




"""simple docstring"""


from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     ["input_values", "padding_mask"]






   def __init__(		self       :		List[Any]   ,     UpperCamelCase       :		int = 1   ,     UpperCamelCase       :		int = 2_40_00   ,     UpperCamelCase       :		float = 0.0   ,     UpperCamelCase       :		float = None   ,     UpperCamelCase       :		float = None   ,     **UpperCamelCase       :		Optional[Any]   ,     )			->						List[str]:

         """simple docstring"""
         super().__init__(feature_size=UpperCamelCase   ,     sampling_rate=UpperCamelCase   ,     padding_value=UpperCamelCase   ,     **UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	chunk_length_s
         lowerCAmelCase__      :			Optional[Any]   =	overlap






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Optional[int]:

         """simple docstring"""
         if self.chunk_length_s is None:
               return None
         else:
               return int(self.chunk_length_s * self.sampling_rate   )






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Optional[int]:

         """simple docstring"""
         if self.chunk_length_s is None or self.overlap is None:
               return None
         else:
               return max(1   ,     int((1.0 - self.overlap) * self.chunk_length   )   )






   def __call__(		self       :		Optional[Any]   ,     UpperCamelCase       :		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,     UpperCamelCase       :		Optional[Union[bool, str, PaddingStrategy]] = None   ,     UpperCamelCase       :		Optional[bool] = False   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     )			->						BatchFeature:

         """simple docstring"""
         if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                     raise ValueError(
                         f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                         f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                         f""" {self.sampling_rate} and not {sampling_rate}."""   )
         else:
               logger.warning(
                   """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                   """Failing to do so can result in silent errors that might be hard to debug."""   )

         if padding and truncation:
               raise ValueError("""Both padding and truncation were set. Make sure you only set one."""   )
         elif padding is None:
               # by default let's pad the inputs
               lowerCAmelCase__      :			Tuple   =	True

         lowerCAmelCase__      :			Tuple   =	bool(
             isinstance(UpperCamelCase   ,     (list, tuple)   ) and (isinstance(raw_audio[0]   ,     (np.ndarray, tuple, list)   ))   )

         if is_batched:
               lowerCAmelCase__      :			Optional[int]   =	[np.asarray(UpperCamelCase   ,     dtype=np.floataa   ).T for audio in raw_audio]
         elif not is_batched and not isinstance(UpperCamelCase   ,     np.ndarray   ):
               lowerCAmelCase__      :			List[Any]   =	np.asarray(UpperCamelCase   ,     dtype=np.floataa   )
         elif isinstance(UpperCamelCase   ,     np.ndarray   ) and raw_audio.dtype is np.dtype(np.floataa   ):
               lowerCAmelCase__      :			Union[str, Any]   =	raw_audio.astype(np.floataa   )

         # always return batch
         if not is_batched:
               lowerCAmelCase__      :			int   =	[np.asarray(UpperCamelCase   ).T]

         # verify inputs are valid
         for idx, example in enumerate(UpperCamelCase   ):
               if example.ndim > 2:
                     raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""   )
               if self.feature_size == 1 and example.ndim != 1:
                     raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""   )
               if self.feature_size == 2 and example.shape[-1] != 2:
                     raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""   )

         lowerCAmelCase__      :			Optional[Any]   =	None
         lowerCAmelCase__      :			Optional[int]   =	BatchFeature({"""input_values""": raw_audio}   )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                     lowerCAmelCase__      :			Any   =	min(array.shape[0] for array in raw_audio   )
                     lowerCAmelCase__      :			List[str]   =	int(np.floor(max_length / self.chunk_stride   )   )
                     lowerCAmelCase__      :			Dict   =	(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                     lowerCAmelCase__      :			Any   =	max(array.shape[0] for array in raw_audio   )
                     lowerCAmelCase__      :			List[str]   =	int(np.ceil(max_length / self.chunk_stride   )   )
                     lowerCAmelCase__      :			List[Any]   =	(nb_step - 1) * self.chunk_stride + self.chunk_length
                     lowerCAmelCase__      :			Tuple   =	"""max_length"""
               else:
                     lowerCAmelCase__      :			Dict   =	input_values

        # normal padding on batch
         if padded_inputs is None:
               lowerCAmelCase__      :			List[str]   =	self.pad(
                   UpperCamelCase   ,     max_length=UpperCamelCase   ,     truncation=UpperCamelCase   ,     padding=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     )
               if padding:
                     lowerCAmelCase__      :			str   =	padded_inputs.pop("""attention_mask"""   )

         lowerCAmelCase__      :			Optional[Any]   =	[]
         for example in padded_inputs.pop("""input_values"""   ):
               if self.feature_size == 1:
                     lowerCAmelCase__      :			Optional[int]   =	example[..., None]
               input_values.append(example.T   )

         lowerCAmelCase__      :			str   =	input_values
         if return_tensors is not None:
               lowerCAmelCase__      :			Union[str, Any]   =	padded_inputs.convert_to_tensors(UpperCamelCase   )

         return padded_inputs











"""simple docstring"""


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


_A													=      logging.getLogger(__name__)


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Union[str, Any]     				=     "token-classification"






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		Any   )			->						Optional[int]:

         """simple docstring"""
         if type(UpperCamelCase   ) == dict:
               lowerCAmelCase__      :			Optional[int]   =	Namespace(**UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	import_module("""tasks"""   )
         try:
               lowerCAmelCase__      :			Union[str, Any]   =	getattr(UpperCamelCase   ,     hparams.task_type   )
               lowerCAmelCase__      :			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__()}"""   )
         lowerCAmelCase__      :			Optional[Any]   =	self.token_classification_task.get_labels(hparams.labels   )
         lowerCAmelCase__      :			Dict   =	CrossEntropyLoss().ignore_index
         super().__init__(UpperCamelCase   ,     len(self.labels   )   ,     self.mode   )






   def 	_lowerCAmelCase   (		self       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						str:

         """simple docstring"""
         return self.model(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Any   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
         if self.config.model_type != "distilbert":
               lowerCAmelCase__      :			List[str]   =	(
                   batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
               )  # XLM and RoBERTa don"t use token_type_ids

         lowerCAmelCase__      :			Tuple   =	self(**UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}






   def 	_lowerCAmelCase   (		self       :		Any   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.hparams
         for mode in ["train", "dev", "test"]:
               lowerCAmelCase__      :			Union[str, Any]   =	self._feature_file(UpperCamelCase   )
               if os.path.exists(UpperCamelCase   ) and not args.overwrite_cache:
                     logger.info("""Loading features from cached file %s"""   ,     UpperCamelCase   )
                     lowerCAmelCase__      :			Tuple   =	torch.load(UpperCamelCase   )
               else:
                     logger.info("""Creating features from dataset file at %s"""   ,     args.data_dir   )
                     lowerCAmelCase__      :			Union[str, Any]   =	self.token_classification_task.read_examples_from_file(args.data_dir   ,     UpperCamelCase   )
                     lowerCAmelCase__      :			Tuple   =	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 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		bool = False   )			->						DataLoader:

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






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		List[str]   )			->						List[str]:

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






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	torch.stack([x["""val_loss"""] for x in outputs]   ).mean()
         lowerCAmelCase__      :			Any   =	np.concatenate([x["""pred"""] for x in outputs]   ,     axis=0   )
         lowerCAmelCase__      :			List[str]   =	np.argmax(UpperCamelCase   ,     axis=2   )
         lowerCAmelCase__      :			str   =	np.concatenate([x["""target"""] for x in outputs]   ,     axis=0   )

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

         lowerCAmelCase__      :			Optional[int]   =	{
             """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   ),
         }

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






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		List[Any]   )			->						Any:

         """simple docstring"""
         # when stable
         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Any   =	self._eval_end(UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	ret["""log"""]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		int   )			->						Optional[Any]:

         """simple docstring"""
         # updating to test_epoch_end instead of deprecated test_end
         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	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
         lowerCAmelCase__      :			int   =	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 	_lowerCAmelCase   (		UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]   )			->						List[str]:

         """simple docstring"""
         # 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__":
  _A													=      argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A													=      NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A													=      parser.parse_args()
  _A													=      NERTransformer(args)
  _A													=      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
    _A													=      sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A													=      model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)





