





import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
a__:      Optional[int]									=		logging.get_logger(__name__)


a__:      Optional[Any]									=		['model.decoder.embed_positions.weights']


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any]  )->int:
       if "emb" in name:
              A__											=						name.replace('''emb''' ,     '''model.decoder.embed_tokens'''  )
       if "transformer" in name:
              A__											=						name.replace('''transformer''' ,     '''model.decoder'''  )
       if "cross_attention" in name:
              A__											=						name.replace('''cross_attention''' ,     '''encoder_attn'''  )
       if "linear1" in name:
              A__											=						name.replace('''linear1''' ,     '''fc1'''  )
       if "linear2" in name:
              A__											=						name.replace('''linear2''' ,     '''fc2'''  )
       if "norm1" in name:
              A__											=						name.replace('''norm1''' ,     '''self_attn_layer_norm'''  )
       if "norm_cross" in name:
              A__											=						name.replace('''norm_cross''' ,     '''encoder_attn_layer_norm'''  )
       if "norm2" in name:
              A__											=						name.replace('''norm2''' ,     '''final_layer_norm'''  )
       if "out_norm" in name:
              A__											=						name.replace('''out_norm''' ,     '''model.decoder.layer_norm'''  )
       if "linears" in name:
              A__											=						name.replace('''linears''' ,     '''lm_heads'''  )
       if "condition_provider.conditioners.description.output_proj" in name:
              A__											=						name.replace('''condition_provider.conditioners.description.output_proj''' ,     '''enc_to_dec_proj'''  )
       return name


def 	UpperCamelCase__(       UpperCamelCase__			:       OrderedDict ,     UpperCamelCase__			:       int  )->Tuple[Dict, Dict]:
       A__											=						list(state_dict.keys()  )
       A__											=						{}
       for key in keys:
              A__											=						state_dict.pop(UpperCamelCase__  )
              A__											=						rename_keys(UpperCamelCase__  )
              if "in_proj_weight" in key:
                     # split fused qkv proj
                     A__											=						val[:hidden_size, :]
                     A__											=						val[hidden_size : 2 * hidden_size, :]
                     A__											=						val[-hidden_size:, :]
              elif "enc_to_dec_proj" in key:
                     A__											=						val
              else:
                     A__											=						val
       return state_dict, enc_dec_proj_state_dict


def 	UpperCamelCase__(       UpperCamelCase__			:       str  )->MusicgenDecoderConfig:
       if checkpoint == "small":
              # default config values
              A__											=						10_24
              A__											=						24
              A__											=						16
       elif checkpoint == "medium":
              A__											=						15_36
              A__											=						48
              A__											=						24
       elif checkpoint == "large":
              A__											=						20_48
              A__											=						48
              A__											=						32
       else:
              raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."  )
       A__											=						MusicgenDecoderConfig(
           hidden_size=UpperCamelCase__ ,     ffn_dim=hidden_size * 4 ,     num_hidden_layers=UpperCamelCase__ ,     num_attention_heads=UpperCamelCase__ ,     )
       return config


@torch.no_grad()
def 	UpperCamelCase__(       UpperCamelCase__			:       Tuple ,     UpperCamelCase__			:       Union[str, Any]=None ,     UpperCamelCase__			:       List[Any]=None ,     UpperCamelCase__			:       Any="cpu"  )->Dict:
       A__											=						MusicGen.get_pretrained(UpperCamelCase__ ,     device=UpperCamelCase__  )
       A__											=						decoder_config_from_checkpoint(UpperCamelCase__  )

       A__											=						fairseq_model.lm.state_dict()
       A__					,	A__											=						rename_state_dict(
           UpperCamelCase__ ,     hidden_size=decoder_config.hidden_size  )

       A__											=						TaEncoderModel.from_pretrained('''t5-base'''  )
       A__											=						EncodecModel.from_pretrained('''facebook/encodec_32khz'''  )
       A__											=						MusicgenForCausalLM(UpperCamelCase__  ).eval()

       # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
       A__					,	A__											=						decoder.load_state_dict(UpperCamelCase__ ,     strict=UpperCamelCase__  )

       for key in missing_keys.copy():
              if key.startswith(('''text_encoder''', '''audio_encoder''')  ) or key in EXPECTED_MISSING_KEYS:
                     missing_keys.remove(UpperCamelCase__  )

       if len(UpperCamelCase__  ) > 0:
              raise ValueError(f"Missing key(s) in state_dict: {missing_keys}"  )

       if len(UpperCamelCase__  ) > 0:
              raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}"  )

       # init the composite model
       A__											=						MusicgenForConditionalGeneration(text_encoder=UpperCamelCase__ ,     audio_encoder=UpperCamelCase__ ,     decoder=UpperCamelCase__  )

       # load the pre-trained enc-dec projection (from the decoder state dict)
       model.enc_to_dec_proj.load_state_dict(UpperCamelCase__  )

       # check we can do a forward pass
       A__											=						torch.arange(0 ,     8 ,     dtype=torch.long  ).reshape(2 ,     -1  )
       A__											=						input_ids.reshape(2 * 4 ,     -1  )

       with torch.no_grad():
              A__											=						model(input_ids=UpperCamelCase__ ,     decoder_input_ids=UpperCamelCase__  ).logits

       if logits.shape != (8, 1, 20_48):
              raise ValueError('''Incorrect shape for logits'''  )

       # now construct the processor
       A__											=						AutoTokenizer.from_pretrained('''t5-base'''  )
       A__											=						AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' ,     padding_side='''left'''  )

       A__											=						MusicgenProcessor(feature_extractor=UpperCamelCase__ ,     tokenizer=UpperCamelCase__  )

       # set the appropriate bos/pad token ids
       A__											=						20_48
       A__											=						20_48

       # set other default generation config params
       A__											=						int(30 * audio_encoder.config.frame_rate  )
       A__											=						True
       A__											=						3.0

       if pytorch_dump_folder is not None:
              Path(UpperCamelCase__  ).mkdir(exist_ok=UpperCamelCase__  )
              logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}"  )
              model.save_pretrained(UpperCamelCase__  )
              processor.save_pretrained(UpperCamelCase__  )

       if repo_id:
              logger.info(f"Pushing model {checkpoint} to {repo_id}"  )
              model.push_to_hub(UpperCamelCase__  )
              processor.push_to_hub(UpperCamelCase__  )


if __name__ == "__main__":
       a__:      Dict									=		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--checkpoint',
           default='small',
           type=str,
           help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
       )
       parser.add_argument(
           '--pytorch_dump_folder',
           required=True,
           default=None,
           type=str,
           help='Path to the output PyTorch model directory.',
       )
       parser.add_argument(
           '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
       )
       parser.add_argument(
           '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
       )

       a__:      Optional[int]									=		parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)












import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

       if is_vision_available():
              from transformers import MaskaFormerImageProcessor

if is_vision_available():
       from PIL import Image




class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=2,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=10,__lowerCamelCase=3,__lowerCamelCase=32 * 8,__lowerCamelCase=32 * 8,__lowerCamelCase=4,__lowerCamelCase=64,):
        A__											=						parent
        A__											=						batch_size
        A__											=						is_training
        A__											=						use_auxiliary_loss
        A__											=						num_queries
        A__											=						num_channels
        A__											=						min_size
        A__											=						max_size
        A__											=						num_labels
        A__											=						hidden_dim
        A__											=						hidden_dim



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
            __lowerCamelCase )

        A__											=						torch.ones([self.batch_size, self.min_size, self.max_size],device=__lowerCamelCase )

        A__											=						(
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size],device=__lowerCamelCase ) > 0.5
        ).float()
        A__											=						(torch.rand((self.batch_size, self.num_labels),device=__lowerCamelCase ) > 0.5).long()

        A__											=						self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerConfig(
            hidden_size=self.hidden_dim,)
        A__											=						self.num_queries
        A__											=						self.num_labels

        A__											=						[1, 1, 1, 1]
        A__											=						self.num_channels

        A__											=						64
        A__											=						128
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        A__											=						self.hidden_dim
        return config



 def 				UpperCamelCase			(							self ):
        A__					,	A__					,	A__					,	A__					,	A__											=						self.prepare_config_and_inputs()
        A__											=						{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
        return config, inputs_dict



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						output.encoder_hidden_states
        A__											=						output.pixel_decoder_hidden_states
        A__											=						output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(__lowerCamelCase ),len(config.backbone_config.depths ) )
        self.parent.assertTrue(len(__lowerCamelCase ),len(config.backbone_config.depths ) )
        self.parent.assertTrue(len(__lowerCamelCase ),config.decoder_layers )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=False ):
        with torch.no_grad():
               A__											=						MaskaFormerModel(config=__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase,output_hidden_states=__lowerCamelCase )

        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape,(self.batch_size, self.num_queries, self.hidden_dim),)
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
        self.parent.assertTrue(output.encoder_last_hidden_state is not None )

        if output_hidden_states:
               self.check_output_hidden_state(__lowerCamelCase,__lowerCamelCase )




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						MaskaFormerForUniversalSegmentation(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()

        def comm_check_on_output(__lowerCamelCase ):
               # let's still check that all the required stuff is there
               self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
               self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
               self.parent.assertTrue(result.encoder_last_hidden_state is not None )
               # okay, now we need to check the logits shape
               # due to the encoder compression, masks have a //4 spatial size
               self.parent.assertEqual(
                   result.masks_queries_logits.shape,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4),)
               # + 1 for null class
               self.parent.assertEqual(
                   result.class_queries_logits.shape,(self.batch_size, self.num_queries, self.num_labels + 1) )

        with torch.no_grad():
               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase )

               comm_check_on_output(__lowerCamelCase )

               A__											=						model(
                   pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

        comm_check_on_output(__lowerCamelCase )

        self.parent.assertTrue(result.loss is not None )
        self.parent.assertEqual(result.loss.shape,torch.Size([1] ) )




@require_torch
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							       =	{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.config_tester.run_common_tests()



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase,**__lowerCamelCase,output_hidden_states=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowerCamelCase )



 @unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former is not a generative model''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''Mask2Former does not use token embeddings''' )
 def 				UpperCamelCase			(							self ):
        pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						model_class(__lowerCamelCase )
               A__											=						inspect.signature(model.forward )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               A__											=						[*signature.parameters.keys()]

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               A__											=						MaskaFormerModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						(self.model_tester.min_size,) * 2
        A__											=						{
            '''pixel_values''': torch.randn((2, 3, *size),device=__lowerCamelCase ),
            '''mask_labels''': torch.randn((2, 10, *size),device=__lowerCamelCase ),
            '''class_labels''': torch.zeros(2,10,device=__lowerCamelCase ).long(),
        }
        A__											=						self.model_tester.get_config()

        A__											=						MaskaFormerForUniversalSegmentation(__lowerCamelCase ).to(__lowerCamelCase )
        A__											=						model(**__lowerCamelCase )
        self.assertTrue(outputs.loss is not None )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase,**__lowerCamelCase,output_hidden_states=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
               A__											=						model(**__lowerCamelCase,output_attentions=__lowerCamelCase )
               self.assertTrue(outputs.attentions is not None )



 def 				UpperCamelCase			(							self ):
        if not self.model_tester.is_training:
               return

        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()

        A__											=						model_class(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase ).loss
        loss.backward()




 def 				UpperCamelCase			(							self ):
        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()
        A__											=						True
        A__											=						True

        A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

        A__											=						outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        A__											=						outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        A__											=						outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        A__											=						outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=__lowerCamelCase )

        self.assertIsNotNone(encoder_hidden_states.grad )
        self.assertIsNotNone(pixel_decoder_hidden_states.grad )
        self.assertIsNotNone(transformer_decoder_hidden_states.grad )
        self.assertIsNotNone(attentions.grad )
a__:      str									=		1e-4


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




@require_vision
@slow
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @cached_property
 def 				UpperCamelCase			(							self ):
        return "facebook/mask2former-swin-small-coco-instance"



 @cached_property
 def 				UpperCamelCase			(							self ):
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase )
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
        # check size
        self.assertEqual(__lowerCamelCase,(1, 3, 384, 384) )

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        A__											=						torch.tensor(
            [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )

        A__											=						torch.tensor(
            [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )

        A__											=						torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__lowerCamelCase )
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
        # check size
        self.assertEqual(__lowerCamelCase,(1, 3, 384, 384) )

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )
        # masks_queries_logits
        A__											=						outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
        A__											=						[
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        A__											=						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )
        # class_queries_logits
        A__											=						outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape,(1, model.config.num_queries, model.config.num_labels + 1) )
        A__											=						torch.tensor(
            [
                [1.8324, -8.0835, -4.1922],
                [0.8450, -9.0050, -3.6053],
                [0.3045, -7.7293, -3.0275],
            ] ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )




 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor

        A__											=						image_processor(
            [np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )],segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )],return_tensors='''pt''',)

        A__											=						inputs['''pixel_values'''].to(__lowerCamelCase )
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''mask_labels''']]
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''class_labels''']]

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        self.assertTrue(outputs.loss is not None )





