cap_v2''',      variance_type='''fixed_large''')

   UpperCamelCase       =  SpectrogramNotesEncoder(
       max_length=synth_model.sequence_length['''inputs'''],      vocab_size=synth_model.model.module.config.vocab_size,      d_model=synth_model.model.module.config.emb_dim,      dropout_rate=synth_model.model.module.config.dropout_rate,      num_layers=synth_model.model.module.config.num_encoder_layers,      num_heads=synth_model.model.module.config.num_heads,      d_kv=synth_model.model.module.config.head_dim,      d_ff=synth_model.model.module.config.mlp_dim,      feed_forward_proj='''gated-gelu''',      )

   UpperCamelCase       =  SpectrogramContEncoder(
       input_dims=synth_model.audio_codec.n_dims,      targets_context_length=synth_model.sequence_length['''targets_context'''],      d_model=synth_model.model.module.config.emb_dim,      dropout_rate=synth_model.model.module.config.dropout_rate,      num_layers=synth_model.model.module.config.num_encoder_layers,      num_heads=synth_model.model.module.config.num_heads,      d_kv=synth_model.model.module.config.head_dim,      d_ff=synth_model.model.module.config.mlp_dim,      feed_forward_proj='''gated-gelu''',      )

   UpperCamelCase       =  TaFilmDecoder(
       input_dims=synth_model.audio_codec.n_dims,      targets_length=synth_model.sequence_length['''targets_context'''],      max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time,      d_model=synth_model.model.module.config.emb_dim,      num_layers=synth_model.model.module.config.num_decoder_layers,      num_heads=synth_model.model.module.config.num_heads,      d_kv=synth_model.model.module.config.head_dim,      d_ff=synth_model.model.module.config.mlp_dim,      dropout_rate=synth_model.model.module.config.dropout_rate,      )

   UpperCamelCase       =  load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''],      _UpperCAmelCase)
   UpperCamelCase       =  load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],      _UpperCAmelCase)
   UpperCamelCase       =  load_decoder(ta_checkpoint['''target''']['''decoder'''],      _UpperCAmelCase)

   UpperCamelCase       =  OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder''')

   UpperCamelCase       =  SpectrogramDiffusionPipeline(
       notes_encoder=_UpperCAmelCase,      continuous_encoder=_UpperCAmelCase,      decoder=_UpperCAmelCase,      scheduler=_UpperCAmelCase,      melgan=_UpperCAmelCase,      )
   if args.save:
      pipe.save_pretrained(args.output_path)


if __name__ == "__main__":
   snake_case_ :  Optional[int]     =					argparse.ArgumentParser()

   parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
   parser.add_argument(
       '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
   )
   parser.add_argument(
       '--checkpoint_path',
       default=F'''{MODEL}/checkpoint_500000''',
       type=str,
       required=False,
       help='Path to the original jax model checkpoint.',
   )
   snake_case_ :  List[Any]     =					parser.parse_args()

   main(args)







'''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 DeformableDetrImageProcessor


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=True ,   lowerCamelCase__=1 / 2_5_5 ,   lowerCamelCase__=True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
      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       UpperCAmelCase				(			self   ):

      '''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 ,   lowerCamelCase__ ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      if not batched:
         UpperCamelCase       =  image_inputs[0]
         if isinstance(lowerCamelCase__ ,   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(lowerCamelCase__ ,   key=lambda lowerCamelCase__   : item[0]   )[0]
         UpperCamelCase       =  max(lowerCamelCase__ ,   key=lambda lowerCamelCase__   : item[1]   )[1]

      return expected_height, expected_width




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							DeformableDetrImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  DeformableDetrImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}   )
      self.assertEqual(image_processor.do_pad ,   lowerCamelCase__   )

      UpperCamelCase       =  self.image_processing_class.from_dict(
          self.image_processor_dict ,   size=4_2 ,   max_size=8_4 ,   pad_and_return_pixel_mask=lowerCamelCase__   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}   )
      self.assertEqual(image_processor.do_pad ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''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=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   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(lowerCamelCase__   )

      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(lowerCamelCase__ ,   batched=lowerCamelCase__   )

      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   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   ):

      '''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=lowerCamelCase__ ,   numpify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   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(lowerCamelCase__   )

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

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )

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







   def       UpperCAmelCase				(			self   ):

      '''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=lowerCamelCase__ ,   torchify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   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(lowerCamelCase__   )

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

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )

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







   @slow
   def       UpperCAmelCase				(			self   ):

      '''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''': 3_9_7_6_9, '''annotations''': target}

      # encode them
      UpperCamelCase       =  DeformableDetrImageProcessor()
      UpperCamelCase       =  image_processing(images=lowerCamelCase__ ,   annotations=lowerCamelCase__ ,   return_tensors='''pt'''   )

      # verify pixel values
      UpperCamelCase       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
      self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase__   )

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

      # verify area
      UpperCamelCase       =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase__   )   )
      # verify boxes
      UpperCamelCase       =  torch.Size([6, 4]   )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase__   )
      UpperCamelCase       =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowerCamelCase__ ,   atol=1e-3   )   )
      # verify image_id
      UpperCamelCase       =  torch.tensor([3_9_7_6_9]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase__   )   )
      # verify is_crowd
      UpperCamelCase       =  torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase__   )   )
      # verify class_labels
      UpperCamelCase       =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase__   )   )
      # verify orig_size
      UpperCamelCase       =  torch.tensor([4_8_0, 6_4_0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase__   )   )
      # verify size
      UpperCamelCase       =  torch.tensor([8_0_0, 1_0_6_6]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase__   )   )







   @slow
   def       UpperCAmelCase				(			self   ):

      '''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''': 3_9_7_6_9, '''segments_info''': target}

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

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

      # verify pixel values
      UpperCamelCase       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
      self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase__   )

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

      # verify area
      UpperCamelCase       =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase__   )   )
      # verify boxes
      UpperCamelCase       =  torch.Size([6, 4]   )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase__   )
      UpperCamelCase       =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowerCamelCase__ ,   atol=1e-3   )   )
      # verify image_id
      UpperCamelCase       =  torch.tensor([3_9_7_6_9]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase__   )   )
      # verify is_crowd
      UpperCamelCase       =  torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase__   )   )
      # verify class_labels
      UpperCamelCase       =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase__   )   )
      # verify masks
      UpperCamelCase       =  8_2_2_8_7_3
      self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,   lowerCamelCase__   )
      # verify orig_size
      UpperCamelCase       =  torch.tensor([4_8_0, 6_4_0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase__   )   )
      # verify size
      UpperCamelCase       =  torch.tensor([8_0_0, 1_0_6_6]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase__   )   )





