




import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


_lowerCAmelCase  :      Tuple						=      "sshleifer/mar_enro_6_3_student"
class 	_UpperCamelCase					( lowercase_						):
      def 		UpperCAmelCase_  (	self      :str					)   ->	Any:
       super().setUp()

       UpperCAmelCase__							       =  cached_path(
           "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"       ,					extract_compressed_file=__UpperCamelCase       ,					)
       UpperCAmelCase__							       =  f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
      @slow
      @require_torch_gpu
      def 		UpperCAmelCase_  (	self      :Union[str, Any]					)   ->	Optional[Any]:
       MarianMTModel.from_pretrained(__UpperCamelCase					)







      @slow
      @require_torch_gpu
      def 		UpperCAmelCase_  (	self      :Tuple					)   ->	Optional[int]:
       UpperCAmelCase__							       =  {
           '$MAX_LEN': 64,
           '$BS': 64,
           '$GAS': 1,
           '$ENRO_DIR': self.data_dir,
           'facebook/mbart-large-cc25': MARIAN_MODEL,
           # "val_check_interval=0.25": "val_check_interval=1.0",
           '--learning_rate=3e-5': '--learning_rate 3e-4',
           '--num_train_epochs 6': '--num_train_epochs 1',
       }

       # Clean up bash script
       UpperCAmelCase__							       =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split("finetune.py"					)[1].strip()
       UpperCAmelCase__							       =  bash_script.replace("\\\n"       ,					""					).strip().replace("\"$@\""       ,					""					)
       for k, v in env_vars_to_replace.items():
        UpperCAmelCase__							       =  bash_script.replace(__UpperCamelCase       ,					str(__UpperCamelCase					)					)
       UpperCAmelCase__							       =  self.get_auto_remove_tmp_dir()

       # bash_script = bash_script.replace("--fp16 ", "")
       UpperCAmelCase__							       =  f'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
       # XXX: args.gpus > 1 : handle multi_gpu in the future

       UpperCAmelCase__							       =  ['finetune.py'] + bash_script.split() + args
       with patch.object(__UpperCamelCase       ,					"argv"       ,					__UpperCamelCase					):
        UpperCAmelCase__							       =  argparse.ArgumentParser()
        UpperCAmelCase__							       =  pl.Trainer.add_argparse_args(__UpperCamelCase					)
        UpperCAmelCase__							       =  SummarizationModule.add_model_specific_args(__UpperCamelCase       ,					os.getcwd()					)
        UpperCAmelCase__							       =  parser.parse_args()
        UpperCAmelCase__							       =  main(__UpperCamelCase					)

       # Check metrics
       UpperCAmelCase__							       =  load_json(model.metrics_save_path					)
       UpperCAmelCase__							       =  metrics['val'][0]
       UpperCAmelCase__							       =  metrics['val'][-1]
       self.assertEqual(len(metrics["val"]					)       ,					(args.max_epochs / args.val_check_interval)					)
       assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}''']       ,					__UpperCamelCase					)

       self.assertGreater(last_step_stats["val_avg_gen_time"]       ,					0.01					)
       # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
       self.assertLessEqual(last_step_stats["val_avg_gen_time"]       ,					1.0					)

       # test learning requirements:

       # 1. BLEU improves over the course of training by more than 2 pts
       self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]       ,					2					)

       # 2. BLEU finishes above 17
       self.assertGreater(last_step_stats["val_avg_bleu"]       ,					17					)

       # 3. test BLEU and val BLEU within ~1.1 pt.
       self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]					)       ,					1.1					)

       # check lightning ckpt can be loaded and has a reasonable statedict
       UpperCAmelCase__							       =  os.listdir(__UpperCamelCase					)
       UpperCAmelCase__							       =  [x for x in contents if x.endswith(".ckpt"					)][0]
       UpperCAmelCase__							       =  os.path.join(args.output_dir       ,					__UpperCamelCase					)
       UpperCAmelCase__							       =  torch.load(__UpperCamelCase       ,					map_location="cpu"					)
       UpperCAmelCase__							       =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
       assert expected_key in ckpt["state_dict"]
       assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

       # TODO: turn on args.do_predict when PL bug fixed.
       if args.do_predict:
        UpperCAmelCase__							       =  {os.path.basename(__UpperCamelCase					) for p in contents}
        assert "test_generations.txt" in contents
        assert "test_results.txt" in contents
        # assert len(metrics["val"]) ==  desired_n_evals
        assert len(metrics["test"]					) == 1



class 	_UpperCamelCase					( lowercase_						):
      @timeout_decorator.timeout(600					)
      @slow
      @require_torch_gpu
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	Tuple:
       UpperCAmelCase__							       =  f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
       UpperCAmelCase__							       =  {
           '--fp16_opt_level=O1': '',
           '$MAX_LEN': 128,
           '$BS': 16,
           '$GAS': 1,
           '$ENRO_DIR': data_dir,
           '$m': 'sshleifer/student_marian_en_ro_6_1',
           'val_check_interval=0.25': 'val_check_interval=1.0',
       }

       # Clean up bash script
       UpperCAmelCase__							       =  (
           (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split("distillation.py"					)[1].strip()
       )
       UpperCAmelCase__							       =  bash_script.replace("\\\n"       ,					""					).strip().replace("\"$@\""       ,					""					)
       UpperCAmelCase__							       =  bash_script.replace("--fp16 "       ,					" "					)

       for k, v in env_vars_to_replace.items():
        UpperCAmelCase__							       =  bash_script.replace(__UpperCamelCase       ,					str(__UpperCamelCase					)					)
       UpperCAmelCase__							       =  self.get_auto_remove_tmp_dir()
       UpperCAmelCase__							       =  bash_script.replace("--fp16"       ,					""					)
       UpperCAmelCase__							       =  6
       UpperCAmelCase__							       =  (
           ['distillation.py']
           + bash_script.split()
           + [
               f'''--output_dir={output_dir}''',
               '--gpus=1',
               '--learning_rate=1e-3',
               f'''--num_train_epochs={epochs}''',
               '--warmup_steps=10',
               '--val_check_interval=1.0',
               '--do_predict',
           ]
       )
       with patch.object(__UpperCamelCase       ,					"argv"       ,					__UpperCamelCase					):
        UpperCAmelCase__							       =  argparse.ArgumentParser()
        UpperCAmelCase__							       =  pl.Trainer.add_argparse_args(__UpperCamelCase					)
        UpperCAmelCase__							       =  SummarizationDistiller.add_model_specific_args(__UpperCamelCase       ,					os.getcwd()					)
        UpperCAmelCase__							       =  parser.parse_args()
        # assert args.gpus == gpus THIS BREAKS for multi_gpu

        UpperCAmelCase__							       =  distill_main(__UpperCamelCase					)

       # Check metrics
       UpperCAmelCase__							       =  load_json(model.metrics_save_path					)
       UpperCAmelCase__							       =  metrics['val'][0]
       UpperCAmelCase__							       =  metrics['val'][-1]
       assert len(metrics["val"]					) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

       assert last_step_stats["val_avg_gen_time"] >= 0.01

       assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
       assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
       assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}''']       ,					__UpperCamelCase					)

       # check lightning ckpt can be loaded and has a reasonable statedict
       UpperCAmelCase__							       =  os.listdir(__UpperCamelCase					)
       UpperCAmelCase__							       =  [x for x in contents if x.endswith(".ckpt"					)][0]
       UpperCAmelCase__							       =  os.path.join(args.output_dir       ,					__UpperCamelCase					)
       UpperCAmelCase__							       =  torch.load(__UpperCamelCase       ,					map_location="cpu"					)
       UpperCAmelCase__							       =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
       assert expected_key in ckpt["state_dict"]
       assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

       # TODO: turn on args.do_predict when PL bug fixed.
       if args.do_predict:
        UpperCAmelCase__							       =  {os.path.basename(__UpperCamelCase					) for p in contents}
        assert "test_generations.txt" in contents
        assert "test_results.txt" in contents
        # assert len(metrics["val"]) ==  desired_n_evals
        assert len(metrics["test"]					) == 1






"""simple docstring"""







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()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    	={
    "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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     for attribute in key.split('.'      ):
          lowercase_	:				Tuple													=	getattr(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

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

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_	:				Optional[int]													=	[]
     lowercase_	:				Optional[int]													=	fairseq_model.state_dict()

     lowercase_	:				List[str]													=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase_	:				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'     ,			)
               lowercase_	:				str													=	True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase_	:				int													=	'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]:
                         lowercase_	:				str													=	True
                         if "*" in mapped_key:
                              lowercase_	:				int													=	name.split(__SCREAMING_SNAKE_CASE      )[0].split('.'      )[-2]
                              lowercase_	:				Optional[Any]													=	mapped_key.replace('*'     ,			__SCREAMING_SNAKE_CASE      )
                         if "weight_g" in name:
                              lowercase_	:				Any													=	'weight_g'
                         elif "weight_v" in name:
                              lowercase_	:				Tuple													=	'weight_v'
                         elif "weight" in name:
                              lowercase_	:				int													=	'weight'
                         elif "bias" in name:
                              lowercase_	:				List[Any]													=	'bias'
                         else:
                              lowercase_	:				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}'''      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Dict													=	full_name.split('conv_layers.'      )[-1]
     lowercase_	:				int													=	name.split('.'      )
     lowercase_	:				Any													=	int(items[0]      )
     lowercase_	:				Tuple													=	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.'''
               )
               lowercase_	:				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.'''
               )
               lowercase_	:				List[Any]													=	value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                   " found."
               )
               lowercase_	:				Optional[Any]													=	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.'''
               )
               lowercase_	:				List[str]													=	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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     lowercase_	:				str													=	SEWConfig()
     if is_finetuned:
          lowercase_	:				List[Any]													=	model.wav_encoder.wav_model.cfg
     else:
          lowercase_	:				Tuple													=	model.cfg

     lowercase_	:				Any													=	fs_config.conv_bias
     lowercase_	:				Optional[Any]													=	eval(fs_config.conv_feature_layers      )
     lowercase_	:				int													=	[x[0] for x in conv_layers]
     lowercase_	:				Any													=	[x[1] for x in conv_layers]
     lowercase_	:				Optional[Any]													=	[x[2] for x in conv_layers]
     lowercase_	:				Tuple													=	'gelu'
     lowercase_	:				str													=	'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     lowercase_	:				int													=	0.0
     lowercase_	:				Any													=	fs_config.activation_fn.name
     lowercase_	:				Tuple													=	fs_config.encoder_embed_dim
     lowercase_	:				int													=	0.02
     lowercase_	:				Union[str, Any]													=	fs_config.encoder_ffn_embed_dim
     lowercase_	:				Tuple													=	1E-5
     lowercase_	:				Union[str, Any]													=	fs_config.encoder_layerdrop
     lowercase_	:				Tuple													=	fs_config.encoder_attention_heads
     lowercase_	:				List[str]													=	fs_config.conv_pos_groups
     lowercase_	:				Union[str, Any]													=	fs_config.conv_pos
     lowercase_	:				str													=	len(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	fs_config.encoder_layers
     lowercase_	:				str													=	fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          lowercase_	:				Dict													=	model.cfg
          lowercase_	:				Dict													=	fs_config.final_dropout
          lowercase_	:				Dict													=	fs_config.layerdrop
     lowercase_	:				Optional[int]													=	fs_config.activation_dropout
     lowercase_	:				Any													=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     lowercase_	:				List[Any]													=	fs_config.attention_dropout
     lowercase_	:				Tuple													=	fs_config.dropout_input
     lowercase_	:				List[Any]													=	fs_config.dropout
     lowercase_	:				Any													=	fs_config.mask_channel_length
     lowercase_	:				str													=	fs_config.mask_channel_prob
     lowercase_	:				Optional[Any]													=	fs_config.mask_length
     lowercase_	:				Tuple													=	fs_config.mask_prob

     lowercase_	:				List[Any]													=	'Wav2Vec2FeatureExtractor'
     lowercase_	:				Union[str, Any]													=	'Wav2Vec2CTCTokenizer'

     return config



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     int=None     ,			__SCREAMING_SNAKE_CASE							:     Any=None     ,			__SCREAMING_SNAKE_CASE							:     Tuple=True      ):

     if is_finetuned:
          lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]     ,			arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
     else:
          lowercase_							,  lowercase_							,  lowercase_	:				Union[str, Any]													=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

     if config_path is not None:
          lowercase_	:				List[str]													=	SEWConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
     else:
          lowercase_	:				Tuple													=	convert_config(model[0]     ,			__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	model[0].eval()

     lowercase_	:				List[Any]													=	True if config.feat_extract_norm == 'layer' else False
     lowercase_	:				Optional[Any]													=	WavaVecaFeatureExtractor(
         feature_size=1     ,			sampling_rate=1_60_00     ,			padding_value=0     ,			do_normalize=__SCREAMING_SNAKE_CASE     ,			return_attention_mask=__SCREAMING_SNAKE_CASE     ,			)

     if is_finetuned:
          if dict_path:
               lowercase_	:				Dict													=	Dictionary.load(__SCREAMING_SNAKE_CASE      )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowercase_	:				str													=	target_dict.pad_index
               lowercase_	:				Union[str, Any]													=	target_dict.bos_index
               lowercase_	:				Tuple													=	target_dict.pad_index
               lowercase_	:				List[Any]													=	target_dict.bos_index
               lowercase_	:				Any													=	target_dict.eos_index
               lowercase_	:				str													=	len(target_dict.symbols      )
               lowercase_	:				Optional[Any]													=	os.path.join(__SCREAMING_SNAKE_CASE     ,			'vocab.json'      )
               if not os.path.isdir(__SCREAMING_SNAKE_CASE      ):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE      )      )
                    return
               os.makedirs(__SCREAMING_SNAKE_CASE     ,			exist_ok=__SCREAMING_SNAKE_CASE      )
               with open(__SCREAMING_SNAKE_CASE     ,			'w'     ,			encoding='utf-8'      ) as vocab_handle:
                    json.dump(target_dict.indices     ,			__SCREAMING_SNAKE_CASE      )
               lowercase_	:				Union[str, Any]													=	WavaVecaCTCTokenizer(
                   __SCREAMING_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=__SCREAMING_SNAKE_CASE     ,			)
               lowercase_	:				Tuple													=	WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE     ,			tokenizer=__SCREAMING_SNAKE_CASE      )
               processor.save_pretrained(__SCREAMING_SNAKE_CASE      )

          lowercase_	:				List[str]													=	SEWForCTC(__SCREAMING_SNAKE_CASE      )
     else:
          lowercase_	:				Any													=	SEWModel(__SCREAMING_SNAKE_CASE      )
          feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE      )

     recursively_load_weights(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       __SCREAMING_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_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"
       )
       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )

