






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:List[str]        =      UNetaDModel(
								    sample_size=(3_2, 6_4)       ,					in_channels=1       ,					out_channels=1       ,					layers_per_block=2       ,					block_out_channels=(1_2_8, 1_2_8)       ,					down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')       ,					up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')       ,					)
								return model







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:Optional[Any]        =      UNetaDConditionModel(
								    sample_size=(6_4, 3_2)       ,					in_channels=1       ,					out_channels=1       ,					layers_per_block=2       ,					block_out_channels=(1_2_8, 1_2_8)       ,					down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')       ,					up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')       ,					cross_attention_dim=1_0       ,					)
								return model







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:Dict        =      AutoencoderKL(
								    sample_size=(1_2_8, 6_4)       ,					in_channels=1       ,					out_channels=1       ,					latent_channels=1       ,					layers_per_block=2       ,					block_out_channels=(1_2_8, 1_2_8)       ,					down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')       ,					up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')       ,					)
								__magic_name__		:int        =      UNetaDModel(
								    sample_size=(6_4, 3_2)       ,					in_channels=1       ,					out_channels=1       ,					layers_per_block=2       ,					block_out_channels=(1_2_8, 1_2_8)       ,					down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')       ,					up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')       ,					)
								return vqvae, unet







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
								__magic_name__		:List[str]        =      Mel(
								    x_res=self.dummy_unet.config.sample_size[1]       ,					y_res=self.dummy_unet.config.sample_size[0]       ,					)

								__magic_name__		:List[str]        =      DDPMScheduler()
								__magic_name__		:Dict        =      AudioDiffusionPipeline(vqvae=_a       ,					unet=self.dummy_unet       ,					mel=_a       ,					scheduler=_a     )
								__magic_name__		:Optional[int]        =      pipe.to(_a     )
								pipe.set_progress_bar_config(disable=_a     )

								__magic_name__		:Optional[int]        =      torch.Generator(device=_a     ).manual_seed(4_2     )
								__magic_name__		:Union[str, Any]        =      pipe(generator=_a       ,					steps=4     )
								__magic_name__		:int        =      output.audios[0]
								__magic_name__		:Dict        =      output.images[0]

								__magic_name__		:Any        =      torch.Generator(device=_a     ).manual_seed(4_2     )
								__magic_name__		:Tuple        =      pipe(generator=_a       ,					steps=4       ,					return_dict=_a     )
								__magic_name__		:List[Any]        =      output[0][0]

								assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
								assert (
								    image.height == self.dummy_unet.config.sample_size[0]
								    and image.width == self.dummy_unet.config.sample_size[1]
								)
								__magic_name__		:List[str]        =      np.frombuffer(image.tobytes()       ,					dtype='''uint8'''     )[:1_0]
								__magic_name__		:Union[str, Any]        =      np.frombuffer(image_from_tuple.tobytes()       ,					dtype='''uint8'''     )[:1_0]
								__magic_name__		:List[Any]        =      np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7]     )

								assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() == 0

								__magic_name__		:str        =      Mel(
								    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]       ,					y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]       ,					)

								__magic_name__		:Any        =      DDIMScheduler()
								__magic_name__		:Tuple        =      self.dummy_vqvae_and_unet
								__magic_name__		:List[str]        =      AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0]       ,					unet=dummy_vqvae_and_unet[1]       ,					mel=_a       ,					scheduler=_a     )
								__magic_name__		:List[Any]        =      pipe.to(_a     )
								pipe.set_progress_bar_config(disable=_a     )

								np.random.seed(0     )
								__magic_name__		:Dict        =      np.random.uniform(-1       ,					1       ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)     )
								__magic_name__		:int        =      torch.Generator(device=_a     ).manual_seed(4_2     )
								__magic_name__		:int        =      pipe(raw_audio=_a       ,					generator=_a       ,					start_step=5       ,					steps=1_0     )
								__magic_name__		:Dict        =      output.images[0]

								assert (
								    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
								    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
								)
								__magic_name__		:Optional[Any]        =      np.frombuffer(image.tobytes()       ,					dtype='''uint8'''     )[:1_0]
								__magic_name__		:List[Any]        =      np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1]     )

								assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0

								__magic_name__		:List[Any]        =      self.dummy_unet_condition
								__magic_name__		:List[Any]        =      AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0]       ,					unet=_a       ,					mel=_a       ,					scheduler=_a     )
								__magic_name__		:List[str]        =      pipe.to(_a     )
								pipe.set_progress_bar_config(disable=_a     )

								np.random.seed(0     )
								__magic_name__		:List[str]        =      torch.rand((1, 1, 1_0)     )
								__magic_name__		:Tuple        =      pipe(generator=_a       ,					encoding=_a     )
								__magic_name__		:Tuple        =      output.images[0]
								__magic_name__		:Optional[int]        =      np.frombuffer(image.tobytes()       ,					dtype='''uint8'''     )[:1_0]
								__magic_name__		:int        =      np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1]     )

								assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0







@slow
@require_torch_gpu
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      torch_device

								__magic_name__		:Union[str, Any]        =      DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''     )
								__magic_name__		:Optional[int]        =      pipe.to(_a     )
								pipe.set_progress_bar_config(disable=_a     )

								__magic_name__		:List[str]        =      torch.Generator(device=_a     ).manual_seed(4_2     )
								__magic_name__		:Dict        =      pipe(generator=_a     )
								__magic_name__		:Optional[int]        =      output.audios[0]
								__magic_name__		:Optional[Any]        =      output.images[0]

								assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
								assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
								__magic_name__		:Union[str, Any]        =      np.frombuffer(image.tobytes()       ,					dtype='''uint8'''     )[:1_0]
								__magic_name__		:Any        =      np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6]     )

								assert np.abs(image_slice.flatten() - expected_slice     ).max() == 0














import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      :			int							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			Optional[int]							=		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				for attribute in key.split('''.''' ):
								__magic_name__		:List[Any]        =      getattr(snake_case,  snake_case )

				if weight_type is not None:
								__magic_name__		:Union[str, Any]        =      getattr(snake_case,  snake_case ).shape
				else:
								__magic_name__		:List[Any]        =      hf_pointer.shape

				assert hf_shape == value.shape, (
				    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''
				)

				if weight_type == "weight":
								__magic_name__		:Optional[Any]        =      value
				elif weight_type == "weight_g":
								__magic_name__		:Dict        =      value
				elif weight_type == "weight_v":
								__magic_name__		:Any        =      value
				elif weight_type == "bias":
								__magic_name__		:List[Any]        =      value
				else:
								__magic_name__		:Union[str, Any]        =      value

				logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      []
				__magic_name__		:Dict        =      fairseq_model.state_dict()

				__magic_name__		:Union[str, Any]        =      hf_model.feature_extractor
				__magic_name__		:Optional[int]        =      hf_model.adapter

				for name, value in fairseq_dict.items():
								__magic_name__		:List[Any]        =      False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case,  snake_case,  snake_case,  snake_case,  hf_model.config.feat_extract_norm == '''group''',  )
												__magic_name__		:List[str]        =      True
								elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
												load_adapter(snake_case,  snake_case,  snake_case,  snake_case )
												__magic_name__		:List[str]        =      True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
																				__magic_name__		:str        =      True
																				if "*" in mapped_key:
																								__magic_name__		:Tuple        =      name.split(snake_case )[0].split('''.''' )[-2]
																								__magic_name__		:Optional[int]        =      mapped_key.replace('''*''',  snake_case )
																				if "weight_g" in name:
																								__magic_name__		:Union[str, Any]        =      '''weight_g'''
																				elif "weight_v" in name:
																								__magic_name__		:int        =      '''weight_v'''
																				elif "bias" in name:
																								__magic_name__		:Optional[int]        =      '''bias'''
																				elif "weight" in name:
																								__magic_name__		:Optional[int]        =      '''weight'''
																				else:
																								__magic_name__		:Union[str, Any]        =      None
																				set_recursively(snake_case,  snake_case,  snake_case,  snake_case,  snake_case )
																continue
								if not is_used:
												unused_weights.append(snake_case )

				logger.warning(f'''Unused weights: {unused_weights}''' )

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      full_name.split('''conv_layers.''' )[-1]
				__magic_name__		:List[str]        =      name.split('''.''' )
				__magic_name__		:Optional[int]        =      int(items[0] )
				__magic_name__		:Dict        =      int(items[1] )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
												)
												__magic_name__		:Tuple        =      value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
												)
												__magic_name__		:List[Any]        =      value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
												    " found."
												)
												__magic_name__		:Dict        =      value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
												)
												__magic_name__		:Any        =      value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
				else:
								unused_weights.append(snake_case )

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      full_name.split('''adaptor.''' )[-1]
				__magic_name__		:Optional[Any]        =      name.split('''.''' )

				if items[1].isdigit():
								__magic_name__		:Tuple        =      int(items[1] )
				else:
								__magic_name__		:str        =      None

				if "adaptor" not in full_name:
								if "proj_ln" in full_name:
												# has to be layer norm
												if "bias" in name:
																assert (
																    value.shape == adapter.proj_layer_norm.bias.data.shape
																), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
																__magic_name__		:Optional[int]        =      value
																logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.''' )
												if "weight" in name:
																assert (
																    value.shape == adapter.proj_layer_norm.weight.data.shape
																), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
																__magic_name__		:Any        =      value
								else:
												# has to be projection layer
												if "bias" in name:
																assert (
																    value.shape == adapter.proj.bias.data.shape
																), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
																__magic_name__		:List[Any]        =      value
																logger.info(f'''Adapter proj layer bias was initialized from {full_name}.''' )
												if "weight" in name:
																assert (
																    value.shape == adapter.proj.weight.data.shape
																), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
																__magic_name__		:Union[str, Any]        =      value
																logger.info(f'''Adapter proj layer weight was initialized from {full_name}.''' )
				elif isinstance(snake_case,  snake_case ):
								if "bias" in name:
												assert (
												    value.shape == adapter.layers[layer_id].conv.bias.data.shape
												), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
												__magic_name__		:Tuple        =      value
												logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
								elif "weight" in name:
												assert (
												    value.shape == adapter.layers[layer_id].conv.weight.data.shape
												), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
												__magic_name__		:Optional[int]        =      value
												logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
				else:
								unused_weights.append(snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:int        =      emb.weight.shape
				__magic_name__		:Tuple        =      nn.Linear(snake_case,  snake_case,  bias=snake_case )
				__magic_name__		:Optional[int]        =      emb.weight.data
				return lin_layer

@torch.no_grad()
def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  ):







				"""simple docstring"""






				__magic_name__		:Any        =      WavaVecaConfig.from_pretrained(
				    snake_case,  add_adapter=snake_case,  adapter_stride=snake_case,  adapter_kernel_size=snake_case,  use_auth_token=snake_case,  output_hidden_size=snake_case,  )
				__magic_name__		:Union[str, Any]        =      MBartConfig.from_pretrained(snake_case )

				# load model
				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Tuple        =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path],  arg_overrides={
				        '''config_yaml''': config_yaml_path,
				        '''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
				        '''w2v_path''': checkpoint_path,
				        '''load_pretrained_decoder_from''': None,
				    },  )
				__magic_name__		:Tuple        =      model[0].eval()

				# load feature extractor
				__magic_name__		:Optional[Any]        =      WavaVecaFeatureExtractor.from_pretrained(snake_case,  use_auth_token=snake_case )

				# set weights for wav2vec2 encoder
				__magic_name__		:str        =      WavaVecaModel(snake_case )

				recursively_load_weights_wavaveca(model.encoder,  snake_case )

				# load decoder weights
				__magic_name__		:int        =      MBartForCausalLM(snake_case )
				__magic_name__     ,				__magic_name__		:int        =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),  strict=snake_case )
				logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
				logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )

				__magic_name__		:Tuple        =      SpeechEncoderDecoderModel(encoder=snake_case,  decoder=snake_case )
				__magic_name__		:int        =      False

				__magic_name__		:int        =      MBartaaTokenizer(snake_case )
				tokenizer.save_pretrained(snake_case )

				__magic_name__		:Union[str, Any]        =      hf_wavavec.config.to_dict()
				__magic_name__		:Any        =      tokenizer.pad_token_id
				__magic_name__		:List[Any]        =      tokenizer.bos_token_id
				__magic_name__		:List[Any]        =      tokenizer.eos_token_id
				__magic_name__		:Any        =      '''mbart50'''
				__magic_name__		:Any        =      '''wav2vec2'''

				__magic_name__		:Dict        =      tokenizer.eos_token_id
				__magic_name__		:Optional[int]        =      2_5_0_0_0_4
				__magic_name__		:List[Any]        =      tokenizer.eos_token_id

				__magic_name__		:int        =      SpeechEncoderDecoderConfig.from_dict(snake_case )

				hf_wavavec.save_pretrained(snake_case )
				feature_extractor.save_pretrained(snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Any							=		argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
					parser.add_argument(
					    """--encoder_config_path""",
					    default="""facebook/wav2vec2-xls-r-1b""",
					    type=str,
					    help="""Path to hf encoder wav2vec2 checkpoint config""",
					)
					parser.add_argument(
					    """--decoder_config_path""",
					    default="""facebook/mbart-large-50-one-to-many-mmt""",
					    type=str,
					    help="""Path to hf decoder checkpoint config""",
					)
					parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
					parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
					parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
					parser.add_argument("""--encoder_output_dim""", default=10_24, type=int, help="""encoder output dim""")
					parser.add_argument("""--start_token_id""", default=25_00_04, type=int, help="""`decoder_start_token_id` of model config""")

					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    args.config_yaml_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    add_adapter=args.add_adapter,
					    adapter_kernel_size=args.adapter_kernel_size,
					    adapter_stride=args.adapter_stride,
					    decoder_start_token_id=args.start_token_id,
					    encoder_output_dim=args.encoder_output_dim,
					)






