
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_						: Tuple         =  logging.getLogger(__name__)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Any					      = """token-classification"""

			def __init__(       self :       List[Any]						,	snake_case_ :       int      ):
							if type(snake_case_      ) == dict:
											UpperCamelCase_:							List[str]	=						Namespace(**snake_case_      )
							UpperCamelCase_:							Any	=						import_module("""tasks"""      )
							try:
											UpperCamelCase_:							List[Any]	=						getattr(snake_case_						,	hparams.task_type      )
											UpperCamelCase_:							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__()}'''      )
							UpperCamelCase_:							Tuple	=						self.token_classification_task.get_labels(hparams.labels      )
							UpperCamelCase_:							Tuple	=						CrossEntropyLoss().ignore_index
							super().__init__(snake_case_						,	len(self.labels      )						,	self.mode      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	**snake_case_ :       str      ):
							return self.model(**snake_case_      )

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

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

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

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

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

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

							UpperCamelCase_:							List[Any]	=						dict(enumerate(self.labels      )      )
							UpperCamelCase_:							Union[str, Any]	=						[[] for _ in range(out_label_ids.shape[0]      )]
							UpperCamelCase_:							int	=						[[] 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_:							Dict	=						{
							    """val_loss""": val_loss_mean,
							    """accuracy_score""": accuracy_score(snake_case_						,	snake_case_      ),
							    """precision""": precision_score(snake_case_						,	snake_case_      ),
							    """recall""": recall_score(snake_case_						,	snake_case_      ),
							    """f1""": fa_score(snake_case_						,	snake_case_      ),
							}

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

			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       Optional[Any]      ):
							# when stable
							UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Dict	=						self._eval_end(snake_case_      )
							UpperCamelCase_:							Tuple	=						ret["""log"""]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Union[str, Any]      ):
							# updating to test_epoch_end instead of deprecated test_end
							UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Union[str, Any]	=						self._eval_end(snake_case_      )

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








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	snake_case_      )

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	snake_case_      )

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	return_tensors="""np"""      )

							for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							self.assertListEqual(
							    processor.model_input_names[2:]						,	feature_extractor.model_input_names						,	msg="""`processor` and `feature_extractor` model input names do not match"""						,	)






