

import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_a			=       logging.getLogger()
_a			=       torch.cuda.is_available()
_a			=       {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 4_2,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1E-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}







def _a			(	SCREAMING_SNAKE_CASE		:      Path   , SCREAMING_SNAKE_CASE		:      list		)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       Tuple            =				'\n'.join(SCREAMING_SNAKE_CASE		)
 Path(SCREAMING_SNAKE_CASE		).open('w'		).writelines(SCREAMING_SNAKE_CASE		)


_a			=       [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_a			=       ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_a			=       '''patrickvonplaten/t5-tiny-random'''
_a			=       '''sshleifer/t5-tinier-random'''
_a			=       '''sshleifer/bart-tiny-random'''
_a			=       '''sshleifer/tiny-mbart'''
_a			=       '''sshleifer/tiny-marian-en-de'''
_a			=       '''stas/tiny-wmt19-en-de'''


_a			=       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]		)   ->   Optional[Any]:

 """simple docstring"""






 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(SCREAMING_SNAKE_CASE   , f'''{split}.source'''		)   , SCREAMING_SNAKE_CASE		)
  _dump_articles(os.path.join(SCREAMING_SNAKE_CASE   , f'''{split}.target'''		)   , SCREAMING_SNAKE_CASE		)
 return tmp_dir






class 							A_			(		snake_case__ ):


     @classmethod
     def   UpperCAmelCase  (							cls      :		Dict )							->     Any:
      logging.disable(logging.CRITICAL )  # remove noisy download output from tracebacks
      return cls


     @slow
     @require_torch_gpu
     def   UpperCAmelCase  (							self      :		str )							->     str:
      __lowerCAmelCase:       Optional[Any]            =				list_models()
      __lowerCAmelCase:       Tuple            =				'sshleifer'
      __lowerCAmelCase:       Tuple            =				[x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase )]
      __lowerCAmelCase:       Union[str, Any]            =				['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
      __lowerCAmelCase:       List[Any]            =				[]
      for m in model_ids:
       if m in allowed_to_be_broken:
        continue
       try:
        AutoConfig.from_pretrained(UpperCAmelCase )
       except Exception:
        failures.append(UpperCAmelCase )
      assert not failures, F'''The following models could not be loaded through AutoConfig: {failures}'''


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     List[str]:
      __lowerCAmelCase:       Optional[Any]            =				{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
      self._test_distiller_cli(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[Any] )							->     List[str]:
      __lowerCAmelCase:       Tuple            =				{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'max_epochs': 4,
          'val_check_interval': 0.25,
          'alpha_hid': 2.0,
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
      }
      __lowerCAmelCase:       Optional[int]            =				self._test_distiller_cli(UpperCAmelCase      ,  check_contents=UpperCAmelCase )

      __lowerCAmelCase:       Optional[Any]            =				list(Path(model.output_dir ).glob('*.ckpt' ) )
      self.assertEqual(1      ,  len(UpperCAmelCase ) )
      __lowerCAmelCase:       Dict            =				list(Path(model.output_dir ).glob('**/*.bin' ) )
      self.assertEqual(len(UpperCAmelCase )      ,  2 )
      __lowerCAmelCase:       Tuple            =				lmap(str.strip      ,  Path(model.hparams.data_dir ).joinpath('test.source' ).open().readlines() )
      __lowerCAmelCase:       Optional[int]            =				tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(UpperCAmelCase      ,  UpperCAmelCase      ,  str(model.output_dir / 'best_tfmr' ) )
      self.assertTrue(Path(UpperCAmelCase ).exists() )

      __lowerCAmelCase:       List[str]            =				self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]      ,  transformer_ckpts[0].parent      ,  UpperCAmelCase )
      assert os.path.exists(os.path.join(UpperCAmelCase      ,  'pytorch_model.bin' ) )


     def   UpperCAmelCase  (							self      :		Dict )							->     Optional[Any]:
      __lowerCAmelCase:       str            =				AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase )
      __lowerCAmelCase     ,							__lowerCAmelCase:       str            =				model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
      __lowerCAmelCase:       Dict            =				torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]      ,  dtype=torch.long      ,  device=model.device )
      __lowerCAmelCase:       int            =				target_ids[:, :-1].contiguous()  # Why this line?
      __lowerCAmelCase:       Optional[Any]            =				target_ids[:, 1:].clone()  # why clone?
      __lowerCAmelCase:       List[Any]            =				model(
          UpperCAmelCase      ,  attention_mask=UpperCAmelCase      ,  decoder_input_ids=UpperCAmelCase      ,  labels=UpperCAmelCase      ,  use_cache=UpperCAmelCase ).loss

      __lowerCAmelCase:       List[Any]            =				model(UpperCAmelCase      ,  attention_mask=UpperCAmelCase      ,  decoder_input_ids=UpperCAmelCase      ,  use_cache=UpperCAmelCase ).logits

      __lowerCAmelCase:       Union[str, Any]            =				nn.functional.log_softmax(UpperCAmelCase      ,  dim=-1 )
      __lowerCAmelCase     ,							__lowerCAmelCase:       Any            =				label_smoothed_nll_loss(
          UpperCAmelCase      ,  UpperCAmelCase      ,  0.1      ,  ignore_index=model.config.pad_token_id )
      with self.assertRaises(UpperCAmelCase ):
       # TODO: understand why this breaks
       self.assertEqual(UpperCAmelCase      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		int )							->     Union[str, Any]:
      __lowerCAmelCase:       List[str]            =				{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'num_train_epochs': 4,
          'val_check_interval': 0.25,
          'alpha_hid': 2.0,
          'task': 'translation',
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
          'tokenizer_name': MBART_TINY,
          'teacher': MBART_TINY,
          'src_lang': 'en_XX',
          'tgt_lang': 'ro_RO',
      }
      __lowerCAmelCase:       Union[str, Any]            =				self._test_distiller_cli(UpperCAmelCase      ,  check_contents=UpperCAmelCase )
      assert model.model.config.model_type == "mbart"

      __lowerCAmelCase:       Tuple            =				list(Path(model.output_dir ).glob('*.ckpt' ) )
      self.assertEqual(1      ,  len(UpperCAmelCase ) )
      __lowerCAmelCase:       int            =				list(Path(model.output_dir ).glob('**/*.bin' ) )
      __lowerCAmelCase:       List[str]            =				list(Path(model.output_dir ).glob('best_tfmr/*' ) )
      assert len(UpperCAmelCase ) > 2
      self.assertEqual(len(UpperCAmelCase )      ,  2 )


     def   UpperCAmelCase  (							self      :		Any )							->     Any:
      __lowerCAmelCase:       Any            =				{
          'student_encoder_layers': 1,
          'student_decoder_layers': 1,
          'alpha_hid': 2.0,
          'teacher': T5_TINY,
          'model_name_or_path': T5_TINY,
          'tokenizer_name': T5_TINY,
      }
      self._test_distiller_cli(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     List[Any]:
      __lowerCAmelCase:       Tuple            =				{
          'teacher': T5_TINY,
          'student': T5_TINIER,
          'model_name_or_path': T5_TINIER,
          'tokenizer_name': T5_TINIER,
      }
      self._test_distiller_cli(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Optional[int]=True )							->     Any:
      __lowerCAmelCase:       List[str]            =				{
          'label_smoothing': 0.0,
          'early_stopping_patience': -1,
          'train_batch_size': 1,
          'eval_batch_size': 2,
          'max_epochs': 2,
          'alpha_mlm': 0.2,
          'alpha_ce': 0.8,
          'do_predict': True,
          'model_name_or_path': 'sshleifer/tinier_bart',
          'teacher': CHEAP_ARGS['model_name_or_path'],
          'val_check_interval': 0.5,
      }
      default_updates.update(UpperCAmelCase )
      __lowerCAmelCase:       dict            =				CHEAP_ARGS.copy()
      __lowerCAmelCase:       Any            =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
      __lowerCAmelCase:       Any            =				self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=UpperCAmelCase      ,  output_dir=UpperCAmelCase      ,  **UpperCAmelCase )
      __lowerCAmelCase:       int            =				distill_main(argparse.Namespace(**UpperCAmelCase ) )
      if not check_contents:
       return model
      __lowerCAmelCase:       List[str]            =				os.listdir(UpperCAmelCase )
      __lowerCAmelCase:       int            =				{os.path.basename(UpperCAmelCase ) for p in contents}
      __lowerCAmelCase:       List[str]            =				[p for p in contents if p.endswith('ckpt' )]
      assert len(UpperCAmelCase ) > 0

      self.assertIn('test_generations.txt'      ,  UpperCAmelCase )
      self.assertIn('test_results.txt'      ,  UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				load_json(model.metrics_save_path )
      __lowerCAmelCase:       int            =				metrics['val'][-1]
      self.assertGreaterEqual(last_step_stats['val_avg_gen_time']      ,  0.01 )
      self.assertGreaterEqual(1.0      ,  last_step_stats['val_avg_gen_time'] )
      self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']      ,  UpperCAmelCase )
      __lowerCAmelCase:       Tuple            =				int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1 )
      self.assertEqual(len(metrics['val'] )      ,  UpperCAmelCase )
      self.assertEqual(len(metrics['test'] )      ,  1 )
      return model







class 							A_			(		snake_case__ ):


     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]      ,  )
     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Union[str, Any] )							->     Union[str, Any]:
      __lowerCAmelCase:       dict            =				CHEAP_ARGS.copy()
      __lowerCAmelCase:       str            =				'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
      __lowerCAmelCase:       str            =				0.1 if task == 'translation' else 0

      __lowerCAmelCase:       Tuple            =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
      __lowerCAmelCase:       str            =				self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=UpperCAmelCase      ,  model_name_or_path=UpperCAmelCase      ,  tokenizer_name=UpperCAmelCase      ,  train_batch_size=2      ,  eval_batch_size=2      ,  output_dir=UpperCAmelCase      ,  do_predict=UpperCAmelCase      ,  task=UpperCAmelCase      ,  src_lang='en_XX'      ,  tgt_lang='ro_RO'      ,  freeze_encoder=UpperCAmelCase      ,  freeze_embeds=UpperCAmelCase      ,  )
      assert "n_train" in args_d
      __lowerCAmelCase:       int            =				argparse.Namespace(**UpperCAmelCase )
      __lowerCAmelCase:       Tuple            =				main(UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
       __lowerCAmelCase:       Optional[int]            =				module.model.lm_head
       assert not lm_head.weight.requires_grad
       assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
       __lowerCAmelCase:       List[Any]            =				module.model.model
       __lowerCAmelCase:       Optional[int]            =				fsmt.decoder.embed_positions
       assert not embed_pos.weight.requires_grad
       assert not fsmt.decoder.embed_tokens.weight.requires_grad
       # check that embeds are not the same
       assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
       __lowerCAmelCase:       Optional[int]            =				module.model.model
       __lowerCAmelCase:       int            =				bart.decoder.embed_positions
       assert not embed_pos.weight.requires_grad
       assert not bart.shared.weight.requires_grad
       # check that embeds are the same
       assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
       assert bart.decoder.embed_tokens == bart.shared

      __lowerCAmelCase:       Any            =				load_json(module.output_dir / 'text_batch.json' )
      assert isinstance(UpperCAmelCase      ,  UpperCAmelCase )
      assert len(UpperCAmelCase ) >= 4


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Dict:
      __lowerCAmelCase:       dict            =				CHEAP_ARGS.copy()

      __lowerCAmelCase:       int            =				'summarization'
      __lowerCAmelCase:       Tuple            =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

      args_d.update(
          data_dir=UpperCAmelCase      ,  tokenizer_name=UpperCAmelCase      ,  train_batch_size=2      ,  eval_batch_size=2      ,  do_predict=UpperCAmelCase      ,  task=UpperCAmelCase      ,  src_lang='en_XX'      ,  tgt_lang='ro_RO'      ,  freeze_encoder=UpperCAmelCase      ,  freeze_embeds=UpperCAmelCase      ,  )

      # test models whose config includes the extra_model_args
      __lowerCAmelCase:       List[str]            =				BART_TINY
      __lowerCAmelCase:       Dict            =				self.get_auto_remove_tmp_dir()
      __lowerCAmelCase:       Union[str, Any]            =				args_d.copy()
      args_da.update(
          model_name_or_path=UpperCAmelCase      ,  output_dir=UpperCAmelCase      ,  )
      __lowerCAmelCase:       Optional[Any]            =				('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
      for p in extra_model_params:
       __lowerCAmelCase:       Tuple            =				0.5
      __lowerCAmelCase:       Dict            =				argparse.Namespace(**UpperCAmelCase )
      __lowerCAmelCase:       List[Any]            =				main(UpperCAmelCase )
      for p in extra_model_params:
       assert getattr(model.config      ,  UpperCAmelCase ) == 0.5, F'''failed to override the model config for param {p}'''

      # test models whose config doesn't include the extra_model_args
      __lowerCAmelCase:       Tuple            =				T5_TINY
      __lowerCAmelCase:       int            =				self.get_auto_remove_tmp_dir()
      __lowerCAmelCase:       Tuple            =				args_d.copy()
      args_da.update(
          model_name_or_path=UpperCAmelCase      ,  output_dir=UpperCAmelCase      ,  )
      __lowerCAmelCase:       int            =				'encoder_layerdrop'
      __lowerCAmelCase:       Tuple            =				0.5
      __lowerCAmelCase:       List[str]            =				argparse.Namespace(**UpperCAmelCase )
      with pytest.raises(UpperCAmelCase ) as excinfo:
       __lowerCAmelCase:       List[Any]            =				main(UpperCAmelCase )
      assert str(excinfo.value ) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''


     def   UpperCAmelCase  (							self      :		int )							->     Optional[int]:
      __lowerCAmelCase:       dict            =				CHEAP_ARGS.copy()

      __lowerCAmelCase:       Dict            =				'summarization'
      __lowerCAmelCase:       Optional[Any]            =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

      __lowerCAmelCase:       int            =				BART_TINY
      __lowerCAmelCase:       Dict            =				self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=UpperCAmelCase      ,  model_name_or_path=UpperCAmelCase      ,  output_dir=UpperCAmelCase      ,  tokenizer_name=UpperCAmelCase      ,  train_batch_size=2      ,  eval_batch_size=2      ,  do_predict=UpperCAmelCase      ,  task=UpperCAmelCase      ,  src_lang='en_XX'      ,  tgt_lang='ro_RO'      ,  freeze_encoder=UpperCAmelCase      ,  freeze_embeds=UpperCAmelCase      ,  )

      # emulate finetune.py
      __lowerCAmelCase:       Any            =				argparse.ArgumentParser()
      __lowerCAmelCase:       int            =				pl.Trainer.add_argparse_args(UpperCAmelCase )
      __lowerCAmelCase:       Dict            =				SummarizationModule.add_model_specific_args(UpperCAmelCase      ,  os.getcwd() )
      __lowerCAmelCase:       Any            =				{'--help': True}

      # --help test
      with pytest.raises(UpperCAmelCase ) as excinfo:
       with CaptureStdout() as cs:
        __lowerCAmelCase:       Union[str, Any]            =				parser.parse_args(UpperCAmelCase )
       assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      __lowerCAmelCase:       str            =				lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      __lowerCAmelCase:       Optional[int]            =				'non_existing_scheduler'
      __lowerCAmelCase:       str            =				{F'''--lr_scheduler={unsupported_param}'''}
      with pytest.raises(UpperCAmelCase ) as excinfo:
       with CaptureStderr() as cs:
        __lowerCAmelCase:       Any            =				parser.parse_args(UpperCAmelCase )
       assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      __lowerCAmelCase:       Optional[Any]            =				F'''invalid choice: \'{unsupported_param}\''''
      assert expected in cs.err, F'''should have bailed on invalid choice of scheduler {unsupported_param}'''

      # --lr_scheduler=existing_scheduler test
      __lowerCAmelCase:       List[Any]            =				'cosine'
      __lowerCAmelCase:       Optional[int]            =				args_d.copy()
      __lowerCAmelCase:       str            =				supported_param
      __lowerCAmelCase:       List[str]            =				argparse.Namespace(**UpperCAmelCase )
      __lowerCAmelCase:       int            =				main(UpperCAmelCase )
      assert (
          getattr(model.hparams      ,  'lr_scheduler' ) == supported_param
      ), F'''lr_scheduler={supported_param} shouldn\'t fail'''








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_a			=       ['''UtteranceLevel''']







@torch.no_grad()
def _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				torch.load(SCREAMING_SNAKE_CASE   , map_location='cpu'		)
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''		)

 __lowerCAmelCase:       str            =				checkpoint['Downstream']

 __lowerCAmelCase:       List[str]            =				HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Any            =				HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   , return_attention_mask=SCREAMING_SNAKE_CASE   , do_normalize=SCREAMING_SNAKE_CASE		)

 if hf_congfig.use_weighted_layer_sum:
  __lowerCAmelCase:       Dict            =				checkpoint['Featurizer']['weights']

 __lowerCAmelCase:       str            =				downstream_dict['projector.weight']
 __lowerCAmelCase:       Any            =				downstream_dict['projector.bias']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.weight']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE		)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  _a			=       argparse.ArgumentParser()
  parser.add_argument(
      '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
  )
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
  parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
  _a			=       parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





