






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel




@require_tf
class     A    :






 """simple docstring"""





 __a			:						Tuple      =						BlenderbotConfig
 __a			:						Any      =						{}
 __a			:						Union[str, Any]      =						'''gelu'''
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=13			,     __lowerCAmelCase=7			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase=99			,     __lowerCAmelCase=32			,     __lowerCAmelCase=2			,     __lowerCAmelCase=4			,     __lowerCAmelCase=37			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=20			,     __lowerCAmelCase=2			,     __lowerCAmelCase=1			,     __lowerCAmelCase=0			,     ):
       UpperCamelCase_				:   str					    =							parent
       UpperCamelCase_				:   int					    =							batch_size
       UpperCamelCase_				:   Dict					    =							seq_length
       UpperCamelCase_				:   List[str]					    =							is_training
       UpperCamelCase_				:   List[Any]					    =							use_labels
       UpperCamelCase_				:   int					    =							vocab_size
       UpperCamelCase_				:   int					    =							hidden_size
       UpperCamelCase_				:   int					    =							num_hidden_layers
       UpperCamelCase_				:   List[Any]					    =							num_attention_heads
       UpperCamelCase_				:   Dict					    =							intermediate_size

       UpperCamelCase_				:   Optional[int]					    =							hidden_dropout_prob
       UpperCamelCase_				:   List[Any]					    =							attention_probs_dropout_prob
       UpperCamelCase_				:   List[str]					    =							max_position_embeddings
       UpperCamelCase_				:   int					    =							eos_token_id
       UpperCamelCase_				:   Tuple					    =							pad_token_id
       UpperCamelCase_				:   Optional[int]					    =							bos_token_id
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size )
       UpperCamelCase_				:   Any					    =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )			,     1 )
       UpperCamelCase_				:   Optional[Any]					    =							tf.concat([input_ids, eos_tensor]			,     axis=1 )

       UpperCamelCase_				:   List[Any]					    =							ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size )

       UpperCamelCase_				:   Tuple					    =							self.config_cls(
           vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     encoder_layers=self.num_hidden_layers			,     decoder_layers=self.num_hidden_layers			,     encoder_attention_heads=self.num_attention_heads			,     decoder_attention_heads=self.num_attention_heads			,     encoder_ffn_dim=self.intermediate_size			,     decoder_ffn_dim=self.intermediate_size			,     dropout=self.hidden_dropout_prob			,     attention_dropout=self.attention_probs_dropout_prob			,     max_position_embeddings=self.max_position_embeddings			,     eos_token_ids=[2]			,     bos_token_id=self.bos_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.pad_token_id			,     **self.config_updates			,     )
       UpperCamelCase_				:   str					    =							prepare_blenderbot_inputs_dict(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       return config, inputs_dict





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							TFBlenderbotModel(config=__lowerCAmelCase ).get_decoder()
       UpperCamelCase_				:   List[str]					    =							inputs_dict["""input_ids"""]

       UpperCamelCase_				:   List[str]					    =							input_ids[:1, :]
       UpperCamelCase_				:   str					    =							inputs_dict["""attention_mask"""][:1, :]
       UpperCamelCase_				:   Union[str, Any]					    =							inputs_dict["""head_mask"""]
       UpperCamelCase_				:   Union[str, Any]					    =							1

       # first forward pass
       UpperCamelCase_				:   List[Any]					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase			,     head_mask=__lowerCAmelCase			,     use_cache=__lowerCAmelCase )

       UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       UpperCamelCase_				:   int					    =							ids_tensor((self.batch_size, 3)			,     config.vocab_size )
       UpperCamelCase_				:   List[str]					    =							tf.cast(ids_tensor((self.batch_size, 3)			,     2 )			,     tf.inta )

       # append to next input_ids and
       UpperCamelCase_				:   Dict					    =							tf.concat([input_ids, next_tokens]			,     axis=-1 )
       UpperCamelCase_				:   Union[str, Any]					    =							tf.concat([attention_mask, next_attn_mask]			,     axis=-1 )

       UpperCamelCase_				:   int					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase )[0]
       UpperCamelCase_				:   Optional[Any]					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase			,     past_key_values=__lowerCAmelCase )[0]

       self.parent.assertEqual(next_tokens.shape[1]			,     output_from_past.shape[1] )

       # select random slice
       UpperCamelCase_				:   List[str]					    =							int(ids_tensor((1,)			,     output_from_past.shape[-1] ) )
       UpperCamelCase_				:   Tuple					    =							output_from_no_past[:, -3:, random_slice_idx]
       UpperCamelCase_				:   Optional[int]					    =							output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(__lowerCAmelCase			,     __lowerCAmelCase			,     rtol=1E-3 )






def 							snake_case					(     a_							: int    ,   a_							: int    ,   a_							: Optional[int]    ,   a_							: Dict=None    ,   a_							: Any=None    ,   a_							: Optional[int]=None    ,   a_							: Dict=None    ,   a_							: Dict=None    ,   )       ->				int:





      """simple docstring"""

      if attention_mask is None:
            UpperCamelCase_				:   List[str]					    =							tf.cast(tf.math.not_equal(a_    ,   config.pad_token_id	)    ,   tf.inta	)
      if decoder_attention_mask is None:
            UpperCamelCase_				:   Dict					    =							tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape    ,   dtype=tf.inta	),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,   config.pad_token_id	)    ,   tf.inta	),
                ]    ,   axis=-1    ,   )
      if head_mask is None:
            UpperCamelCase_				:   Dict					    =							tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
      if decoder_head_mask is None:
            UpperCamelCase_				:   str					    =							tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
      if cross_attn_head_mask is None:
            UpperCamelCase_				:   Optional[int]					    =							tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }




@require_tf
class     A    (      SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Optional[int]      =						(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 __a			:						List[str]      =						(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 __a			:						str      =						(
     {
         '''conversational''': TFBlenderbotForConditionalGeneration,
         '''feature-extraction''': TFBlenderbotModel,
         '''summarization''': TFBlenderbotForConditionalGeneration,
         '''text2text-generation''': TFBlenderbotForConditionalGeneration,
         '''translation''': TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 __a			:						Union[str, Any]      =						True
 __a			:						Any      =						False
 __a			:						Any      =						False
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							TFBlenderbotModelTester(self )
       UpperCamelCase_				:   Dict					    =							ConfigTester(self			,     config_class=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       self.config_tester.run_common_tests()





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase )






@require_tokenizers
@require_tf
class     A    (      unittest.TestCase						):






 """simple docstring"""





 __a			:						str      =						['''My friends are cool but they eat too many carbs.''']
 __a			:						Tuple      =						'''facebook/blenderbot-400M-distill'''
 @cached_property
 def   _UpperCAmelCase       (							self ):
       return BlenderbotTokenizer.from_pretrained(self.model_name )
 @cached_property
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
       return model





 @slow
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.tokenizer(self.src_text			,     return_tensors="""tf""" )
       UpperCamelCase_				:   Union[str, Any]					    =							self.model.generate(
           model_inputs.input_ids			,     )
       UpperCamelCase_				:   Tuple					    =							self.tokenizer.batch_decode(generated_ids.numpy()			,     skip_special_tokens=__lowerCAmelCase )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )














'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

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



def 							snake_case					(     a_							: str    ,   a_							: Any    ,   a_							: Dict    ,   a_							: Optional[int]    ,   a_							: Optional[int]	)       ->				int:





      """simple docstring"""

      for attribute in key.split("""."""	):
            UpperCamelCase_				:   Union[str, Any]					    =							getattr(a_    ,   a_	)

      if weight_type is not None:
            UpperCamelCase_				:   Dict					    =							getattr(a_    ,   a_	).shape
      else:
            UpperCamelCase_				:   int					    =							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":
            UpperCamelCase_				:   str					    =							value
      elif weight_type == "weight_g":
            UpperCamelCase_				:   str					    =							value
      elif weight_type == "weight_v":
            UpperCamelCase_				:   Optional[int]					    =							value
      elif weight_type == "bias":
            UpperCamelCase_				:   Any					    =							value
      else:
            UpperCamelCase_				:   Dict					    =							value

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



def 							snake_case					(     a_							: Optional[int]    ,   a_							: int	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							[]
      UpperCamelCase_				:   Dict					    =							fairseq_model.state_dict()

      UpperCamelCase_				:   Union[str, Any]					    =							hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      UpperCamelCase_				:   Optional[int]					    =							None

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

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

      return proj_weight



def 							snake_case					(     a_							: Any    ,   a_							: Any    ,   a_							: Union[str, Any]    ,   a_							: Dict    ,   a_							: Tuple	)       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							full_name.split("""conv_layers."""	)[-1]
      UpperCamelCase_				:   Optional[Any]					    =							name.split("""."""	)
      UpperCamelCase_				:   List[Any]					    =							int(items[0]	)
      UpperCamelCase_				:   Optional[Any]					    =							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."
                  )
                  UpperCamelCase_				:   List[Any]					    =							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."
                  )
                  UpperCamelCase_				:   Dict					    =							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."
                  )
                  UpperCamelCase_				:   List[str]					    =							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."
                  )
                  UpperCamelCase_				:   str					    =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
      else:
            unused_weights.append(a_	)



def 							snake_case					(     a_							: str	)       ->				Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							emb.weight.shape
      UpperCamelCase_				:   str					    =							nn.Linear(a_    ,   a_    ,   bias=a_	)
      UpperCamelCase_				:   Optional[int]					    =							emb.weight.data
      return lin_layer



def 							snake_case					(     a_							: Any	)       ->				Tuple:





      """simple docstring"""

      with open(a_    ,   """r"""    ,   encoding="""utf-8"""	) as f:
            UpperCamelCase_				:   Optional[int]					    =							f.readlines()
            UpperCamelCase_				:   Union[str, Any]					    =							[line.split(""" """	)[0] for line in lines]

      UpperCamelCase_				:   List[Any]					    =							len(a_	)

      UpperCamelCase_				:   Union[str, Any]					    =							{
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

      vocab_dict.update(dict(zip(a_    ,   range(4    ,   num_words + 4	)	)	)	)
      return vocab_dict



@torch.no_grad()
def 							snake_case					(     a_							: List[Any]    ,   a_							: str    ,   a_							: int    ,   a_							: int    ,   a_							: Optional[int]    ,   a_							: Any    ,   a_							: Tuple    ,   )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							WavaVecaConfig.from_pretrained(a_	)
      UpperCamelCase_				:   Any					    =							SpeechaTextaConfig.from_pretrained(
          a_    ,   vocab_size=a_    ,   decoder_layers=a_    ,   do_stable_layer_norm=a_	)

      UpperCamelCase_				:   Optional[Any]					    =							WavaVecaFeatureExtractor(
          feature_size=1    ,   sampling_rate=16_000    ,   padding_value=0    ,   do_normalize=a_    ,   return_attention_mask=a_    ,   )

      UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]    ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
      UpperCamelCase_				:   Union[str, Any]					    =							model[0].eval()

      # set weights for wav2vec2 encoder
      UpperCamelCase_				:   Tuple					    =							WavaVecaModel(a_	)
      UpperCamelCase_				:   Any					    =							recursively_load_weights_wavaveca(model.encoder    ,   a_	)

      UpperCamelCase_				:   Any					    =							SpeechaTextaForCausalLM(a_	)
      UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,   strict=a_	)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""	)
      UpperCamelCase_				:   Tuple					    =							nn.Parameter(model.decoder.embed_out.detach()	)

      # layer norm is init to identity matrix so leaving it is fine
      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}"	)

      UpperCamelCase_				:   Optional[Any]					    =							SpeechEncoderDecoderModel(encoder=a_    ,   decoder=a_	)
      UpperCamelCase_				:   Dict					    =							False

      # add projection layer
      UpperCamelCase_				:   Any					    =							nn.Parameter(projection_layer.weight	)
      UpperCamelCase_				:   Optional[Any]					    =							nn.Parameter(projection_layer.bias	)

      UpperCamelCase_				:   Dict					    =							create_vocab_dict(a_	)

      with open(os.path.join(a_    ,   """vocab.json"""	)    ,   """w"""	) as fp:
            json.dump(a_    ,   a_	)

      UpperCamelCase_				:   Optional[int]					    =							SpeechaTextaTokenizer(os.path.join(a_    ,   """vocab.json"""	)	)
      tokenizer.save_pretrained(a_	)

      UpperCamelCase_				:   Optional[int]					    =							hf_wavavec.config.to_dict()
      UpperCamelCase_				:   Union[str, Any]					    =							tokenizer.pad_token_id
      UpperCamelCase_				:   List[Any]					    =							tokenizer.bos_token_id
      UpperCamelCase_				:   str					    =							tokenizer.eos_token_id
      UpperCamelCase_				:   Dict					    =							"""speech_to_text_2"""
      UpperCamelCase_				:   Optional[Any]					    =							"""wav2vec2"""

      UpperCamelCase_				:   Dict					    =							SpeechEncoderDecoderConfig.from_dict(a_	)

      hf_wavavec.save_pretrained(a_	)
      feature_extractor.save_pretrained(a_	)


if __name__ == "__main__":
      UpperCamelCase							=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(
          "--encoder_config_path",
          default="facebook/wav2vec2-large-lv60",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/s2t-small-mustc-en-fr-st",
          type=str,
          help="Path to hf decoder s2t checkpoint config",
      )
      parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

      UpperCamelCase							=parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          vocab_size=args.vocab_size,
          num_decoder_layers=args.num_decoder_layers,
      )






