





'''simple docstring'''


import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCamelCase					:						List[Any]					     =							logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_lowerCamelCase					:						int					     =							{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCamelCase					:						Any					     =							{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCamelCase					:						List[str]					     =							sorted(arg_to_scheduler.keys())
_lowerCamelCase					:						Tuple					     =							'{' + ', '.join(arg_to_scheduler_choices) + '}'



class 				__UpperCAmelCase	(							pl.LightningModule    ):







		'''simple docstring'''







		def __init__(self    :   List[Any]   ,   _lowerCAmelCase    :   argparse.Namespace   ,   _lowerCAmelCase    :   List[Any]=None   ,   _lowerCAmelCase    :   int="base"   ,   _lowerCAmelCase    :   int=None   ,   _lowerCAmelCase    :   Optional[int]=None   ,   _lowerCAmelCase    :   List[Any]=None   ,   **_lowerCAmelCase    :   str   ,   ):
			super().__init__()
			# TODO: move to self.save_hyperparameters()
			# self.save_hyperparameters()
			# can also expand arguments into trainer signature for easier reading

			self.save_hyperparameters(UpperCamelCase_   )
			A					=   0
			A					=   Path(self.hparams.output_dir   )
			A					=   self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
				A					=   AutoConfig.from_pretrained(
				    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path   ,   **({"""num_labels""": num_labels} if num_labels is not None else {})   ,   cache_dir=UpperCamelCase_   ,   **UpperCamelCase_   ,   )
			else:
				A					=   config

			A					=   ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
			for p in extra_model_params:
				if getattr(self.hparams   ,   UpperCamelCase_   ,   UpperCamelCase_   ):
					assert hasattr(self.config   ,   UpperCamelCase_   ), F"""model config doesn\'t have a `{p}` attribute"""
					setattr(self.config   ,   UpperCamelCase_   ,   getattr(self.hparams   ,   UpperCamelCase_   )   )

			if tokenizer is None:
				A					=   AutoTokenizer.from_pretrained(
				    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,   cache_dir=UpperCamelCase_   ,   )
			else:
				A					=   tokenizer
			A					=   MODEL_MODES[mode]
			if model is None:
				A					=   self.model_type.from_pretrained(
				    self.hparams.model_name_or_path   ,   from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path   )   ,   config=self.config   ,   cache_dir=UpperCamelCase_   ,   )
			else:
				A					=   model







		def 							A  (self    :   Union[str, Any]   ,   *_lowerCAmelCase    :   Tuple   ,   **_lowerCAmelCase    :   List[Any]   ):
			A					=   self.model_type.from_pretrained(*UpperCamelCase_   ,   **UpperCamelCase_   )







		def 							A  (self    :   Optional[Any]   ):
			A					=   arg_to_scheduler[self.hparams.lr_scheduler]
			A					=   get_schedule_func(
			    self.opt   ,   num_warmup_steps=self.hparams.warmup_steps   ,   num_training_steps=self.total_steps()   )
			A					=   {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
			return scheduler







		def 							A  (self    :   Dict   ):
			A					=   self.model
			A					=   ["""bias""", """LayerNorm.weight"""]
			A					=   [
			    {
			        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
			        """weight_decay""": self.hparams.weight_decay,
			    },
			    {
			        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )],
			        """weight_decay""": 0.0,
			    },
			]
			if self.hparams.adafactor:
				A					=   Adafactor(
				    UpperCamelCase_   ,   lr=self.hparams.learning_rate   ,   scale_parameter=UpperCamelCase_   ,   relative_step=UpperCamelCase_   )

			else:
				A					=   AdamW(
				    UpperCamelCase_   ,   lr=self.hparams.learning_rate   ,   eps=self.hparams.adam_epsilon   )
			A					=   optimizer

			A					=   self.get_lr_scheduler()

			return [optimizer], [scheduler]







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   Tuple   ):
			return self.validation_step(UpperCamelCase_   ,   UpperCamelCase_   )







		def 							A  (self    :   Optional[int]   ,   _lowerCAmelCase    :   Optional[int]   ):
			return self.validation_end(UpperCamelCase_   )







		def 							A  (self    :   Tuple   ):
			A					=   max(1   ,   self.hparams.gpus   )  # TODO: consider num_tpu_cores
			A					=   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
			return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   Union[str, Any]   ):
			if stage == "test":
				A					=   len(self.test_dataloader().dataset   )
			else:
				A					=   self.get_dataloader("""train"""   ,   self.hparams.train_batch_size   ,   shuffle=UpperCamelCase_   )
				A					=   len(self.train_dataloader().dataset   )







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   bool = False   ):
			raise NotImplementedError("""You must implement this for your task"""   )







		def 							A  (self    :   Union[str, Any]   ):
			return self.train_loader







		def 							A  (self    :   int   ):
			return self.get_dataloader("""dev"""   ,   self.hparams.eval_batch_size   ,   shuffle=UpperCamelCase_   )







		def 							A  (self    :   Any   ):
			return self.get_dataloader("""test"""   ,   self.hparams.eval_batch_size   ,   shuffle=UpperCamelCase_   )







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Dict   ):
			return os.path.join(
			    self.hparams.data_dir   ,   """cached_{}_{}_{}""".format(
			        UpperCamelCase_   ,   list(filter(UpperCamelCase_   ,   self.hparams.model_name_or_path.split("""/"""   )   )   ).pop()   ,   str(self.hparams.max_seq_length   )   ,   )   ,   )







		@pl.utilities.rank_zero_only
		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   Dict[str, Any]   ):
			A					=   self.output_dir.joinpath("""best_tfmr"""   )
			A					=   self.step_count
			self.model.save_pretrained(UpperCamelCase_   )
			self.tokenizer.save_pretrained(UpperCamelCase_   )







		@staticmethod
		def 							A  (_lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   List[str]   ):
			parser.add_argument(
			    """--model_name_or_path"""   ,   default=UpperCamelCase_   ,   type=UpperCamelCase_   ,   required=UpperCamelCase_   ,   help="""Path to pretrained model or model identifier from huggingface.co/models"""   ,   )
			parser.add_argument(
			    """--config_name"""   ,   default=""""""   ,   type=UpperCamelCase_   ,   help="""Pretrained config name or path if not the same as model_name"""   )
			parser.add_argument(
			    """--tokenizer_name"""   ,   default=UpperCamelCase_   ,   type=UpperCamelCase_   ,   help="""Pretrained tokenizer name or path if not the same as model_name"""   ,   )
			parser.add_argument(
			    """--cache_dir"""   ,   default=""""""   ,   type=UpperCamelCase_   ,   help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""   ,   )
			parser.add_argument(
			    """--encoder_layerdrop"""   ,   type=UpperCamelCase_   ,   help="""Encoder layer dropout probability (Optional). Goes into model.config"""   ,   )
			parser.add_argument(
			    """--decoder_layerdrop"""   ,   type=UpperCamelCase_   ,   help="""Decoder layer dropout probability (Optional). Goes into model.config"""   ,   )
			parser.add_argument(
			    """--dropout"""   ,   type=UpperCamelCase_   ,   help="""Dropout probability (Optional). Goes into model.config"""   ,   )
			parser.add_argument(
			    """--attention_dropout"""   ,   type=UpperCamelCase_   ,   help="""Attention dropout probability (Optional). Goes into model.config"""   ,   )
			parser.add_argument("""--learning_rate"""   ,   default=5e-5   ,   type=UpperCamelCase_   ,   help="""The initial learning rate for Adam."""   )
			parser.add_argument(
			    """--lr_scheduler"""   ,   default="""linear"""   ,   choices=UpperCamelCase_   ,   metavar=UpperCamelCase_   ,   type=UpperCamelCase_   ,   help="""Learning rate scheduler"""   ,   )
			parser.add_argument("""--weight_decay"""   ,   default=0.0   ,   type=UpperCamelCase_   ,   help="""Weight decay if we apply some."""   )
			parser.add_argument("""--adam_epsilon"""   ,   default=1e-8   ,   type=UpperCamelCase_   ,   help="""Epsilon for Adam optimizer."""   )
			parser.add_argument("""--warmup_steps"""   ,   default=0   ,   type=UpperCamelCase_   ,   help="""Linear warmup over warmup_steps."""   )
			parser.add_argument("""--num_workers"""   ,   default=4   ,   type=UpperCamelCase_   ,   help="""kwarg passed to DataLoader"""   )
			parser.add_argument("""--num_train_epochs"""   ,   dest="""max_epochs"""   ,   default=3   ,   type=UpperCamelCase_   )
			parser.add_argument("""--train_batch_size"""   ,   default=32   ,   type=UpperCamelCase_   )
			parser.add_argument("""--eval_batch_size"""   ,   default=32   ,   type=UpperCamelCase_   )
			parser.add_argument("""--adafactor"""   ,   action="""store_true"""   )






class 				__UpperCAmelCase	(							pl.Callback    ):







		'''simple docstring'''







		def 							A  (self    :   List[Any]   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   Union[str, Any]   ):
			if (
			    trainer.is_global_zero and trainer.global_rank == 0
			):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
				pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 				__UpperCAmelCase	(							pl.Callback    ):







		'''simple docstring'''







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   Optional[int]   ):
			A					=   trainer.lr_schedulers[0]["""scheduler"""]
			A					=   {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
			pl_module.logger.log_metrics(UpperCamelCase_   )







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   pl.Trainer   ,   _lowerCAmelCase    :   pl.LightningModule   ):
			rank_zero_info("""***** Validation results *****"""   )
			A					=   trainer.callback_metrics
			# Log results
			for key in sorted(UpperCamelCase_   ):
				if key not in ["log", "progress_bar"]:
					rank_zero_info("""{} = {}\n""".format(UpperCamelCase_   ,   str(metrics[key]   )   )   )







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   pl.Trainer   ,   _lowerCAmelCase    :   pl.LightningModule   ):
			rank_zero_info("""***** Test results *****"""   )
			A					=   trainer.callback_metrics
			# Log and save results to file
			A					=   os.path.join(pl_module.hparams.output_dir   ,   """test_results.txt"""   )
			with open(UpperCamelCase_   ,   """w"""   ) as writer:
				for key in sorted(UpperCamelCase_   ):
					if key not in ["log", "progress_bar"]:
						rank_zero_info("""{} = {}\n""".format(UpperCamelCase_   ,   str(metrics[key]   )   )   )
						writer.write("""{} = {}\n""".format(UpperCamelCase_   ,   str(metrics[key]   )   )   )






def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->Dict:



	"""simple docstring"""







	parser.add_argument(
	    """--output_dir"""							,      default=A__							,      type=A__							,      required=A__							,      help="""The output directory where the model predictions and checkpoints will be written."""							,      )
	parser.add_argument(
	    """--fp16"""							,      action="""store_true"""							,      help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""							,      )

	parser.add_argument(
	    """--fp16_opt_level"""							,      type=A__							,      default="""O2"""							,      help=(
	        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
	        """See details at https://nvidia.github.io/apex/amp.html"""
	    )							,      )
	parser.add_argument("""--n_tpu_cores"""							,      dest="""tpu_cores"""							,      type=A__     )
	parser.add_argument("""--max_grad_norm"""							,      dest="""gradient_clip_val"""							,      default=1.0							,      type=A__							,      help="""Max gradient norm"""     )
	parser.add_argument("""--do_train"""							,      action="""store_true"""							,      help="""Whether to run training."""     )
	parser.add_argument("""--do_predict"""							,      action="""store_true"""							,      help="""Whether to run predictions on the test set."""     )
	parser.add_argument(
	    """--gradient_accumulation_steps"""							,      dest="""accumulate_grad_batches"""							,      type=A__							,      default=1							,      help="""Number of updates steps to accumulate before performing a backward/update pass."""							,      )
	parser.add_argument("""--seed"""							,      type=A__							,      default=42							,      help="""random seed for initialization"""     )
	parser.add_argument(
	    """--data_dir"""							,      default=A__							,      type=A__							,      required=A__							,      help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""							,      )




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase=None							,      UpperCAmelCase=True							,      UpperCAmelCase=None							,      UpperCAmelCase=[]							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      **UpperCAmelCase							,      )   ->Tuple:



	"""simple docstring"""







	pl.seed_everything(args.seed     )

	# init model
	A					=   Path(model.hparams.output_dir     )
	odir.mkdir(exist_ok=A__     )

	# add custom checkpoints
	if checkpoint_callback is None:
		A					=   pl.callbacks.ModelCheckpoint(
		    filepath=args.output_dir							,      prefix="""checkpoint"""							,      monitor="""val_loss"""							,      mode="""min"""							,      save_top_k=1     )
	if early_stopping_callback:
		extra_callbacks.append(A__     )
	if logging_callback is None:
		A					=   LoggingCallback()

	A					=   {}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		A					=   16
		# train_params["amp_level"] = args.fp16_opt_level

	if args.gpus > 1:
		A					=   """auto"""  # "ddp"
		A					=   """ddp"""

	A					=   args.accumulate_grad_batches
	A					=   None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
	A					=   """auto"""

	A					=   pl.Trainer.from_argparse_args(
	    A__							,      weights_summary=A__							,      callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]							,      logger=A__							,      **A__							,      )

	if args.do_train:
		trainer.fit(A__     )

	return trainer







import argparse

import torch

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


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




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForAudioFrameClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""model.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForXVector.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""connector.weight"""]
       __lowerCamelCase  =		downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
              __lowerCamelCase  =		downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase  =		downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase  =		downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

       __lowerCamelCase  =		hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""      ):
              __lowerCamelCase  =		convert_classification(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForAudioFrameClassification"""      ):
              __lowerCamelCase  =		convert_diarization(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForXVector"""      ):
              __lowerCamelCase  =		convert_xvector(A__    ,     A__    ,     A__      )
       else:
              raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'      )

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase  =		checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      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.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

