




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


lowerCamelCase__ :   str					=							logging.getLogger(__name__)





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    "token-classification"



 def __init__(							self  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]						):
       if type(_lowerCAmelCase						) == dict:
             SCREAMING_SNAKE_CASE_							=			Namespace(**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			import_module('tasks'						)
       try:
             SCREAMING_SNAKE_CASE_							=			getattr(_lowerCAmelCase				,				hparams.task_type						)
             SCREAMING_SNAKE_CASE_							=			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_							=			self.token_classification_task.get_labels(hparams.labels						)
       SCREAMING_SNAKE_CASE_							=			CrossEntropyLoss().ignore_index
       super().__init__(_lowerCAmelCase				,				len(self.labels						)				,				self.mode						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				**_lowerCAmelCase  :		str						):
       return self.model(**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
       if self.config.model_type != "distilbert":
             SCREAMING_SNAKE_CASE_							=			(
                 batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
             )  # XLM and RoBERTa don"t use token_type_ids

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



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			self.hparams
       for mode in ["train", "dev", "test"]:
             SCREAMING_SNAKE_CASE_							=			self._feature_file(_lowerCAmelCase						)
             if os.path.exists(_lowerCAmelCase						) and not args.overwrite_cache:
                   logger.info('Loading features from cached file %s'				,				_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			torch.load(_lowerCAmelCase						)
             else:
                   logger.info('Creating features from dataset file at %s'				,				args.data_dir						)
                   SCREAMING_SNAKE_CASE_							=			self.token_classification_task.read_examples_from_file(args.data_dir				,				_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			self.token_classification_task.convert_examples_to_features(
                       _lowerCAmelCase				,				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=_lowerCAmelCase				,				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'				,				_lowerCAmelCase						)
                   torch.save(_lowerCAmelCase				,				_lowerCAmelCase						)



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



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Optional[Any]						):
       """Compute validation""" ""
       SCREAMING_SNAKE_CASE_							=			{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
       if self.config.model_type != "distilbert":
             SCREAMING_SNAKE_CASE_							=			(
                 batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
             )  # XLM and RoBERTa don"t use token_type_ids
       SCREAMING_SNAKE_CASE_							=			self(**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			outputs[:2]
       SCREAMING_SNAKE_CASE_							=			logits.detach().cpu().numpy()
       SCREAMING_SNAKE_CASE_							=			inputs['labels'].detach().cpu().numpy()
       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			torch.stack([x['val_loss'] for x in outputs]						).mean()
       SCREAMING_SNAKE_CASE_							=			np.concatenate([x['pred'] for x in outputs]				,				axis=0						)
       SCREAMING_SNAKE_CASE_							=			np.argmax(_lowerCAmelCase				,				axis=2						)
       SCREAMING_SNAKE_CASE_							=			np.concatenate([x['target'] for x in outputs]				,				axis=0						)

       SCREAMING_SNAKE_CASE_							=			dict(enumerate(self.labels						)						)
       SCREAMING_SNAKE_CASE_							=			[[] for _ in range(out_label_ids.shape[0]						)]
       SCREAMING_SNAKE_CASE_							=			[[] 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_							=			{
           'val_loss': val_loss_mean,
           'accuracy_score': accuracy_score(_lowerCAmelCase				,				_lowerCAmelCase						),
           'precision': precision_score(_lowerCAmelCase				,				_lowerCAmelCase						),
           'recall': recall_score(_lowerCAmelCase				,				_lowerCAmelCase						),
           'f1': fa_score(_lowerCAmelCase				,				_lowerCAmelCase						),
       }

       SCREAMING_SNAKE_CASE_							=			dict(results.items()						)
       SCREAMING_SNAKE_CASE_							=			results
       return ret, preds_list, out_label_list



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		int						):
       # when stable
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self._eval_end(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ret['log']
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		Any						):
       # updating to test_epoch_end instead of deprecated test_end
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self._eval_end(_lowerCAmelCase						)

       # Converting to the dict required by pl
       # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
       # pytorch_lightning/trainer/logging.py#L139
       SCREAMING_SNAKE_CASE_							=			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_  (							_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		int						):
       # Add NER specific options
       BaseTransformer.add_model_specific_args(_lowerCAmelCase				,				_lowerCAmelCase						)
       parser.add_argument(
           '--task_type'				,				default='NER'				,				type=_lowerCAmelCase				,				help='Task type to fine tune in training (e.g. NER, POS, etc)'						)
       parser.add_argument(
           '--max_seq_length'				,				default=128				,				type=_lowerCAmelCase				,				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=_lowerCAmelCase				,				help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'				,				)
       parser.add_argument(
           '--gpus'				,				default=0				,				type=_lowerCAmelCase				,				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__":
   lowerCamelCase__ :   int					=							argparse.ArgumentParser()
   add_generic_args(parser, os.getcwd())
   lowerCamelCase__ :   Dict					=							NERTransformer.add_model_specific_args(parser, os.getcwd())
   lowerCamelCase__ :   List[Any]					=							parser.parse_args()
   lowerCamelCase__ :   str					=							NERTransformer(args)
   lowerCamelCase__ :   List[Any]					=							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
      lowerCamelCase__ :   Any					=							sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
      lowerCamelCase__ :   List[str]					=							model.load_from_checkpoint(checkpoints[-1])
      trainer.test(model)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)

lowerCamelCase__ :   List[str]					=							{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    "speech_to_text"
 lowercase_				    =    ["past_key_values"]
 lowercase_				    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		List[Any]=10_000				,				_lowerCAmelCase  :		List[Any]=12				,				_lowerCAmelCase  :		Union[str, Any]=2_048				,				_lowerCAmelCase  :		Optional[int]=4				,				_lowerCAmelCase  :		Union[str, Any]=6				,				_lowerCAmelCase  :		Optional[int]=2_048				,				_lowerCAmelCase  :		Optional[Any]=4				,				_lowerCAmelCase  :		Any=0.0				,				_lowerCAmelCase  :		Optional[Any]=0.0				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		List[Any]=True				,				_lowerCAmelCase  :		int="relu"				,				_lowerCAmelCase  :		Union[str, Any]=256				,				_lowerCAmelCase  :		List[str]=0.1				,				_lowerCAmelCase  :		str=0.0				,				_lowerCAmelCase  :		Tuple=0.0				,				_lowerCAmelCase  :		List[str]=0.02				,				_lowerCAmelCase  :		List[str]=2				,				_lowerCAmelCase  :		Tuple=True				,				_lowerCAmelCase  :		List[str]=1				,				_lowerCAmelCase  :		str=0				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Union[str, Any]=6_000				,				_lowerCAmelCase  :		List[str]=1_024				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Optional[Any]=(5, 5)				,				_lowerCAmelCase  :		str=1_024				,				_lowerCAmelCase  :		str=80				,				_lowerCAmelCase  :		Tuple=1				,				**_lowerCAmelCase  :		Any				,				):
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			d_model
       SCREAMING_SNAKE_CASE_							=			encoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			encoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			decoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			decoder_layers
       SCREAMING_SNAKE_CASE_							=			decoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			dropout
       SCREAMING_SNAKE_CASE_							=			attention_dropout
       SCREAMING_SNAKE_CASE_							=			activation_dropout
       SCREAMING_SNAKE_CASE_							=			activation_function
       SCREAMING_SNAKE_CASE_							=			init_std
       SCREAMING_SNAKE_CASE_							=			encoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			decoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			use_cache
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE_							=			max_source_positions
       SCREAMING_SNAKE_CASE_							=			max_target_positions
       SCREAMING_SNAKE_CASE_							=			num_conv_layers
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			conv_channels
       SCREAMING_SNAKE_CASE_							=			input_feat_per_channel
       SCREAMING_SNAKE_CASE_							=			input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, "
                 F"`config.num_conv_layers = {self.num_conv_layers}`."						)

       super().__init__(
           pad_token_id=_lowerCAmelCase				,				bos_token_id=_lowerCAmelCase				,				eos_token_id=_lowerCAmelCase				,				is_encoder_decoder=_lowerCAmelCase				,				decoder_start_token_id=_lowerCAmelCase				,				**_lowerCAmelCase				,				)