


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        _a							(			UpperCamelCase__			):
							_lowercase	:			Union[str, Any]        = '''token-classification'''

							def __init__(							self:	int    ,						UpperCamelCase_:	Optional[Any]							)			->   Dict:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Tuple    ,						**UpperCamelCase_:	Optional[int]							)			->   str:





									"""simple docstring"""







									return self.model(**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







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

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

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.hparams
									for mode in ["train", "dev", "test"]:
											lowercase__            =      self._feature_file(UpperCamelCase_							)
											if os.path.exists(UpperCamelCase_							) and not args.overwrite_cache:
													logger.info('''Loading features from cached file %s'''    ,						UpperCamelCase_							)
													lowercase__            =      torch.load(UpperCamelCase_							)
											else:
													logger.info('''Creating features from dataset file at %s'''    ,						args.data_dir							)
													lowercase__            =      self.token_classification_task.read_examples_from_file(args.data_dir    ,						UpperCamelCase_							)
													lowercase__            =      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       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	bool = False							)			->   DataLoader:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[Any]							)			->   Union[str, Any]:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Dict    ,						UpperCamelCase_:	List[str]							)			->   int:





									"""simple docstring"""







									lowercase__            =      torch.stack([x['''val_loss'''] for x in outputs]							).mean()
									lowercase__            =      np.concatenate([x['''pred'''] for x in outputs]    ,						axis=0							)
									lowercase__            =      np.argmax(UpperCamelCase_    ,						axis=2							)
									lowercase__            =      np.concatenate([x['''target'''] for x in outputs]    ,						axis=0							)

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

									lowercase__            =      {
									    '''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_							),
									}

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

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	List[Any]							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__     ,		lowercase__            =      self._eval_end(UpperCamelCase_							)
									lowercase__            =      ret['''log''']
									return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	Dict							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__     ,		lowercase__            =      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
									lowercase__            =      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_ (							UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	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       =							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)






import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import DecisionTransformerModel
			from transformers.models.decision_transformer.modeling_decision_transformer import (
			    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


class        _a							:

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Any=13    ,						UpperCamelCase_:	Optional[Any]=7    ,						UpperCamelCase_:	Optional[Any]=6    ,						UpperCamelCase_:	Any=17    ,						UpperCamelCase_:	str=23    ,						UpperCamelCase_:	List[Any]=11    ,						UpperCamelCase_:	Optional[int]=True    ,						)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      seq_length
									lowercase__            =      act_dim
									lowercase__            =      state_dim
									lowercase__            =      hidden_size
									lowercase__            =      max_length
									lowercase__            =      is_training

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, 1)							)
									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, 1)							)
									lowercase__            =      ids_tensor((self.batch_size, self.seq_length)    ,						vocab_size=1_000							)
									lowercase__            =      random_attention_mask((self.batch_size, self.seq_length)							)

									lowercase__            =      self.get_config()

									return (
									    config,
									    states,
									    actions,
									    rewards,
									    returns_to_go,
									    timesteps,
									    attention_mask,
									)

							def       lowerCamelCase_ (							self:	Dict							)			->   List[Any]:





									"""simple docstring"""







									return DecisionTransformerConfig(
									    batch_size=self.batch_size    ,						seq_length=self.seq_length    ,						act_dim=self.act_dim    ,						state_dim=self.state_dim    ,						hidden_size=self.hidden_size    ,						max_length=self.max_length    ,						)

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int    ,						)			->   Dict:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModel(config=UpperCamelCase_							)
									model.to(UpperCamelCase_							)
									model.eval()
									lowercase__            =      model(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

									self.parent.assertEqual(result.state_preds.shape    ,						states.shape							)
									self.parent.assertEqual(result.action_preds.shape    ,						actions.shape							)
									self.parent.assertEqual(result.return_preds.shape    ,						returns_to_go.shape							)
									self.parent.assertEqual(
									    result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length * 3, self.hidden_size)							)  # seq length *3 as there are 3 modelities: states, returns and actions


							def       lowerCamelCase_ (							self:	List[Any]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.prepare_config_and_inputs()
									(
									    (
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		
									)            =      config_and_inputs
									lowercase__            =      {
									    '''states''': states,
									    '''actions''': actions,
									    '''rewards''': rewards,
									    '''returns_to_go''': returns_to_go,
									    '''timesteps''': timesteps,
									    '''attention_mask''': attention_mask,
									}
									return config, inputs_dict


@require_torch
class        _a							(			UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			str        = (DecisionTransformerModel,) if is_torch_available() else ()
							_lowercase	:			List[str]        = ()
							_lowercase	:			List[Any]        = {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

							# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
							_lowercase	:			Any        = False

							# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
							_lowercase	:			Tuple        = False
							_lowercase	:			str        = False
							_lowercase	:			Tuple        = False
							_lowercase	:			Optional[Any]        = False
							_lowercase	:			Tuple        = False
							_lowercase	:			Dict        = False
							_lowercase	:			Tuple        = False
							_lowercase	:			Optional[Any]        = False
							_lowercase	:			Optional[int]        = False

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModelTester(self							)
									lowercase__            =      ConfigTester(self    ,						config_class=UpperCamelCase_    ,						hidden_size=37							)

							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									self.config_tester.run_common_tests()

							def       lowerCamelCase_ (							self:	int							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase_							)

							@slow
							def       lowerCamelCase_ (							self:	Dict							)			->   Optional[Any]:





									"""simple docstring"""







									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowercase__            =      DecisionTransformerModel.from_pretrained(UpperCamelCase_							)
											self.assertIsNotNone(UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											lowercase__            =      inspect.signature(model.forward							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowercase__            =      [*signature.parameters.keys()]

											lowercase__            =      [
											    '''states''',
											    '''actions''',
											    '''rewards''',
											    '''returns_to_go''',
											    '''timesteps''',
											    '''attention_mask''',
											]

											self.assertListEqual(arg_names[: len(UpperCamelCase_							)]    ,						UpperCamelCase_							)


@require_torch
class        _a							(			unittest.TestCase			):

							@slow
							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







									lowercase__            =      2  # number of steps of autoregressive prediction we will perform
									lowercase__            =      10  # defined by the RL environment, may be normalized
									lowercase__            =      DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''							)
									lowercase__            =      model.to(UpperCamelCase_							)
									lowercase__            =      model.config
									torch.manual_seed(0							)
									lowercase__            =      torch.randn(1    ,						1    ,						config.state_dim							).to(device=UpperCamelCase_    ,						dtype=torch.floataa							)  # env.reset()

									lowercase__            =      torch.tensor(
									    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]    ,						device=UpperCamelCase_							)

									lowercase__            =      torch.tensor(UpperCamelCase_    ,						device=UpperCamelCase_    ,						dtype=torch.floataa							).reshape(1    ,						1    ,						1							)
									lowercase__            =      state
									lowercase__            =      torch.zeros(1    ,						0    ,						config.act_dim    ,						device=UpperCamelCase_    ,						dtype=torch.floataa							)
									lowercase__            =      torch.zeros(1    ,						0    ,						device=UpperCamelCase_    ,						dtype=torch.floataa							)
									lowercase__            =      torch.tensor(0    ,						device=UpperCamelCase_    ,						dtype=torch.long							).reshape(1    ,						1							)

									for step in range(UpperCamelCase_							):
											lowercase__            =      torch.cat([actions, torch.zeros(1    ,						1    ,						config.act_dim    ,						device=UpperCamelCase_							)]    ,						dim=1							)
											lowercase__            =      torch.cat([rewards, torch.zeros(1    ,						1    ,						device=UpperCamelCase_							)]    ,						dim=1							)

											lowercase__            =      torch.ones(1    ,						states.shape[1]							).to(dtype=torch.long    ,						device=states.device							)

											with torch.no_grad():
													lowercase__     ,		lowercase__     ,		lowercase__            =      model(
													    states=UpperCamelCase_    ,						actions=UpperCamelCase_    ,						rewards=UpperCamelCase_    ,						returns_to_go=UpperCamelCase_    ,						timesteps=UpperCamelCase_    ,						attention_mask=UpperCamelCase_    ,						return_dict=UpperCamelCase_    ,						)

											self.assertEqual(action_pred.shape    ,						actions.shape							)
											self.assertTrue(torch.allclose(action_pred[0, -1]    ,						expected_outputs[step]    ,						atol=1E-4							)							)
											lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      (  # env.step(action)
											    torch.randn(1    ,						1    ,						config.state_dim							).to(device=UpperCamelCase_    ,						dtype=torch.floataa							),
											    1.0,
											    False,
											    {},
											)

											lowercase__            =      action_pred[0, -1]
											lowercase__            =      torch.cat([states, state]    ,						dim=1							)
											lowercase__            =      returns_to_go[0, -1] - reward
											lowercase__            =      torch.cat([returns_to_go, pred_return.reshape(1    ,						1    ,						1							)]    ,						dim=1							)
											lowercase__            =      torch.cat(
											    [timesteps, torch.ones((1, 1)    ,						device=UpperCamelCase_    ,						dtype=torch.long							) * (step + 1)]    ,						dim=1							)


