


import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     _SCREAMING_SNAKE_CASE							):
		lowerCamelCase_	:			List[Any]       			=  """token-classification"""






		def __init__(						self				:     Dict   ,  UpperCAmelCase				:     Union[str, Any]):
				if type(UpperCAmelCase) == dict:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						Namespace(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						import_module("tasks")
				try:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						getattr(UpperCAmelCase   ,  hparams.task_type)
						SCREAMING_SNAKE_CASE_				:TokenClassificationTask				     =						token_classification_task_clazz()
				except AttributeError:
						raise ValueError(
						    F"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
						    F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}")
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.token_classification_task.get_labels(hparams.labels)
				SCREAMING_SNAKE_CASE_				:Dict				     =						CrossEntropyLoss().ignore_index
				super().__init__(UpperCAmelCase   ,  len(self.labels)   ,  self.mode)






		def _snake_case		(						self				:     Optional[int]   ,  **UpperCAmelCase				:     Union[str, Any]):
				return self.model(**UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     List[str]):
				SCREAMING_SNAKE_CASE_				:str				     =						{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
				if self.config.model_type != "distilbert":
						SCREAMING_SNAKE_CASE_				:int				     =						(
						    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
						)  # XLM and RoBERTa don"t use token_type_ids

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Dict				     =						outputs[0]
				# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
				return {"loss": loss}






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.hparams
				for mode in ["train", "dev", "test"]:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self._feature_file(UpperCAmelCase)
						if os.path.exists(UpperCAmelCase) and not args.overwrite_cache:
								logger.info("Loading features from cached file %s"   ,  UpperCAmelCase)
								SCREAMING_SNAKE_CASE_				:str				     =						torch.load(UpperCAmelCase)
						else:
								logger.info("Creating features from dataset file at %s"   ,  args.data_dir)
								SCREAMING_SNAKE_CASE_				:int				     =						self.token_classification_task.read_examples_from_file(args.data_dir   ,  UpperCAmelCase)
								SCREAMING_SNAKE_CASE_				:int				     =						self.token_classification_task.convert_examples_to_features(
								    UpperCAmelCase   ,  self.labels   ,  args.max_seq_length   ,  self.tokenizer   ,  cls_token_at_end=bool(self.config.model_type in ["xlnet"])   ,  cls_token=self.tokenizer.cls_token   ,  cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0   ,  sep_token=self.tokenizer.sep_token   ,  sep_token_extra=UpperCAmelCase   ,  pad_on_left=bool(self.config.model_type in ["xlnet"])   ,  pad_token=self.tokenizer.pad_token_id   ,  pad_token_segment_id=self.tokenizer.pad_token_type_id   ,  pad_token_label_id=self.pad_token_label_id   ,  )
								logger.info("Saving features into cached file %s"   ,  UpperCAmelCase)
								torch.save(UpperCAmelCase   ,  UpperCAmelCase)






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     bool = False):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self._feature_file(UpperCAmelCase)
				logger.info("Loading features from cached file %s"   ,  UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.load(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.tensor([f.input_ids for f in features]   ,  dtype=torch.long)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.tensor([f.attention_mask for f in features]   ,  dtype=torch.long)
				if features[0].token_type_ids is not None:
						SCREAMING_SNAKE_CASE_				:int				     =						torch.tensor([f.token_type_ids for f in features]   ,  dtype=torch.long)
				else:
						SCREAMING_SNAKE_CASE_				:str				     =						torch.tensor([0 for f in features]   ,  dtype=torch.long)
						# HACK(we will not use this anymore soon)
				SCREAMING_SNAKE_CASE_				:Dict				     =						torch.tensor([f.label_ids for f in features]   ,  dtype=torch.long)
				return DataLoader(
				    TensorDataset(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)   ,  batch_size=UpperCAmelCase)






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Optional[Any]):
				"""Compute validation""" ""
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
				if self.config.model_type != "distilbert":
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(
						    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
						)  # XLM and RoBERTa don"t use token_type_ids
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						outputs[:2]
				SCREAMING_SNAKE_CASE_				:Dict				     =						logits.detach().cpu().numpy()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						inputs["""labels"""].detach().cpu().numpy()
				return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Dict				     =						torch.stack([x["val_loss"] for x in outputs]).mean()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.concatenate([x["pred"] for x in outputs]   ,  axis=0)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.argmax(UpperCAmelCase   ,  axis=2)
				SCREAMING_SNAKE_CASE_				:str				     =						np.concatenate([x["target"] for x in outputs]   ,  axis=0)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dict(enumerate(self.labels))
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[[] for _ in range(out_label_ids.shape[0])]
				SCREAMING_SNAKE_CASE_				:str				     =						[[] for _ in range(out_label_ids.shape[0])]

				for i in range(out_label_ids.shape[0]):
						for j in range(out_label_ids.shape[1]):
								if out_label_ids[i, j] != self.pad_token_label_id:
										out_label_list[i].append(label_map[out_label_ids[i][j]])
										preds_list[i].append(label_map[preds[i][j]])

				SCREAMING_SNAKE_CASE_				:List[str]				     =						{
				    """val_loss""": val_loss_mean,
				    """accuracy_score""": accuracy_score(UpperCAmelCase   ,  UpperCAmelCase),
				    """precision""": precision_score(UpperCAmelCase   ,  UpperCAmelCase),
				    """recall""": recall_score(UpperCAmelCase   ,  UpperCAmelCase),
				    """f1""": fa_score(UpperCAmelCase   ,  UpperCAmelCase),
				}

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						dict(results.items())
				SCREAMING_SNAKE_CASE_				:str				     =						results
				return ret, preds_list, out_label_list






		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     List[str]):
				# when stable
				SCREAMING_SNAKE_CASE_				:str				     =						self._eval_end(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						ret["""log"""]
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     int):
				# updating to test_epoch_end instead of deprecated test_end
				SCREAMING_SNAKE_CASE_				:str				     =						self._eval_end(UpperCAmelCase)

				# Converting to the dict required by pl
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
				# pytorch_lightning/trainer/logging.py#L139
				SCREAMING_SNAKE_CASE_				:str				     =						ret["""log"""]
				# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
				return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







		@staticmethod
		def _snake_case		(						UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Tuple):
				# Add NER specific options
				BaseTransformer.add_model_specific_args(UpperCAmelCase   ,  UpperCAmelCase)
				parser.add_argument(
				    "--task_type"   ,  default="NER"   ,  type=UpperCAmelCase   ,  help="Task type to fine tune in training (e.g. NER, POS, etc)")
				parser.add_argument(
				    "--max_seq_length"   ,  default=1_28   ,  type=UpperCAmelCase   ,  help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )   ,  )

				parser.add_argument(
				    "--labels"   ,  default=""   ,  type=UpperCAmelCase   ,  help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."   ,  )
				parser.add_argument(
				    "--gpus"   ,  default=0   ,  type=UpperCAmelCase   ,  help="The number of GPUs allocated for this, it is by default 0 meaning none"   ,  )

				parser.add_argument(
				    "--overwrite_cache"   ,  action="store_true"   ,  help="Overwrite the cached training and evaluation sets")

				return parser


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			argparse.ArgumentParser()
		add_generic_args(parser, os.getcwd())
		SCREAMING_SNAKE_CASE__     					=			NERTransformer.add_model_specific_args(parser, os.getcwd())
		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()
		SCREAMING_SNAKE_CASE__     					=			NERTransformer(args)
		SCREAMING_SNAKE_CASE__     					=			generic_train(model, args)

		if args.do_predict:
				# See https://github.com/huggingface/transformers/issues/3159
				# pl use this default format to create a checkpoint:
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
				# /pytorch_lightning/callbacks/model_checkpoint.py#L322
				SCREAMING_SNAKE_CASE__     					=			sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
				SCREAMING_SNAKE_CASE__     					=			model.load_from_checkpoint(checkpoints[-1])
				trainer.test(model)










'''simple docstring'''






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

  lowerCAmelCase_     : Optional[int]     =  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""   ):
    lowerCAmelCase_     : List[str]     =  convert_classification(A__				,       A__				,       A__   )
  elif arch.endswith("""ForAudioFrameClassification"""   ):
    lowerCAmelCase_     : Dict     =  convert_diarization(A__				,       A__				,       A__   )
  elif arch.endswith("""ForXVector"""   ):
    lowerCAmelCase_     : List[Any]     =  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_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

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


if __name__ == "__main__":
  __A   :    List[Any]											= 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   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



