





'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A__				:   List[Any]      =logging.get_logger(__name__)




class  UpperCAmelCase     (			_UpperCAmelCase   ):
      _lowercase:			Dict						     =   ["""input_features"""]




      def __init__(			self			: Tuple			,			__snake_case			: Any=80			,			__snake_case			: int=1_60_00			,			__snake_case			: Dict=1_60			,			__snake_case			: Tuple=30			,			__snake_case			: List[Any]=4_00			,			__snake_case			: int=0.0			,			__snake_case			: Any=False			,			**__snake_case			: Dict			,			)				->      str:
            super().__init__(
                feature_size=UpperCAmelCase_			,			sampling_rate=UpperCAmelCase_			,			padding_value=UpperCAmelCase_			,			return_attention_mask=UpperCAmelCase_			,			**UpperCAmelCase_			,			)
            _lowerCAmelCase							     =							n_fft
            _lowerCAmelCase							     =							hop_length
            _lowerCAmelCase							     =							chunk_length
            _lowerCAmelCase							     =							chunk_length * sampling_rate
            _lowerCAmelCase							     =							self.n_samples // hop_length
            _lowerCAmelCase							     =							sampling_rate
            _lowerCAmelCase							     =							mel_filter_bank(
                num_frequency_bins=1 + n_fft // 2			,			num_mel_filters=UpperCAmelCase_			,			min_frequency=0.0			,			max_frequency=80_00.0			,			sampling_rate=UpperCAmelCase_			,			norm="""slaney"""			,			mel_scale="""slaney"""			,			)




      def 				lowercase__    (			self			: Optional[Any]			,			__snake_case			: np.array  )				->      str:
            _lowerCAmelCase							     =							spectrogram(
                UpperCAmelCase_			,			window_function(self.n_fft			,			"""hann"""  )			,			frame_length=self.n_fft			,			hop_length=self.hop_length			,			power=2.0			,			mel_filters=self.mel_filters			,			log_mel="""log10"""			,			)
            _lowerCAmelCase							     =							log_spec[:, :-1]
            _lowerCAmelCase							     =							np.maximum(UpperCAmelCase_			,			log_spec.max() - 8.0  )
            _lowerCAmelCase							     =							(log_spec + 4.0) / 4.0
            return log_spec




      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def 				lowercase__    (			__snake_case			: List[np.ndarray]			,			__snake_case			: List[np.ndarray]			,			__snake_case			: float = 0.0  )				->      Any:
            if attention_mask is not None:
                  _lowerCAmelCase							     =							np.array(UpperCAmelCase_			,			np.intaa  )
                  _lowerCAmelCase							     =							[]

                  for vector, length in zip(UpperCAmelCase_			,			attention_mask.sum(-1  )  ):
                        _lowerCAmelCase							     =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7  )
                        if length < normed_slice.shape[0]:
                              _lowerCAmelCase							     =							padding_value

                        normed_input_values.append(UpperCAmelCase_  )
            else:
                  _lowerCAmelCase							     =							[(x - x.mean()) / np.sqrt(x.var() + 1E-7  ) for x in input_values]

            return normed_input_values




      def __call__(			self			: Tuple			,			__snake_case			: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,			__snake_case			: bool = True			,			__snake_case			: Optional[int] = None			,			__snake_case			: Optional[Union[str, TensorType]] = None			,			__snake_case			: Optional[bool] = None			,			__snake_case			: Optional[str] = "max_length"			,			__snake_case			: Optional[int] = None			,			__snake_case			: Optional[int] = None			,			__snake_case			: Optional[bool] = None			,			**__snake_case			: int			,			)				->      int:

            if sampling_rate is not None:
                  if sampling_rate != self.sampling_rate:
                        raise ValueError(
                            f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                            f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                            f" was sampled with {self.sampling_rate} and not {sampling_rate}."  )
            else:
                  logger.warning(
                      """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                      """Failing to do so can result in silent errors that might be hard to debug."""  )

            _lowerCAmelCase							     =							isinstance(UpperCAmelCase_			,			np.ndarray  ) and len(raw_speech.shape  ) > 1
            if is_batched_numpy and len(raw_speech.shape  ) > 2:
                  raise ValueError(f"Only mono-channel audio is supported for input to {self}"  )
            _lowerCAmelCase							     =							is_batched_numpy or (
                isinstance(UpperCAmelCase_			,			(list, tuple)  ) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)  ))
            )

            if is_batched:
                  _lowerCAmelCase							     =							[np.asarray([speech]			,			dtype=np.floataa  ).T for speech in raw_speech]
            elif not is_batched and not isinstance(UpperCAmelCase_			,			np.ndarray  ):
                  _lowerCAmelCase							     =							np.asarray(UpperCAmelCase_			,			dtype=np.floataa  )
            elif isinstance(UpperCAmelCase_			,			np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
                  _lowerCAmelCase							     =							raw_speech.astype(np.floataa  )

            # always return batch
            if not is_batched:
                  _lowerCAmelCase							     =							[np.asarray([raw_speech]  ).T]

            _lowerCAmelCase							     =							BatchFeature({"""input_features""": raw_speech}  )

            # convert into correct format for padding

            _lowerCAmelCase							     =							self.pad(
                UpperCAmelCase_			,			padding=UpperCAmelCase_			,			max_length=max_length if max_length else self.n_samples			,			truncation=UpperCAmelCase_			,			pad_to_multiple_of=UpperCAmelCase_			,			return_attention_mask=return_attention_mask or do_normalize			,			)

            # zero-mean and unit-variance normalization
            if do_normalize:
                  _lowerCAmelCase							     =							self.zero_mean_unit_var_norm(
                      padded_inputs["""input_features"""]			,			attention_mask=padded_inputs["""attention_mask"""]			,			padding_value=self.padding_value			,			)
                  _lowerCAmelCase							     =							np.stack(padded_inputs["""input_features"""]			,			axis=0  )

            # make sure list is in array format
            _lowerCAmelCase							     =							padded_inputs.get("""input_features"""  ).transpose(2			,			0			,			1  )

            _lowerCAmelCase							     =							[self._np_extract_fbank_features(UpperCAmelCase_  ) for waveform in input_features[0]]

            if isinstance(input_features[0]			,			UpperCAmelCase_  ):
                  _lowerCAmelCase							     =							[np.asarray(UpperCAmelCase_			,			dtype=np.floataa  ) for feature in input_features]
            else:
                  _lowerCAmelCase							     =							input_features

            if return_attention_mask:
                  # rescale from sample (48000) to feature (3000)
                  _lowerCAmelCase							     =							padded_inputs["""attention_mask"""][:, :: self.hop_length]

            if return_tensors is not None:
                  _lowerCAmelCase							     =							padded_inputs.convert_to_tensors(UpperCAmelCase_  )

            return padded_inputs




      def 				lowercase__    (			self			: Optional[Any]  )				->      Dict:
            _lowerCAmelCase							     =							copy.deepcopy(self.__dict__  )
            _lowerCAmelCase							     =							self.__class__.__name__
            if "mel_filters" in output:
                  del output["mel_filters"]
            return output








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()
__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """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""",
}
__snake_case								=  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					for attribute in key.split('.'		):
										SCREAMING_SNAKE_CASE__													= getattr(UpperCamelCase_	,	UpperCamelCase_		)

					if weight_type is not None:
										SCREAMING_SNAKE_CASE__													= getattr(UpperCamelCase_	,	UpperCamelCase_		).shape
					else:
										SCREAMING_SNAKE_CASE__													= 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":
										SCREAMING_SNAKE_CASE__													= value
					elif weight_type == "weight_g":
										SCREAMING_SNAKE_CASE__													= value
					elif weight_type == "weight_v":
										SCREAMING_SNAKE_CASE__													= value
					elif weight_type == "bias":
										SCREAMING_SNAKE_CASE__													= value
					else:
										SCREAMING_SNAKE_CASE__													= value

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					SCREAMING_SNAKE_CASE__													= fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE__													= hf_model.feature_extractor
					SCREAMING_SNAKE_CASE__													= hf_model.adapter

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

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= full_name.split('conv_layers.'		)[-1]
					SCREAMING_SNAKE_CASE__													= name.split('.'		)
					SCREAMING_SNAKE_CASE__													= int(items[0]		)
					SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= 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."
															)
															SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
					else:
										unused_weights.append(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= full_name.split('adaptor.'		)[-1]
					SCREAMING_SNAKE_CASE__													= name.split('.'		)

					if items[1].isdigit():
										SCREAMING_SNAKE_CASE__													= int(items[1]		)
					else:
										SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer weight was initialized from {full_name}.'		)
					elif isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										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.'
															SCREAMING_SNAKE_CASE__													= 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.'
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
					else:
										unused_weights.append(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= emb.weight.shape
					SCREAMING_SNAKE_CASE__													= nn.Linear(UpperCamelCase_	,	UpperCamelCase_	,	bias=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= emb.weight.data
					return lin_layer






@torch.no_grad()
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	)		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= WavaVecaConfig.from_pretrained(
					    UpperCamelCase_	,	add_adapter=UpperCamelCase_	,	adapter_stride=UpperCamelCase_	,	adapter_kernel_size=UpperCamelCase_	,	use_auth_token=UpperCamelCase_	,	output_hidden_size=UpperCamelCase_	,	)
					SCREAMING_SNAKE_CASE__													= MBartConfig.from_pretrained(UpperCamelCase_		)

					# load model
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= 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,
					    }	,	)
					SCREAMING_SNAKE_CASE__													= model[0].eval()

					# load feature extractor
					SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_	,	use_auth_token=UpperCamelCase_		)

					# set weights for wav2vec2 encoder
					SCREAMING_SNAKE_CASE__													= WavaVecaModel(UpperCamelCase_		)

					recursively_load_weights_wavaveca(model.encoder	,	UpperCamelCase_		)

					# load decoder weights
					SCREAMING_SNAKE_CASE__													= MBartForCausalLM(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,	strict=UpperCamelCase_		)
					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}'		)

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderModel(encoder=UpperCamelCase_	,	decoder=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= False

					SCREAMING_SNAKE_CASE__													= MBartaaTokenizer(UpperCamelCase_		)
					tokenizer.save_pretrained(UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= hf_wavavec.config.to_dict()
					SCREAMING_SNAKE_CASE__													= tokenizer.pad_token_id
					SCREAMING_SNAKE_CASE__													= tokenizer.bos_token_id
					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id
					SCREAMING_SNAKE_CASE__													= 'mbart50'
					SCREAMING_SNAKE_CASE__													= 'wav2vec2'

					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id
					SCREAMING_SNAKE_CASE__													= 250004
					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_		)

					hf_wavavec.save_pretrained(UpperCamelCase_		)
					feature_extractor.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
				__snake_case								=  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""")

				__snake_case								=  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,
				)





