"""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


lowerCAmelCase_     =	logging.getLogger(__name__)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"token-classification"





				def __init__(      self							:							Dict      ,    _A							:							int):



											"""simple docstring"""







											if type(_A) == dict:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    Namespace(**_A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    import_module("""tasks""")
											try:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    getattr(_A      ,    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		:						Any			  =    self.token_classification_task.get_labels(hparams.labels)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    CrossEntropyLoss().ignore_index
											super().__init__(_A      ,    len(self.labels)      ,    self.mode)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    **_A							:							Dict):



											"""simple docstring"""







											return self.model(**_A)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Any      ,    _A							:							List[Any]):



											"""simple docstring"""







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

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





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							int      ,    _A							:							int      ,    _A							:							bool = False):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							Any      ,    _A							:							str):



											"""simple docstring"""







											"""Compute validation""" ""
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
											if self.config.model_type != "distilbert":
																		_SCREAMING_SNAKE_CASE		:						Optional[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		:						int			  =    self(**_A)
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs[:2]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    logits.detach().cpu().numpy()
											_SCREAMING_SNAKE_CASE		:						Any			  =    inputs["""labels"""].detach().cpu().numpy()
											return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    torch.stack([x["""val_loss"""] for x in outputs]).mean()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.concatenate([x["""pred"""] for x in outputs]      ,    axis=0)
											_SCREAMING_SNAKE_CASE		:						int			  =    np.argmax(_A      ,    axis=2)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    np.concatenate([x["""target"""] for x in outputs]      ,    axis=0)

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

											_SCREAMING_SNAKE_CASE		:						Dict			  =    {
											    """val_loss""": val_loss_mean,
											    """accuracy_score""": accuracy_score(_A      ,    _A),
											    """precision""": precision_score(_A      ,    _A),
											    """recall""": recall_score(_A      ,    _A),
											    """f1""": fa_score(_A      ,    _A),
											}

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    dict(results.items())
											_SCREAMING_SNAKE_CASE		:						int			  =    results
											return ret, preds_list, out_label_list





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self._eval_end(_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    ret["""log"""]
											return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    self._eval_end(_A)

											# 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 		_lowerCAmelCase		(      _A							:							Optional[int]      ,    _A							:							int):



											"""simple docstring"""







											BaseTransformer.add_model_specific_args(_A      ,    _A)
											parser.add_argument(
											    """--task_type"""      ,    default="""NER"""      ,    type=_A      ,    help="""Task type to fine tune in training (e.g. NER, POS, etc)""")
											parser.add_argument(
											    """--max_seq_length"""      ,    default=1_2_8      ,    type=_A      ,    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=_A      ,    help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""      ,    )
											parser.add_argument(
											    """--gpus"""      ,    default=0      ,    type=_A      ,    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_     =	argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			lowerCAmelCase_     =	NERTransformer.add_model_specific_args(parser, os.getcwd())
			lowerCAmelCase_     =	parser.parse_args()
			lowerCAmelCase_     =	NERTransformer(args)
			lowerCAmelCase_     =	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_     =	sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
						lowerCAmelCase_     =	model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)



"""simple docstring"""





import functools
import operator

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


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"sew"





				def __init__(      self							:							List[Any]      ,    _A							:							Tuple=3_2      ,    _A							:							str=7_6_8      ,    _A							:							Dict=1_2      ,    _A							:							Tuple=1_2      ,    _A							:							Optional[Any]=3_0_7_2      ,    _A							:							List[str]=2      ,    _A							:							Dict="gelu"      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.0      ,    _A							:							str=0.1      ,    _A							:							Tuple=0.1      ,    _A							:							Optional[int]=0.02      ,    _A							:							Dict=1e-5      ,    _A							:							str="group"      ,    _A							:							Tuple="gelu"      ,    _A							:							Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,    _A							:							Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,    _A							:							Any=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,    _A							:							Tuple=False      ,    _A							:							Tuple=1_2_8      ,    _A							:							int=1_6      ,    _A							:							Union[str, Any]=True      ,    _A							:							Optional[Any]=0.05      ,    _A							:							List[Any]=1_0      ,    _A							:							Union[str, Any]=2      ,    _A							:							Tuple=0.0      ,    _A							:							Union[str, Any]=1_0      ,    _A							:							Optional[int]=0      ,    _A							:							Union[str, Any]="mean"      ,    _A							:							Optional[int]=False      ,    _A							:							List[Any]=False      ,    _A							:							int=2_5_6      ,    _A							:							str=0      ,    _A							:							Optional[int]=1      ,    _A							:							List[Any]=2      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											super().__init__(**_A      ,    pad_token_id=_A      ,    bos_token_id=_A      ,    eos_token_id=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_norm
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_activation
											_SCREAMING_SNAKE_CASE		:						Dict			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    conv_bias
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_conv_pos_embeddings
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_conv_pos_embedding_groups
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    len(self.conv_dim)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						str			  =    squeeze_factor
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_dropout
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    attention_dropout
											_SCREAMING_SNAKE_CASE		:						int			  =    activation_dropout
											_SCREAMING_SNAKE_CASE		:						Any			  =    feat_proj_dropout
											_SCREAMING_SNAKE_CASE		:						str			  =    final_dropout
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    layerdrop
											_SCREAMING_SNAKE_CASE		:						Any			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						int			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    vocab_size

											if (
											    (len(self.conv_stride) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel) != self.num_feat_extract_layers)
											    or (len(self.conv_dim) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    """Configuration for convolutional layers is incorrect."""
																		    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
																		    f"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
																		    f"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    apply_spec_augment
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_time_prob
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    mask_time_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_time_min_masks
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_feature_prob
											_SCREAMING_SNAKE_CASE		:						int			  =    mask_feature_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_feature_min_masks

											# ctc loss
											_SCREAMING_SNAKE_CASE		:						int			  =    ctc_loss_reduction
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ctc_zero_infinity

											# sequence classification
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_weighted_layer_sum
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    classifier_proj_size





				@property
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											return functools.reduce(operator.mul      ,    self.conv_stride      ,    1)


