






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


a    =       torch.cuda.is_available()
a    =       [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
a    =       ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
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''': 42,
    '''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''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''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__						(  A		,				A					):
   lowercase__						:	Any         = """\n""".join(A					)
   Path(A					).open("""w"""					).writelines(A					)
def       a__						(  A					):
   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(A		,				F'''{split}.source'''					)		,				A					)
      _dump_articles(os.path.join(A		,				F'''{split}.target'''					)		,				A					)
   return tmp_dir






class        __snake_case(					__UpperCamelCase      ):



 @classmethod
 def 						UpperCAmelCase    (	cls   :   Optional[Any]	):

    """simple docstring"""






    return cls



 @require_torch_multi_gpu
 def 						UpperCAmelCase    (	self   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Any         = {
        """no_teacher""": True,
        """freeze_encoder""": True,
        """gpus""": 2,
        """overwrite_output_dir""": True,
        """sortish_sampler""": True,
    }
    self._test_distiller_cli_fork(UpperCAmelCase__ ,		check_contents=UpperCAmelCase__	)







 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   int=True	):

    """simple docstring"""






    lowercase__						:	int         = {
        """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__	)
    lowercase__						:	dict         = CHEAP_ARGS.copy()
    lowercase__						:	str         = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
    lowercase__						:	List[Any]         = self.get_auto_remove_tmp_dir()
    args_d.update(data_dir=UpperCAmelCase__ ,		output_dir=UpperCAmelCase__ ,		**UpperCAmelCase__	)

    def convert(UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   Optional[Any]	):
       if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
          return ""
       if v is False or v is None:
          return ""
       if v is True:  # or len(str(v))==0:
          return f'''--{k}'''
       return f'''--{k}={v}'''

    lowercase__						:	Tuple         = [x for x in (convert(UpperCAmelCase__ ,		UpperCAmelCase__	) for k, v in args_d.items()) if len(UpperCAmelCase__	)]
    lowercase__						:	List[Any]         = [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
    execute_subprocess_async(UpperCAmelCase__ ,		env=self.get_env()	)

    lowercase__						:	Optional[int]         = os.listdir(UpperCAmelCase__	)
    lowercase__						:	Dict         = {os.path.basename(UpperCAmelCase__	) for p in contents}
    lowercase__						:	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__	)

    # get the following from the module, (we don't have access to `model` here)
    lowercase__						:	Dict         = os.path.join(UpperCAmelCase__ ,		"""metrics.json"""	)
    lowercase__						:	List[Any]         = """rouge2"""

    lowercase__						:	List[Any]         = load_json(UpperCAmelCase__	)
    # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
    print(UpperCAmelCase__	)
    lowercase__						:	Any         = metrics["""val"""][-1]
    self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""] ,		0.01	)
    self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}'''] ,		UpperCAmelCase__	)
    self.assertEqual(len(metrics["""test"""]	) ,		1	)
    lowercase__						:	Dict         = int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1	)
    self.assertEqual(len(metrics["""val"""]	) ,		UpperCAmelCase__	)










import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a    =       logging.get_logger(__name__)
def       a__						(  A		,				A		,				A					):
   lowercase__						:	int         = WavLMForSequenceClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	Dict         = downstream_dict["""projector.weight"""]
   lowercase__						:	int         = downstream_dict["""projector.bias"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.post_net.linear.weight"""]
   lowercase__						:	List[str]         = downstream_dict["""model.post_net.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Tuple         = WavLMForAudioFrameClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	List[Any]         = downstream_dict["""model.linear.weight"""]
   lowercase__						:	Optional[int]         = downstream_dict["""model.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Optional[int]         = WavLMForXVector.from_pretrained(A		,				config=A					)
   lowercase__						:	List[str]         = downstream_dict["""connector.weight"""]
   lowercase__						:	int         = downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
      lowercase__						:	Optional[Any]         = downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      lowercase__						:	List[str]         = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   lowercase__						:	Union[str, Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   lowercase__						:	Any         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   lowercase__						:	List[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   lowercase__						:	List[Any]         = downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def       a__						(  A		,				A		,				A		,				A					):
   lowercase__						:	List[Any]         = torch.load(A		,				map_location="""cpu"""					)

   lowercase__						:	Tuple         = checkpoint["""Downstream"""]

   lowercase__						:	List[str]         = WavLMConfig.from_pretrained(A					)
   lowercase__						:	Dict         = WavaVecaFeatureExtractor.from_pretrained(
       A		,				return_attention_mask=A		,				do_normalize=A					)

   lowercase__						:	Union[str, Any]         = hf_config.architectures[0]
   if arch.endswith("""ForSequenceClassification"""					):
      lowercase__						:	Union[str, Any]         = convert_classification(A		,				A		,				A					)
   elif arch.endswith("""ForAudioFrameClassification"""					):
      lowercase__						:	Dict         = convert_diarization(A		,				A		,				A					)
   elif arch.endswith("""ForXVector"""					):
      lowercase__						:	Dict         = convert_xvector(A		,				A		,				A					)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''					)

   if hf_config.use_weighted_layer_sum:
      lowercase__						:	Union[str, Any]         = checkpoint["""Featurizer"""]["""weights"""]

   hf_feature_extractor.save_pretrained(A					)
   hf_model.save_pretrained(A					)


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)


