





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


UpperCamelCase__           =			logging.getLogger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""token-classification"""






							def __init__(    self	,			__lowerCAmelCase	):
														if type(__lowerCAmelCase	) == dict:
																					UpperCamelCase__					  =      Namespace(**__lowerCAmelCase	)
														UpperCamelCase__					  =      import_module("""tasks"""	)
														try:
																					UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			hparams.task_type	)
																					UpperCamelCase__					  =      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__()}"""	)
														UpperCamelCase__					  =      self.token_classification_task.get_labels(hparams.labels	)
														UpperCamelCase__					  =      CrossEntropyLoss().ignore_index
														super().__init__(__lowerCAmelCase	,			len(self.labels	)	,			self.mode	)






							def 					_lowerCamelCase		(    self	,			**__lowerCAmelCase	):
														return self.model(**__lowerCAmelCase	)






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

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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.hparams
														for mode in ["train", "dev", "test"]:
																					UpperCamelCase__					  =      self._feature_file(__lowerCAmelCase	)
																					if os.path.exists(__lowerCAmelCase	) and not args.overwrite_cache:
																												logger.info("""Loading features from cached file %s"""	,			__lowerCAmelCase	)
																												UpperCamelCase__					  =      torch.load(__lowerCAmelCase	)
																					else:
																												logger.info("""Creating features from dataset file at %s"""	,			args.data_dir	)
																												UpperCamelCase__					  =      self.token_classification_task.read_examples_from_file(args.data_dir	,			__lowerCAmelCase	)
																												UpperCamelCase__					  =      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	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = False	):
														UpperCamelCase__					  =      self._feature_file(__lowerCAmelCase	)
														logger.info("""Loading features from cached file %s"""	,			__lowerCAmelCase	)
														UpperCamelCase__					  =      torch.load(__lowerCAmelCase	)
														UpperCamelCase__					  =      torch.tensor([f.input_ids for f in features]	,			dtype=torch.long	)
														UpperCamelCase__					  =      torch.tensor([f.attention_mask for f in features]	,			dtype=torch.long	)
														if features[0].token_type_ids is not None:
																					UpperCamelCase__					  =      torch.tensor([f.token_type_ids for f in features]	,			dtype=torch.long	)
														else:
																					UpperCamelCase__					  =      torch.tensor([0 for f in features]	,			dtype=torch.long	)
																					# HACK(we will not use this anymore soon)
														UpperCamelCase__					  =      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	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														"""Compute validation""" ""
														UpperCamelCase__					  =      {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
														if self.config.model_type != "distilbert":
																					UpperCamelCase__					  =      (
																					    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
																					)  # XLM and RoBERTa don"t use token_type_ids
														UpperCamelCase__					  =      self(**__lowerCAmelCase	)
														UpperCamelCase__  ,  UpperCamelCase__					  =      outputs[:2]
														UpperCamelCase__					  =      logits.detach().cpu().numpy()
														UpperCamelCase__					  =      inputs["""labels"""].detach().cpu().numpy()
														return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      torch.stack([x["""val_loss"""] for x in outputs]	).mean()
														UpperCamelCase__					  =      np.concatenate([x["""pred"""] for x in outputs]	,			axis=0	)
														UpperCamelCase__					  =      np.argmax(__lowerCAmelCase	,			axis=2	)
														UpperCamelCase__					  =      np.concatenate([x["""target"""] for x in outputs]	,			axis=0	)

														UpperCamelCase__					  =      dict(enumerate(self.labels	)	)
														UpperCamelCase__					  =      [[] for _ in range(out_label_ids.shape[0]	)]
														UpperCamelCase__					  =      [[] 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]]	)

														UpperCamelCase__					  =      {
														    """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	),
														}

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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														# when stable
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      self._eval_end(__lowerCAmelCase	)
														UpperCamelCase__					  =      ret["""log"""]
														return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														# updating to test_epoch_end instead of deprecated test_end
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      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
														UpperCamelCase__					  =      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	,			__lowerCAmelCase	):
														# 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__":
					UpperCamelCase__           =			argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					UpperCamelCase__           =			NERTransformer.add_model_specific_args(parser, os.getcwd())
					UpperCamelCase__           =			parser.parse_args()
					UpperCamelCase__           =			NERTransformer(args)
					UpperCamelCase__           =			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
										UpperCamelCase__           =			sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
										UpperCamelCase__           =			model.load_from_checkpoint(checkpoints[-1])
										trainer.test(model)











# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCamelCase__           =			subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
UpperCamelCase__           =			subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode("utf-8").split()

UpperCamelCase__           =			"|".join(sys.argv[1:])
UpperCamelCase__           =			re.compile(rf"""^({joined_dirs}).*?\.py$""")

UpperCamelCase__           =			[x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")




