import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Dict      =				(DPMSolverSinglestepScheduler,)
    a__     :						List[Any]      =				(("""num_inference_steps""", 2_5),)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Dict:
        __UpperCamelCase					:Optional[Any]        =		{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.00_01,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''prediction_type''': '''epsilon''',
            '''thresholding''': False,
            '''sample_max_value''': 1.0,
            '''algorithm_type''': '''dpmsolver++''',
            '''solver_type''': '''midpoint''',
            '''lambda_min_clipped''': -float('''inf'''),
            '''variance_type''': None,
        }

        config.update(**__lowercase)
        return config







    def       UpperCamelCase__       (     self			,			__lowercase=0			,			**__lowercase)	->     Tuple:
        __UpperCamelCase					:str        =		dict(self.forward_default_kwargs)
        __UpperCamelCase					:Dict        =		kwargs.pop('''num_inference_steps'''			,			__lowercase)
        __UpperCamelCase					:str        =		self.dummy_sample
        __UpperCamelCase					:Dict        =		0.1 * sample
        __UpperCamelCase					:Optional[int]        =		[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            __UpperCamelCase					:List[Any]        =		self.get_scheduler_config(**__lowercase)
            __UpperCamelCase					:Optional[int]        =		scheduler_class(**__lowercase)
            scheduler.set_timesteps(__lowercase)
            # copy over dummy past residuals
            __UpperCamelCase					:str        =		dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(__lowercase)
                __UpperCamelCase					:str        =		scheduler_class.from_pretrained(__lowercase)
                new_scheduler.set_timesteps(__lowercase)
                # copy over dummy past residuals
                __UpperCamelCase					:str        =		dummy_past_residuals[: new_scheduler.config.solver_order]

            __UpperCamelCase					:Tuple        =		sample, sample
            for t in range(__lowercase			,			time_step + scheduler.config.solver_order + 1):
                __UpperCamelCase					:Any        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample
                __UpperCamelCase					:Any        =		new_scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample

                assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"







    def       UpperCamelCase__       (     self)	->     Any:
        pass







    def       UpperCamelCase__       (     self			,			__lowercase=0			,			**__lowercase)	->     int:
        __UpperCamelCase					:Optional[Any]        =		dict(self.forward_default_kwargs)
        __UpperCamelCase					:Dict        =		kwargs.pop('''num_inference_steps'''			,			__lowercase)
        __UpperCamelCase					:List[Any]        =		self.dummy_sample
        __UpperCamelCase					:str        =		0.1 * sample
        __UpperCamelCase					:Tuple        =		[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            __UpperCamelCase					:Optional[Any]        =		self.get_scheduler_config()
            __UpperCamelCase					:List[str]        =		scheduler_class(**__lowercase)
            scheduler.set_timesteps(__lowercase)

            # copy over dummy past residuals (must be after setting timesteps)
            __UpperCamelCase					:Optional[int]        =		dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(__lowercase)
                __UpperCamelCase					:str        =		scheduler_class.from_pretrained(__lowercase)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(__lowercase)

                # copy over dummy past residual (must be after setting timesteps)
                __UpperCamelCase					:str        =		dummy_past_residuals[: new_scheduler.config.solver_order]

            __UpperCamelCase					:int        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample
            __UpperCamelCase					:Dict        =		new_scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample

            assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"







    def       UpperCamelCase__       (     self			,			__lowercase=None			,			**__lowercase)	->     List[str]:
        if scheduler is None:
            __UpperCamelCase					:int        =		self.scheduler_classes[0]
            __UpperCamelCase					:Dict        =		self.get_scheduler_config(**__lowercase)
            __UpperCamelCase					:Dict        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:Optional[int]        =		self.scheduler_classes[0]
        __UpperCamelCase					:str        =		self.get_scheduler_config(**__lowercase)
        __UpperCamelCase					:Optional[int]        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:Tuple        =		10
        __UpperCamelCase					:str        =		self.dummy_model()
        __UpperCamelCase					:List[Any]        =		self.dummy_sample_deter
        scheduler.set_timesteps(__lowercase)

        for i, t in enumerate(scheduler.timesteps):
            __UpperCamelCase					:Tuple        =		model(__lowercase			,			__lowercase)
            __UpperCamelCase					:str        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase).prev_sample

        return sample







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:List[str]        =		DPMSolverSinglestepScheduler(**self.get_scheduler_config())
        __UpperCamelCase					:Any        =		50
        __UpperCamelCase					:Optional[Any]        =		self.dummy_model()
        __UpperCamelCase					:Optional[int]        =		self.dummy_sample_deter
        scheduler.set_timesteps(__lowercase)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]):
            __UpperCamelCase					:List[Any]        =		model(__lowercase			,			__lowercase)
            __UpperCamelCase					:Optional[int]        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase).prev_sample

        __UpperCamelCase					:Tuple        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.25_74) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[Any]:
        for timesteps in [25, 50, 100, 999, 1_000]:
            self.check_over_configs(num_train_timesteps=__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        __UpperCamelCase					:Any        =		DPMSolverSinglestepScheduler(**self.get_scheduler_config())
        __UpperCamelCase					:List[str]        =		self.full_loop(scheduler=__lowercase)
        __UpperCamelCase					:Dict        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.27_91) < 1E-3

        __UpperCamelCase					:Dict        =		DEISMultistepScheduler.from_config(scheduler.config)
        __UpperCamelCase					:List[str]        =		DPMSolverMultistepScheduler.from_config(scheduler.config)
        __UpperCamelCase					:int        =		UniPCMultistepScheduler.from_config(scheduler.config)
        __UpperCamelCase					:Union[str, Any]        =		DPMSolverSinglestepScheduler.from_config(scheduler.config)

        __UpperCamelCase					:int        =		self.full_loop(scheduler=__lowercase)
        __UpperCamelCase					:List[Any]        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.27_91) < 1E-3







    def       UpperCamelCase__       (     self)	->     Tuple:
        self.check_over_configs(thresholding=__lowercase)
        for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
                for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            thresholding=__lowercase			,			prediction_type=__lowercase			,			sample_max_value=__lowercase			,			algorithm_type='''dpmsolver++'''			,			solver_order=__lowercase			,			solver_type=__lowercase			,			)







    def       UpperCamelCase__       (     self)	->     int:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowercase)







    def       UpperCamelCase__       (     self)	->     Tuple:
        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
                for order in [1, 2, 3]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=__lowercase			,			solver_type=__lowercase			,			prediction_type=__lowercase			,			algorithm_type=__lowercase			,			)
                        __UpperCamelCase					:List[str]        =		self.full_loop(
                            solver_order=__lowercase			,			solver_type=__lowercase			,			prediction_type=__lowercase			,			algorithm_type=__lowercase			,			)
                        assert not torch.isnan(__lowercase).any(), "Samples have nan numbers"







    def       UpperCamelCase__       (     self)	->     Dict:
        self.check_over_configs(lower_order_final=__lowercase)
        self.check_over_configs(lower_order_final=__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        self.check_over_configs(lambda_min_clipped=-float('''inf'''))
        self.check_over_configs(lambda_min_clipped=-5.1)







    def       UpperCamelCase__       (     self)	->     Dict:
        self.check_over_configs(variance_type=__lowercase)
        self.check_over_configs(variance_type='''learned_range''')







    def       UpperCamelCase__       (     self)	->     Tuple:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
            self.check_over_forward(num_inference_steps=__lowercase			,			time_step=0)







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:int        =		self.full_loop()
        __UpperCamelCase					:Dict        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.27_91) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[str]        =		self.full_loop(use_karras_sigmas=__lowercase)
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.22_48) < 1E-3







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:Optional[Any]        =		self.full_loop(prediction_type='''v_prediction''')
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.14_53) < 1E-3







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:Any        =		self.full_loop(prediction_type='''v_prediction'''			,			use_karras_sigmas=__lowercase)
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.06_49) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:int        =		self.scheduler_classes[0]
        __UpperCamelCase					:Optional[int]        =		self.get_scheduler_config(thresholding=__lowercase			,			dynamic_thresholding_ratio=0)
        __UpperCamelCase					:List[Any]        =		scheduler_class(**__lowercase)

        __UpperCamelCase					:Dict        =		10
        __UpperCamelCase					:Optional[Any]        =		self.dummy_model()
        __UpperCamelCase					:Dict        =		self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowercase)

        for i, t in enumerate(scheduler.timesteps):
            __UpperCamelCase					:str        =		model(__lowercase			,			__lowercase)
            __UpperCamelCase					:Union[str, Any]        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase).prev_sample

        assert sample.dtype == torch.floataa






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      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						str      =				"""token-classification"""







    def __init__(     self			,			__lowercase)	->     str:
        if type(__lowercase) == dict:
            __UpperCamelCase					:List[Any]        =		Namespace(**__lowercase)
        __UpperCamelCase					:Dict        =		import_module('''tasks''')
        try:
            __UpperCamelCase					:str        =		getattr(__lowercase			,			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__(__lowercase			,			len(self.labels)			,			self.mode)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     List[Any]:
        return self.model(**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Any:
        __UpperCamelCase					:str        =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            __UpperCamelCase					:Union[str, Any]        =		(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids

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







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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = False)	->     DataLoader:
        __UpperCamelCase					:Tuple        =		self._feature_file(__lowercase)
        logger.info('''Loading features from cached file %s'''			,			__lowercase)
        __UpperCamelCase					:str        =		torch.load(__lowercase)
        __UpperCamelCase					:int        =		torch.tensor([f.input_ids for f in features]			,			dtype=torch.long)
        __UpperCamelCase					:Optional[Any]        =		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					:int        =		torch.tensor([f.label_ids for f in features]			,			dtype=torch.long)
        return DataLoader(
            TensorDataset(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)			,			batch_size=__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Dict:
        """Compute validation""" ""
        __UpperCamelCase					:int        =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            __UpperCamelCase					:Any        =		(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids
        __UpperCamelCase					:Any        =		self(**__lowercase)
        __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		outputs[:2]
        __UpperCamelCase					:List[str]        =		logits.detach().cpu().numpy()
        __UpperCamelCase					:List[str]        =		inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Tuple        =		torch.stack([x['''val_loss'''] for x in outputs]).mean()
        __UpperCamelCase					:str        =		np.concatenate([x['''pred'''] for x in outputs]			,			axis=0)
        __UpperCamelCase					:Any        =		np.argmax(__lowercase			,			axis=2)
        __UpperCamelCase					:str        =		np.concatenate([x['''target'''] for x in outputs]			,			axis=0)

        __UpperCamelCase					:List[str]        =		dict(enumerate(self.labels))
        __UpperCamelCase					:Tuple        =		[[] for _ in range(out_label_ids.shape[0])]
        __UpperCamelCase					:Any        =		[[] 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					:Any        =		{
            '''val_loss''': val_loss_mean,
            '''accuracy_score''': accuracy_score(__lowercase			,			__lowercase),
            '''precision''': precision_score(__lowercase			,			__lowercase),
            '''recall''': recall_score(__lowercase			,			__lowercase),
            '''f1''': fa_score(__lowercase			,			__lowercase),
        }

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







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        # when stable
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self._eval_end(__lowercase)
        __UpperCamelCase					:Tuple        =		ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        # updating to test_epoch_end instead of deprecated test_end
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		self._eval_end(__lowercase)

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        __UpperCamelCase					:Optional[Any]        =		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			,			__lowercase)	->     Union[str, Any]:
        # Add NER specific options
        BaseTransformer.add_model_specific_args(__lowercase			,			__lowercase)
        parser.add_argument(
            '''--task_type'''			,			default='''NER'''			,			type=__lowercase			,			help='''Task type to fine tune in training (e.g. NER, POS, etc)''')
        parser.add_argument(
            '''--max_seq_length'''			,			default=128			,			type=__lowercase			,			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=__lowercase			,			help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''			,			)
        parser.add_argument(
            '''--gpus'''			,			default=0			,			type=__lowercase			,			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)





