


"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
   import tensorflow as tf

   from transformers.models.distilbert.modeling_tf_distilbert import (
       TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFDistilBertForMaskedLM,
       TFDistilBertForMultipleChoice,
       TFDistilBertForQuestionAnswering,
       TFDistilBertForSequenceClassification,
       TFDistilBertForTokenClassification,
       TFDistilBertModel,
   )


class 			__lowerCamelCase     :

     '''simple docstring'''


     def __init__(					self			, __UpperCAmelCase			, )				->							Optional[int]:
          _a					= parent
          _a					= 13
          _a					= 7
          _a					= True
          _a					= True
          _a					= False
          _a					= True
          _a					= 99
          _a					= 32
          _a					= 2
          _a					= 4
          _a					= 37
          _a					= 'gelu'
          _a					= 0.1
          _a					= 0.1
          _a					= 512
          _a					= 16
          _a					= 2
          _a					= 0.02
          _a					= 3
          _a					= 4
          _a					= None


     def 							_UpperCAmelCase					(					self							)				->							Dict:
          _a					= ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size							)

          _a					= None
          if self.use_input_mask:
               _a					= random_attention_mask([self.batch_size, self.seq_length]							)

          _a					= None
          _a					= None
          _a					= None
          if self.use_labels:
               _a					= ids_tensor([self.batch_size]			, self.type_sequence_label_size							)
               _a					= ids_tensor([self.batch_size, self.seq_length]			, self.num_labels							)
               _a					= ids_tensor([self.batch_size]			, self.num_choices							)

          _a					= DistilBertConfig(
              vocab_size=self.vocab_size			, dim=self.hidden_size			, n_layers=self.num_hidden_layers			, n_heads=self.num_attention_heads			, hidden_dim=self.intermediate_size			, hidden_act=self.hidden_act			, dropout=self.hidden_dropout_prob			, attention_dropout=self.attention_probs_dropout_prob			, max_position_embeddings=self.max_position_embeddings			, initializer_range=self.initializer_range			, )

          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							int:
          _a					= TFDistilBertModel(config=__a							)
          _a					= {'input_ids': input_ids, 'attention_mask': input_mask}

          _a					= model(__a							)

          _a					= [input_ids, input_mask]

          _a					= model(__a							)

          self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							List[Any]:
          _a					= TFDistilBertForMaskedLM(config=__a							)
          _a					= {'input_ids': input_ids, 'attention_mask': input_mask}
          _a					= model(__a							)
          self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Optional[int]:
          _a					= TFDistilBertForQuestionAnswering(config=__a							)
          _a					= {
              'input_ids': input_ids,
              'attention_mask': input_mask,
          }
          _a					= model(__a							)
          self.parent.assertEqual(result.start_logits.shape			, (self.batch_size, self.seq_length)							)
          self.parent.assertEqual(result.end_logits.shape			, (self.batch_size, self.seq_length)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							List[str]:
          _a					= self.num_labels
          _a					= TFDistilBertForSequenceClassification(__a							)
          _a					= {'input_ids': input_ids, 'attention_mask': input_mask}
          _a					= model(__a							)
          self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Any:
          _a					= self.num_choices
          _a					= TFDistilBertForMultipleChoice(__a							)
          _a					= tf.tile(tf.expand_dims(__a			, 1							)			, (1, self.num_choices, 1)							)
          _a					= tf.tile(tf.expand_dims(__a			, 1							)			, (1, self.num_choices, 1)							)
          _a					= {
              'input_ids': multiple_choice_inputs_ids,
              'attention_mask': multiple_choice_input_mask,
          }
          _a					= model(__a							)
          self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_choices)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Optional[Any]:
          _a					= self.num_labels
          _a					= TFDistilBertForTokenClassification(__a							)
          _a					= {'input_ids': input_ids, 'attention_mask': input_mask}
          _a					= model(__a							)
          self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.num_labels)							)





     def 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          _a					= self.prepare_config_and_inputs()
          (_a)					= config_and_inputs
          _a					= {'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict







@require_tf
class 			__lowerCamelCase     (    lowerCAmelCase_     ,    lowerCAmelCase_     ,    unittest.TestCase ):

     '''simple docstring'''
     A_				:		Tuple			=						(
         (
             TFDistilBertModel,
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
             TFDistilBertForTokenClassification,
             TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
     )
     A_				:		int			=						(
         {
             'feature-extraction': TFDistilBertModel,
             'fill-mask': TFDistilBertForMaskedLM,
             'question-answering': TFDistilBertForQuestionAnswering,
             'text-classification': TFDistilBertForSequenceClassification,
             'token-classification': TFDistilBertForTokenClassification,
             'zero-shot': TFDistilBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     A_				:		Any			=						False
     A_				:		Tuple			=						False


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          _a					= TFDistilBertModelTester(self							)
          _a					= ConfigTester(self			, config_class=__a			, dim=37							)


     def 							_UpperCAmelCase					(					self							)				->							Optional[Any]:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase					(					self							)				->							str:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_model(*__a							)


     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_masked_lm(*__a							)


     def 							_UpperCAmelCase					(					self							)				->							str:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_question_answering(*__a							)


     def 							_UpperCAmelCase					(					self							)				->							int:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_sequence_classification(*__a							)


     def 							_UpperCAmelCase					(					self							)				->							Optional[Any]:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_multiple_choice(*__a							)


     def 							_UpperCAmelCase					(					self							)				->							str:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_token_classification(*__a							)





     @slow
     def 							_UpperCAmelCase					(					self							)				->							Tuple:





          for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
               _a					= TFDistilBertModel.from_pretrained(__a							)
               self.assertIsNotNone(__a							)







@require_tf
class 			__lowerCamelCase     (    unittest.TestCase ):

     '''simple docstring'''


     @slow
     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a					= TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''							)
          _a					= tf.constant([[0, 1, 2, 3, 4, 5]]							)
          _a					= model(__a							)[0]

          _a					= [1, 6, 768]
          self.assertEqual(output.shape			, __a							)

          _a					= tf.constant(
              [
                  [
                      [0.19261885, -0.13732955, 0.4119799],
                      [0.22150156, -0.07422661, 0.39037204],
                      [0.22756018, -0.0896414, 0.3701467],
                  ]
              ]							)
          tf.debugging.assert_near(output[:, :3, :3]			, __a			, atol=1e-4							)



'''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()
__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)

__lowercase	:						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',
}
__lowercase	:						Optional[Any]       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Dict   ):
					for attribute in key.split('.'   ):
										__a			:       Any					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					if weight_type is not None:
										__a			:       List[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ).shape
					else:
										__a			:       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":
										__a			:       Tuple					    =  value
					elif weight_type == "weight_g":
										__a			:       str					    =  value
					elif weight_type == "weight_v":
										__a			:       Optional[Any]					    =  value
					elif weight_type == "bias":
										__a			:       Union[str, Any]					    =  value
					else:
										__a			:       List[Any]					    =  value

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       int					    =  []
					__a			:       List[str]					    =  fairseq_model.state_dict()

					__a			:       Tuple					    =  hf_model.feature_extractor

					# if encoder has different dim to decoder -> use proj_weight
					__a			:       int					    =  None

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

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

					return proj_weight






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       List[str]					    =  full_name.split('conv_layers.'   )[-1]
					__a			:       Any					    =  name.split('.'   )
					__a			:       List[str]					    =  int(items[0]   )
					__a			:       List[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."""
															)
															__a			:       List[str]					    =  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."""
															)
															__a			:       str					    =  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."
															)
															__a			:       Tuple					    =  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."""
															)
															__a			:       List[Any]					    =  value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
					else:
										unused_weights.append(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a    ,				__a			:       List[str]					    =  emb.weight.shape
					__a			:       str					    =  nn.Linear(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      bias=_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  emb.weight.data
					return lin_layer






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'						,      encoding='utf-8'   ) as f:
										__a			:       Union[str, Any]					    =  f.readlines()
										__a			:       Tuple					    =  [line.split(' '   )[0] for line in lines]

					__a			:       int					    =  len(_SCREAMING_SNAKE_CASE   )

					__a			:       List[Any]					    =  {
					    '<s>': 0,
					    '<pad>': 1,
					    '</s>': 2,
					    '<unk>': 3,
					}

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






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      ):
					__a			:       Optional[int]					    =  WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  SpeechaTextaConfig.from_pretrained(
					    _SCREAMING_SNAKE_CASE						,      vocab_size=_SCREAMING_SNAKE_CASE						,      decoder_layers=_SCREAMING_SNAKE_CASE						,      do_stable_layer_norm=_SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  WavaVecaFeatureExtractor(
					    feature_size=1						,      sampling_rate=16_000						,      padding_value=0						,      do_normalize=_SCREAMING_SNAKE_CASE						,      return_attention_mask=_SCREAMING_SNAKE_CASE						,      )

					__a    ,				__a    ,				__a			:       List[Any]					    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]						,      arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
					__a			:       Optional[int]					    =  model[0].eval()

					# set weights for wav2vec2 encoder
					__a			:       Tuple					    =  WavaVecaModel(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  recursively_load_weights_wavaveca(model.encoder						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  SpeechaTextaForCausalLM(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       Optional[int]					    =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,      strict=_SCREAMING_SNAKE_CASE   )

					# set output linear layer
					unexpected_keys.remove('embed_out'   )
					__a			:       Optional[Any]					    =  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}"""   )

					__a			:       Tuple					    =  SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE						,      decoder=_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  False

					# add projection layer
					__a			:       str					    =  nn.Parameter(projection_layer.weight   )
					__a			:       Any					    =  nn.Parameter(projection_layer.bias   )

					__a			:       str					    =  create_vocab_dict(_SCREAMING_SNAKE_CASE   )

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

					__a			:       Optional[Any]					    =  SpeechaTextaTokenizer(os.path.join(_SCREAMING_SNAKE_CASE						,      'vocab.json'   )   )
					tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )

					__a			:       Union[str, Any]					    =  hf_wavavec.config.to_dict()
					__a			:       Tuple					    =  tokenizer.pad_token_id
					__a			:       Optional[int]					    =  tokenizer.bos_token_id
					__a			:       Union[str, Any]					    =  tokenizer.eos_token_id
					__a			:       Tuple					    =  'speech_to_text_2'
					__a			:       Tuple					    =  'wav2vec2'

					__a			:       List[str]					    =  SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE   )

					hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE   )
					feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Dict       =	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_02_24, type=int, help='Vocab size of decoder')
	parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

	__lowercase	:						Tuple       =	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,
	)



