




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


UpperCAmelCase_   							=      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCAmelCase_   							=      {
    '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
UpperCAmelCase_   							=      {
    '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
}
UpperCAmelCase_   							=      sorted(arg_to_scheduler.keys())
UpperCAmelCase_   							=      '{' + ', '.join(arg_to_scheduler_choices) + '}'





class        lowerCamelCase__(   pl.LightningModule):
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					argparse.Namespace       ,			UpperCamelCase_:					List[Any]=None       ,			UpperCamelCase_:					int="base"       ,			UpperCamelCase_:					int=None       ,			UpperCamelCase_:					Optional[int]=None       ,			UpperCamelCase_:					List[Any]=None       ,			**UpperCamelCase_:					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_  )
             __lowerCamelCase  =		0
             __lowerCamelCase  =		Path(self.hparams.output_dir  )
             __lowerCamelCase  =		self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                    __lowerCamelCase  =		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:
                    __lowerCamelCase  =		config

             __lowerCamelCase  =		("""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:
                    __lowerCamelCase  =		AutoTokenizer.from_pretrained(
                        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,			cache_dir=UpperCamelCase_       ,			)
             else:
                    __lowerCamelCase  =		tokenizer
             __lowerCamelCase  =		MODEL_MODES[mode]
             if model is None:
                    __lowerCamelCase  =		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:
                    __lowerCamelCase  =		model
      def      lowerCAmelCase__		(							self:					Union[str, Any]       ,			*UpperCamelCase_:					Tuple       ,			**UpperCamelCase_:					List[Any]  ):
             __lowerCamelCase  =		self.model_type.from_pretrained(*UpperCamelCase_       ,			**UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		arg_to_scheduler[self.hparams.lr_scheduler]
             __lowerCamelCase  =		get_schedule_func(
                 self.opt       ,			num_warmup_steps=self.hparams.warmup_steps       ,			num_training_steps=self.total_steps()  )
             __lowerCamelCase  =		{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
             return scheduler
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		self.model
             __lowerCamelCase  =		["""bias""", """LayerNorm.weight"""]
             __lowerCamelCase  =		[
                 {
                     """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:
                    __lowerCamelCase  =		Adafactor(
                        UpperCamelCase_       ,			lr=self.hparams.learning_rate       ,			scale_parameter=UpperCamelCase_       ,			relative_step=UpperCamelCase_  )

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

             __lowerCamelCase  =		self.get_lr_scheduler()

             return [optimizer], [scheduler]
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					Tuple  ):
             return self.validation_step(UpperCamelCase_       ,			UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Optional[int]       ,			UpperCamelCase_:					Optional[int]  ):
             return self.validation_end(UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase  =		max(1       ,			self.hparams.gpus  )  # TODO: consider num_tpu_cores
             __lowerCamelCase  =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
             return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
      def      lowerCAmelCase__		(							self:					Union[str, Any]       ,			UpperCamelCase_:					Union[str, Any]  ):
             if stage == "test":
                    __lowerCamelCase  =		len(self.test_dataloader().dataset  )
             else:
                    __lowerCamelCase  =		self.get_dataloader("""train"""       ,			self.hparams.train_batch_size       ,			shuffle=UpperCamelCase_  )
                    __lowerCamelCase  =		len(self.train_dataloader().dataset  )
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					bool = False  ):
             raise NotImplementedError("""You must implement this for your task"""  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             return self.train_loader
      def      lowerCAmelCase__		(							self:					int  ):
             return self.get_dataloader("""dev"""       ,			self.hparams.eval_batch_size       ,			shuffle=UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Any  ):
             return self.get_dataloader("""test"""       ,			self.hparams.eval_batch_size       ,			shuffle=UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					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      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Dict[str, Any]  ):
             __lowerCamelCase  =		self.output_dir.joinpath("""best_tfmr"""  )
             __lowerCamelCase  =		self.step_count
             self.model.save_pretrained(UpperCamelCase_  )
             self.tokenizer.save_pretrained(UpperCamelCase_  )




      @staticmethod
      def      lowerCAmelCase__		(							UpperCamelCase_:					Tuple       ,			UpperCamelCase_:					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        lowerCamelCase__(   pl.Callback):
      def      lowerCAmelCase__		(							self:					List[Any]       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					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        lowerCamelCase__(   pl.Callback):
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		trainer.lr_schedulers[0]["""scheduler"""]
             __lowerCamelCase  =		{F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
             pl_module.logger.log_metrics(UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]       ,			UpperCamelCase_:					pl.Trainer       ,			UpperCamelCase_:					pl.LightningModule  ):
             rank_zero_info("""***** Validation results *****"""  )
             __lowerCamelCase  =		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      lowerCAmelCase__		(							self:					Union[str, Any]       ,			UpperCamelCase_:					pl.Trainer       ,			UpperCamelCase_:					pl.LightningModule  ):
             rank_zero_info("""***** Test results *****"""  )
             __lowerCamelCase  =		trainer.callback_metrics
             # Log and save results to file
             __lowerCamelCase  =		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    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   str      ):





       '''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    lowerCamelCase__			(		A__			:   BaseTransformer    ,     A__			:   argparse.Namespace    ,     A__			:   str=None    ,     A__			:   Dict=True    ,     A__			:   Any=None    ,     A__			:   Any=[]    ,     A__			:   Dict=None    ,     A__			:   List[str]=None    ,     **A__			:   List[Any]    ,     ):





       '''simple docstring'''



       pl.seed_everything(args.seed      )

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

       # add custom checkpoints
       if checkpoint_callback is None:
              __lowerCamelCase  =		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:
              __lowerCamelCase  =		LoggingCallback()

       __lowerCamelCase  =		{}

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

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

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

       __lowerCamelCase  =		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)

