



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


lowercase			= logging.getLogger(__name__)






class    SCREAMING_SNAKE_CASE			(							__snake_case					):
							__lowerCamelCase	:	Optional[int]    ='token-classification'





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		str	):

											'''simple docstring'''




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





							def 						UpperCamelCase_					(   self		:		List[str]      ,				**__lowercase		:		List[Any]	):

											'''simple docstring'''
											return self.model(**A_	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Dict	):

											'''simple docstring'''
											__a       =		{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
											if self.config.model_type != "distilbert":
															__a       =		(
															    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
															)  # XLM and RoBERTa don"t use token_type_ids

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





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		self.hparams
											for mode in ["train", "dev", "test"]:
															__a       =		self._feature_file(A_	)
															if os.path.exists(A_	) and not args.overwrite_cache:
																			logger.info("""Loading features from cached file %s"""      ,				A_	)
																			__a       =		torch.load(A_	)
															else:
																			logger.info("""Creating features from dataset file at %s"""      ,				args.data_dir	)
																			__a       =		self.token_classification_task.read_examples_from_file(args.data_dir      ,				A_	)
																			__a       =		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 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		int      ,				__lowercase		:		List[str] = False	):

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





							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		Dict      ,				__lowercase		:		Optional[int]	):

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





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		torch.stack([x["""val_loss"""] for x in outputs]	).mean()
											__a       =		np.concatenate([x["""pred"""] for x in outputs]      ,				axis=0	)
											__a       =		np.argmax(A_      ,				axis=2	)
											__a       =		np.concatenate([x["""target"""] for x in outputs]      ,				axis=0	)

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

											__a       =		{
											    "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_	),
											}

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





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		int	):

											'''simple docstring'''
											__a       =		self._eval_end(A_	)
											__a       =		ret["log"]
											return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		int	):

											'''simple docstring'''
											__a       =		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
											__a       =		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 						UpperCamelCase_					(   __lowercase		:		Dict      ,				__lowercase		:		Optional[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=128      ,				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__":
						lowercase			= argparse.ArgumentParser()
						add_generic_args(parser, os.getcwd())
						lowercase			= NERTransformer.add_model_specific_args(parser, os.getcwd())
						lowercase			= parser.parse_args()
						lowercase			= NERTransformer(args)
						lowercase			= 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
												lowercase			= sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
												lowercase			= model.load_from_checkpoint(checkpoints[-1])
												trainer.test(model)





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowerCamelCase__			= 16
lowerCamelCase__			= 32

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Accelerator				,     _SCREAMING_SNAKE_CASE       :			int = 16    ):
				"""simple docstring"""







				__a       =		AutoTokenizer.from_pretrained("""bert-base-cased"""    )
				__a       =		load_dataset("""glue"""				,     """mrpc"""    )

				def tokenize_function(_SCREAMING_SNAKE_CASE       :			int    ):
								# max_length=None => use the model max length (it's actually the default)
								__a       =		tokenizer(examples["""sentence1"""]				,     examples["""sentence2"""]				,     truncation=_SCREAMING_SNAKE_CASE				,     max_length=_SCREAMING_SNAKE_CASE    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__a       =		datasets.map(
								    _SCREAMING_SNAKE_CASE				,     batched=_SCREAMING_SNAKE_CASE				,     remove_columns=["""idx""", """sentence1""", """sentence2"""]				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__a       =		tokenized_datasets.rename_column("""label"""				,     """labels"""    )

				def collate_fn(_SCREAMING_SNAKE_CASE       :			Any    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__a       =		128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__a       =		16
								elif accelerator.mixed_precision != "no":
												__a       =		8
								else:
												__a       =		None

								return tokenizer.pad(
								    _SCREAMING_SNAKE_CASE				,     padding="""longest"""				,     max_length=_SCREAMING_SNAKE_CASE				,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""				,     )

				# Instantiate dataloaders.
				__a       =		DataLoader(
				    tokenized_datasets["""train"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE    )
				__a       =		DataLoader(
				    tokenized_datasets["""validation"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE    )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						lowerCamelCase__			= mocked_dataloaders  # noqa: F811

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
				"""simple docstring"""







				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,     _SCREAMING_SNAKE_CASE    ) == "1":
								__a       =		2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__a       =		Accelerator(
								    cpu=args.cpu				,     mixed_precision=args.mixed_precision				,     log_with="""all"""				,     project_dir=args.project_dir    )
				else:
								__a       =		Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__a       =		config["""lr"""]
				__a       =		int(config["""num_epochs"""]    )
				__a       =		int(config["""seed"""]    )
				__a       =		int(config["""batch_size"""]    )
				set_seed(_SCREAMING_SNAKE_CASE    )

				__a     ,					__a       =		get_dataloaders(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				__a       =		evaluate.load("""glue"""				,     """mrpc"""    )

				# If the batch size is too big we use gradient accumulation
				__a       =		1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__a       =		batch_size // MAX_GPU_BATCH_SIZE
								__a       =		MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__a       =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,     return_dict=_SCREAMING_SNAKE_CASE    )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__a       =		model.to(accelerator.device    )

				# Instantiate optimizer
				__a       =		AdamW(params=model.parameters()				,     lr=_SCREAMING_SNAKE_CASE    )

				# Instantiate scheduler
				__a       =		get_linear_schedule_with_warmup(
				    optimizer=_SCREAMING_SNAKE_CASE				,     num_warmup_steps=100				,     num_training_steps=(len(_SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__a     ,					__a     ,					__a     ,					__a     ,					__a       =		accelerator.prepare(
				    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								__a       =		os.path.split(_SCREAMING_SNAKE_CASE    )[-1].split("""."""    )[0]
								accelerator.init_trackers(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# Now we train the model
				for epoch in range(_SCREAMING_SNAKE_CASE    ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												__a       =		0
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												__a       =		model(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__a       =		loss / gradient_accumulation_steps
												accelerator.backward(_SCREAMING_SNAKE_CASE    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device    )
												with torch.no_grad():
																__a       =		model(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.logits.argmax(dim=-1    )
												__a     ,					__a       =		accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
												metric.add_batch(
												    predictions=_SCREAMING_SNAKE_CASE				,     references=_SCREAMING_SNAKE_CASE				,     )

								__a       =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"epoch {epoch}:"				,     _SCREAMING_SNAKE_CASE    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(_SCREAMING_SNAKE_CASE    ),
												        """epoch""": epoch,
												    }				,     step=_SCREAMING_SNAKE_CASE				,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		argparse.ArgumentParser(description="""Simple example of training script."""    )
				parser.add_argument(
				    """--mixed_precision"""				,     type=_SCREAMING_SNAKE_CASE				,     default=_SCREAMING_SNAKE_CASE				,     choices=["""no""", """fp16""", """bf16""", """fp8"""]				,     help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""				,     )
				parser.add_argument("""--cpu"""				,     action="""store_true"""				,     help="""If passed, will train on the CPU."""    )
				parser.add_argument(
				    """--with_tracking"""				,     action="""store_true"""				,     help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,     )
				parser.add_argument(
				    """--project_dir"""				,     type=_SCREAMING_SNAKE_CASE				,     default="""logs"""				,     help="""Location on where to store experiment tracking logs` and relevent project information"""				,     )
				__a       =		parser.parse_args()
				__a       =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						main()
