"""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   :				Dict     			=						logging.getLogger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							"""token-classification"""




							def __init__(  self				:   Any		,							_UpperCamelCase				:   List[Any] )   ->			List[str]:



												'''simple docstring'''

												if type(_UpperCamelCase ) == dict:
																	SCREAMING_SNAKE_CASE           =    Namespace(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    import_module("tasks" )
												try:
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							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__(_UpperCamelCase		,							len(self.labels )		,							self.mode )




							def 		__snake_case(  self				:   str		,							**_UpperCamelCase				:   List[str] )   ->			Any:



												'''simple docstring'''

												return self.model(**_UpperCamelCase )




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[str] )   ->			int:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.hparams
												for mode in ["train", "dev", "test"]:
																	SCREAMING_SNAKE_CASE           =    self._feature_file(_UpperCamelCase )
																	if os.path.exists(_UpperCamelCase ) and not args.overwrite_cache:
																						logger.info("Loading features from cached file %s"		,							_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    torch.load(_UpperCamelCase )
																	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		,							_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    self.token_classification_task.convert_examples_to_features(
																						    _UpperCamelCase		,							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=_UpperCamelCase		,							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"		,							_UpperCamelCase )
																						torch.save(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   bool = False )   ->			DataLoader:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._feature_file(_UpperCamelCase )
												logger.info("Loading features from cached file %s"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.load(_UpperCamelCase )
												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(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )		,							batch_size=_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[Any] )   ->			int:



												'''simple docstring'''

												"""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(**_UpperCamelCase )
												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 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   str )   ->			List[str]:



												'''simple docstring'''

												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(_UpperCamelCase		,							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(_UpperCamelCase		,							_UpperCamelCase ),
												    "precision": precision_score(_UpperCamelCase		,							_UpperCamelCase ),
												    "recall": recall_score(_UpperCamelCase		,							_UpperCamelCase ),
												    "f1": fa_score(_UpperCamelCase		,							_UpperCamelCase ),
												}

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




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[Any] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self._eval_end(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    ret["log"]
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[Any] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self._eval_end(_UpperCamelCase )

												# 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 		__snake_case(  _UpperCamelCase				:   Any		,							_UpperCamelCase				:   List[str] )   ->			Optional[Any]:



												'''simple docstring'''

												BaseTransformer.add_model_specific_args(_UpperCamelCase		,							_UpperCamelCase )
												parser.add_argument(
												    "--task_type"		,							default="NER"		,							type=_UpperCamelCase		,							help="Task type to fine tune in training (e.g. NER, POS, etc)" )
												parser.add_argument(
												    "--max_seq_length"		,							default=128		,							type=_UpperCamelCase		,							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=_UpperCamelCase		,							help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."		,							)
												parser.add_argument(
												    "--gpus"		,							default=0		,							type=_UpperCamelCase		,							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   :				Optional[int]     			=						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   :				Optional[int]     			=						NERTransformer(args)
							_lowerCamelCase   :				List[str]     			=						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 unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
							import jax.numpy as jnp

							from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    "The dog is cute and lives in the garden house"
												SCREAMING_SNAKE_CASE           =    jnp.array([tokenizer.encode(_UpperCamelCase )] )

												SCREAMING_SNAKE_CASE           =    (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
												SCREAMING_SNAKE_CASE           =    jnp.array(
												    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )["last_hidden_state"]
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# compare the actual values for a slice of last dim
												self.assertTrue(jnp.allclose(output[:, :, -1]		,							_UpperCamelCase		,							atol=1e-3 ) )




