






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A					=	logging.getLogger(__name__)



@dataclass
class 				_A			:
    """simple docstring"""

    lowerCamelCase	:							str				= field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
    lowerCamelCase	:							Optional[str]				= field(
        default=_A					,					metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
    lowerCamelCase	:							Optional[str]				= field(
        default=_A					,					metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
    lowerCamelCase	:							Optional[str]				= field(
        default=_A					,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,					)
    lowerCamelCase	:							bool				= field(default=_A					,					metadata={'help': 'Whether tp freeze the encoder.'}				)
    lowerCamelCase	:							bool				= field(default=_A					,					metadata={'help': 'Whether  to freeze the embeddings.'}				)




@dataclass
class 				_A			:
    """simple docstring"""

    lowerCamelCase	:							str				= field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
    lowerCamelCase	:							Optional[str]				= field(
        default='summarization'					,					metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}					,					)
    lowerCamelCase	:							Optional[int]				= field(
        default=1_024					,					metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }					,					)
    lowerCamelCase	:							Optional[int]				= field(
        default=128					,					metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }					,					)
    lowerCamelCase	:							Optional[int]				= field(
        default=142					,					metadata={
            'help': (
                'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded. '
                'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                'during ``evaluate`` and ``predict``.'
            )
        }					,					)
    lowerCamelCase	:							Optional[int]				= field(
        default=142					,					metadata={
            'help': (
                'The maximum total sequence length for test target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }					,					)
    lowerCamelCase	:							Optional[int]				= field(default=-1					,					metadata={'help': '# training examples. -1 means use all.'}				)
    lowerCamelCase	:							Optional[int]				= field(default=-1					,					metadata={'help': '# validation examples. -1 means use all.'}				)
    lowerCamelCase	:							Optional[int]				= field(default=-1					,					metadata={'help': '# test examples. -1 means use all.'}				)
    lowerCamelCase	:							Optional[str]				= field(default=_A					,					metadata={'help': 'Source language id for translation.'}				)
    lowerCamelCase	:							Optional[str]				= field(default=_A					,					metadata={'help': 'Target language id for translation.'}				)
    lowerCamelCase	:							Optional[int]				= field(default=_A					,					metadata={'help': '# num_beams to use for evaluation.'}				)
    lowerCamelCase	:							bool				= field(
        default=_A					,					metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}					,					)







def       lowercase__	(					A_:						List[Any]  ,      A_:						Dict  ,      A_:						Optional[Any]		)	->				Optional[int]:







       """simple docstring"""






       logger.info(F'''***** {split} metrics *****'''		)
       for key in sorted(metrics.keys()		):
              logger.info(F'''  {key} = {metrics[key]}'''		)
       save_json(_snake_case  ,      os.path.join(_snake_case  ,      F'''{split}_results.json'''		)		)







def       lowercase__	(					)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)		)

       if len(sys.argv		) == 2 and sys.argv[1].endswith(""".json"""		):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
       else:
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=parser.parse_args_into_dataclasses()

       check_output_dir(_snake_case		)

       # Setup logging
       logging.basicConfig(
           format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,      datefmt="""%m/%d/%Y %H:%M:%S"""  ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,      )
       logger.warning(
           """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""  ,      training_args.local_rank  ,      training_args.device  ,      training_args.n_gpu  ,      bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED		)  ,      training_args.fpaa  ,      )
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank		):
              transformers.utils.logging.set_verbosity_info()
       logger.info("""Training/evaluation parameters %s"""  ,      _snake_case		)

       # Set seed
       set_seed(training_args.seed		)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       __UpperCAmelCase     				=AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,      cache_dir=model_args.cache_dir  ,      )

       __UpperCAmelCase     				=("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
       for p in extra_model_params:
              if getattr(_snake_case  ,      _snake_case  ,      _snake_case		):
                     assert hasattr(_snake_case  ,      _snake_case		), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
                     setattr(_snake_case  ,      _snake_case  ,      getattr(_snake_case  ,      _snake_case		)		)

       __UpperCAmelCase     				=AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,      cache_dir=model_args.cache_dir  ,      )
       __UpperCAmelCase     				=AutoModelForSeqaSeqLM.from_pretrained(
           model_args.model_name_or_path  ,      from_tf=""".ckpt""" in model_args.model_name_or_path  ,      config=_snake_case  ,      cache_dir=model_args.cache_dir  ,      )

       # use task specific params
       use_task_specific_params(_snake_case  ,      data_args.task		)

       # set num_beams for evaluation
       if data_args.eval_beams is None:
              __UpperCAmelCase     				=model.config.num_beams

       # set decoder_start_token_id for MBart
       if model.config.decoder_start_token_id is None and isinstance(_snake_case  ,      (MBartTokenizer, MBartTokenizerFast)		):
              assert (
                  data_args.tgt_lang is not None and data_args.src_lang is not None
              ), "mBart requires --tgt_lang and --src_lang"
              if isinstance(_snake_case  ,      _snake_case		):
                     __UpperCAmelCase     				=tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     __UpperCAmelCase     				=tokenizer.convert_tokens_to_ids(data_args.tgt_lang		)

       if model_args.freeze_embeds:
              freeze_embeds(_snake_case		)
       if model_args.freeze_encoder:
              freeze_params(model.get_encoder()		)
              assert_all_frozen(model.get_encoder()		)

       __UpperCAmelCase     				=SeqaSeqDataset

       # Get datasets
       __UpperCAmelCase     				=(
           dataset_class(
               _snake_case  ,      type_path="""train"""  ,      data_dir=data_args.data_dir  ,      n_obs=data_args.n_train  ,      max_target_length=data_args.max_target_length  ,      max_source_length=data_args.max_source_length  ,      prefix=model.config.prefix or """"""  ,      )
           if training_args.do_train
           else None
       )
       __UpperCAmelCase     				=(
           dataset_class(
               _snake_case  ,      type_path="""val"""  ,      data_dir=data_args.data_dir  ,      n_obs=data_args.n_val  ,      max_target_length=data_args.val_max_target_length  ,      max_source_length=data_args.max_source_length  ,      prefix=model.config.prefix or """"""  ,      )
           if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
           else None
       )
       __UpperCAmelCase     				=(
           dataset_class(
               _snake_case  ,      type_path="""test"""  ,      data_dir=data_args.data_dir  ,      n_obs=data_args.n_test  ,      max_target_length=data_args.test_max_target_length  ,      max_source_length=data_args.max_source_length  ,      prefix=model.config.prefix or """"""  ,      )
           if training_args.do_predict
           else None
       )

       # Initialize our Trainer
       __UpperCAmelCase     				=(
           build_compute_metrics_fn(data_args.task  ,      _snake_case		) if training_args.predict_with_generate else None
       )
       __UpperCAmelCase     				=SeqaSeqTrainer(
           model=_snake_case  ,      args=_snake_case  ,      data_args=_snake_case  ,      train_dataset=_snake_case  ,      eval_dataset=_snake_case  ,      data_collator=SeqaSeqDataCollator(
               _snake_case  ,      _snake_case  ,      model.config.decoder_start_token_id  ,      training_args.tpu_num_cores		)  ,      compute_metrics=_snake_case  ,      tokenizer=_snake_case  ,      )

       __UpperCAmelCase     				={}
       # Training
       if training_args.do_train:
              logger.info("""*** Train ***"""		)

              __UpperCAmelCase     				=trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
              __UpperCAmelCase     				=train_result.metrics
              __UpperCAmelCase     				=data_args.n_train

              trainer.save_model()  # this also saves the tokenizer

              if trainer.is_world_process_zero():
                     handle_metrics("""train"""  ,      _snake_case  ,      training_args.output_dir		)
                     all_metrics.update(_snake_case		)

                     # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir  ,      """trainer_state.json"""		)		)

                     # For convenience, we also re-save the tokenizer to the same directory,
                     # so that you can share your model easily on huggingface.co/models =)
                     tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
       if training_args.do_eval:
              logger.info("""*** Evaluate ***"""		)

              __UpperCAmelCase     				=trainer.evaluate(metric_key_prefix="""val"""		)
              __UpperCAmelCase     				=data_args.n_val
              __UpperCAmelCase     				=round(metrics["""val_loss"""]  ,      4		)

              if trainer.is_world_process_zero():
                     handle_metrics("""val"""  ,      _snake_case  ,      training_args.output_dir		)
                     all_metrics.update(_snake_case		)

       if training_args.do_predict:
              logger.info("""*** Predict ***"""		)

              __UpperCAmelCase     				=trainer.predict(test_dataset=_snake_case  ,      metric_key_prefix="""test"""		)
              __UpperCAmelCase     				=test_output.metrics
              __UpperCAmelCase     				=data_args.n_test

              if trainer.is_world_process_zero():
                     __UpperCAmelCase     				=round(metrics["""test_loss"""]  ,      4		)
                     handle_metrics("""test"""  ,      _snake_case  ,      training_args.output_dir		)
                     all_metrics.update(_snake_case		)

                     if training_args.predict_with_generate:
                            __UpperCAmelCase     				=tokenizer.batch_decode(
                                test_output.predictions  ,      skip_special_tokens=_snake_case  ,      clean_up_tokenization_spaces=_snake_case		)
                            __UpperCAmelCase     				=lmap(str.strip  ,      _snake_case		)
                            write_txt_file(_snake_case  ,      os.path.join(training_args.output_dir  ,      """test_generations.txt"""		)		)

       if trainer.is_world_process_zero():
              save_json(_snake_case  ,      os.path.join(training_args.output_dir  ,      """all_results.json"""		)		)

       return all_metrics







def       lowercase__	(					A_:						Any		)	->				List[Any]:







       """simple docstring"""






       main()


if __name__ == "__main__":
   main()









import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase_		 =					{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:str	,   _snake_case	:Any	,   _snake_case	:int	,   _snake_case	:List[Any]				)  ->     Optional[int]:
   for attribute in key.split('''.'''				):
      _A      =       getattr(_snake_case	,   _snake_case				)

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

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:Any	,   _snake_case	:int				)  ->     Any:
   _A      =       []
   _A      =       fairseq_model.state_dict()

   _A      =       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _A      =       False
      if "conv_layers" in name:
         load_conv_layer(
             _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   hf_model.config.feat_extract_norm == '''group'''	,   )
         _A      =       True
      else:
         for key, mapped_key in MAPPING.items():
            _A      =       '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

            if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
               _A      =       True
               if "*" in mapped_key:
                  _A      =       name.split(_snake_case				)[0].split('''.'''				)[-2]
                  _A      =       mapped_key.replace('''*'''	,   _snake_case				)
               if "weight_g" in name:
                  _A      =       '''weight_g'''
               elif "weight_v" in name:
                  _A      =       '''weight_v'''
               elif "weight" in name:
                  _A      =       '''weight'''
               elif "bias" in name:
                  _A      =       '''bias'''
               else:
                  _A      =       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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:List[str]	,   _snake_case	:List[str]	,   _snake_case	:Optional[int]	,   _snake_case	:List[Any]				)  ->     Any:
   _A      =       full_name.split('''conv_layers.'''				)[-1]
   _A      =       name.split('''.'''				)
   _A      =       int(items[0]				)
   _A      =       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      =       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      =       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      =       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      =       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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:Dict				)  ->     Tuple:
   _A      =       SEWConfig()
   if is_finetuned:
      _A      =       model.wav_encoder.wav_model.cfg
   else:
      _A      =       model.cfg

   _A      =       fs_config.conv_bias
   _A      =       eval(fs_config.conv_feature_layers				)
   _A      =       [x[0] for x in conv_layers]
   _A      =       [x[1] for x in conv_layers]
   _A      =       [x[2] for x in conv_layers]
   _A      =       '''gelu'''
   _A      =       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   _A      =       0.0
   _A      =       fs_config.activation_fn.name
   _A      =       fs_config.encoder_embed_dim
   _A      =       0.02
   _A      =       fs_config.encoder_ffn_embed_dim
   _A      =       1E-5
   _A      =       fs_config.encoder_layerdrop
   _A      =       fs_config.encoder_attention_heads
   _A      =       fs_config.conv_pos_groups
   _A      =       fs_config.conv_pos
   _A      =       len(_snake_case				)
   _A      =       fs_config.encoder_layers
   _A      =       fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      _A      =       model.cfg
      _A      =       fs_config.final_dropout
      _A      =       fs_config.layerdrop
   _A      =       fs_config.activation_dropout
   _A      =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   _A      =       fs_config.attention_dropout
   _A      =       fs_config.dropout_input
   _A      =       fs_config.dropout
   _A      =       fs_config.mask_channel_length
   _A      =       fs_config.mask_channel_prob
   _A      =       fs_config.mask_length
   _A      =       fs_config.mask_prob

   _A      =       '''Wav2Vec2FeatureExtractor'''
   _A      =       '''Wav2Vec2CTCTokenizer'''

   return config

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:Union[str, Any]	,   _snake_case	:Optional[Any]=None	,   _snake_case	:Optional[int]=None	,   _snake_case	:Dict=True				)  ->     List[Any]:

   if is_finetuned:
      _A  ,    _A  ,    _A      =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
   else:
      _A  ,    _A  ,    _A      =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

   if config_path is not None:
      _A      =       SEWConfig.from_pretrained(_snake_case				)
   else:
      _A      =       convert_config(model[0]	,   _snake_case				)
   _A      =       model[0].eval()

   _A      =       True if config.feat_extract_norm == '''layer''' else False
   _A      =       WavaVecaFeatureExtractor(
       feature_size=1	,   sampling_rate=16_000	,   padding_value=0	,   do_normalize=_snake_case	,   return_attention_mask=_snake_case	,   )

   if is_finetuned:
      if dict_path:
         _A      =       Dictionary.load(_snake_case				)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         _A      =       target_dict.pad_index
         _A      =       target_dict.bos_index
         _A      =       target_dict.pad_index
         _A      =       target_dict.bos_index
         _A      =       target_dict.eos_index
         _A      =       len(target_dict.symbols				)
         _A      =       os.path.join(_snake_case	,   '''vocab.json'''				)
         if not os.path.isdir(_snake_case				):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case				)				)
            return
         os.makedirs(_snake_case	,   exist_ok=_snake_case				)
         with open(_snake_case	,   '''w'''	,   encoding='''utf-8'''				) as vocab_handle:
            json.dump(target_dict.indices	,   _snake_case				)
         _A      =       WavaVecaCTCTokenizer(
             _snake_case	,   unk_token=target_dict.unk_word	,   pad_token=target_dict.pad_word	,   bos_token=target_dict.bos_word	,   eos_token=target_dict.eos_word	,   word_delimiter_token='''|'''	,   do_lower_case=_snake_case	,   )
         _A      =       WavaVecaProcessor(feature_extractor=_snake_case	,   tokenizer=_snake_case				)
         processor.save_pretrained(_snake_case				)

      _A      =       SEWForCTC(_snake_case				)
   else:
      _A      =       SEWModel(_snake_case				)
      feature_extractor.save_pretrained(_snake_case				)

   recursively_load_weights(_snake_case	,   _snake_case	,   _snake_case				)

   hf_model.save_pretrained(_snake_case				)


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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
   )
   UpperCAmelCase_		 =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )



