from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__lowercase										= 8



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=BITS     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		x.device

    __UpperCamelCase					:Any        =		(x * 255).int().clamp(0  ,      255     )

    __UpperCamelCase					:Tuple        =		2 ** torch.arange(bits - 1  ,      -1  ,      -1  ,      device=_a     )
    __UpperCamelCase					:Any        =		rearrange(_a  ,      '''d -> d 1 1'''     )
    __UpperCamelCase					:Union[str, Any]        =		rearrange(_a  ,      '''b c h w -> b c 1 h w'''     )

    __UpperCamelCase					:List[Any]        =		((x & mask) != 0).float()
    __UpperCamelCase					:Optional[Any]        =		rearrange(_a  ,      '''b c d h w -> b (c d) h w'''     )
    __UpperCamelCase					:Tuple        =		bits * 2 - 1
    return bits



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=BITS     ):
    '''simple docstring'''







    __UpperCamelCase					:Any        =		x.device

    __UpperCamelCase					:Dict        =		(x > 0).int()
    __UpperCamelCase					:Dict        =		2 ** torch.arange(bits - 1  ,      -1  ,      -1  ,      device=_a  ,      dtype=torch.intaa     )

    __UpperCamelCase					:int        =		rearrange(_a  ,      '''d -> d 1 1'''     )
    __UpperCamelCase					:Any        =		rearrange(_a  ,      '''b (c d) h w -> b c d h w'''  ,      d=8     )
    __UpperCamelCase					:Optional[int]        =		reduce(x * mask  ,      '''b c d h w -> b c h w'''  ,      '''sum'''     )
    return (dec / 255).clamp(0.0  ,      1.0     )



def lowerCamelCase  (       self  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE = 0.0  ,      SCREAMING_SNAKE_CASE = True  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE = True  ,      ):
    '''simple docstring'''







    if self.num_inference_steps is None:
        raise ValueError(
            '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''     )

    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding

    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"

    # 1. get previous step value (=t-1)
    __UpperCamelCase					:str        =		timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    __UpperCamelCase					:str        =		self.alphas_cumprod[timestep]
    __UpperCamelCase					:Optional[int]        =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    __UpperCamelCase					:Union[str, Any]        =		1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    __UpperCamelCase					:List[str]        =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    __UpperCamelCase					:List[Any]        =		self.bit_scale
    if self.config.clip_sample:
        __UpperCamelCase					:Dict        =		torch.clamp(_a  ,      -scale  ,      _a     )

    # 5. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    __UpperCamelCase					:Any        =		self._get_variance(_a  ,      _a     )
    __UpperCamelCase					:int        =		eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        __UpperCamelCase					:Optional[Any]        =		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    __UpperCamelCase					:List[Any]        =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    __UpperCamelCase					:int        =		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        __UpperCamelCase					:Optional[Any]        =		model_output.device if torch.is_tensor(_a     ) else '''cpu'''
        __UpperCamelCase					:List[Any]        =		torch.randn(model_output.shape  ,      dtype=model_output.dtype  ,      generator=_a     ).to(_a     )
        __UpperCamelCase					:int        =		self._get_variance(_a  ,      _a     ) ** 0.5 * eta * noise

        __UpperCamelCase					:Dict        =		prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=_a  ,      pred_original_sample=_a     )



def lowerCamelCase  (       self  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE="epsilon"  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE = True  ,      ):
    '''simple docstring'''







    __UpperCamelCase					:Any        =		timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        __UpperCamelCase  ,	__UpperCamelCase					:Dict        =		torch.split(_a  ,      sample.shape[1]  ,      dim=1     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		None

    # 1. compute alphas, betas
    __UpperCamelCase					:List[Any]        =		self.alphas_cumprod[t]
    __UpperCamelCase					:Optional[Any]        =		self.alphas_cumprod[t - 1] if t > 0 else self.one
    __UpperCamelCase					:Any        =		1 - alpha_prod_t
    __UpperCamelCase					:Any        =		1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if prediction_type == "epsilon":
        __UpperCamelCase					:Dict        =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        __UpperCamelCase					:Union[str, Any]        =		model_output
    else:
        raise ValueError(f"""Unsupported prediction_type {prediction_type}."""     )

    # 3. Clip "predicted x_0"
    __UpperCamelCase					:List[Any]        =		self.bit_scale
    if self.config.clip_sample:
        __UpperCamelCase					:Tuple        =		torch.clamp(_a  ,      -scale  ,      _a     )

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    __UpperCamelCase					:Any        =		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    __UpperCamelCase					:Dict        =		self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    __UpperCamelCase					:int        =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    __UpperCamelCase					:List[Any]        =		0
    if t > 0:
        __UpperCamelCase					:List[Any]        =		torch.randn(
            model_output.size()  ,      dtype=model_output.dtype  ,      layout=model_output.layout  ,      generator=_a     ).to(model_output.device     )
        __UpperCamelCase					:Union[str, Any]        =		(self._get_variance(_a  ,      predicted_variance=_a     ) ** 0.5) * noise

    __UpperCamelCase					:Union[str, Any]        =		pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=_a  ,      pred_original_sample=_a     )


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase = 1.0			,			)	->     Dict:
        super().__init__()
        __UpperCamelCase					:List[str]        =		bit_scale
        __UpperCamelCase					:Dict        =		(
            ddim_bit_scheduler_step if isinstance(lowerCamelCase_			,			lowerCamelCase_) else ddpm_bit_scheduler_step
        )

        self.register_modules(unet=lowerCamelCase_			,			scheduler=lowerCamelCase_)







    @torch.no_grad()
    def __call__(     self			,			__lowercase = 256			,			__lowercase = 256			,			__lowercase = 50			,			__lowercase = None			,			__lowercase = 1			,			__lowercase = "pil"			,			__lowercase = True			,			**__lowercase			,			)	->     Union[Tuple, ImagePipelineOutput]:
        __UpperCamelCase					:Tuple        =		torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)			,			generator=lowerCamelCase_			,			)
        __UpperCamelCase					:str        =		decimal_to_bits(lowerCamelCase_) * self.bit_scale
        __UpperCamelCase					:List[Any]        =		latents.to(self.device)

        self.scheduler.set_timesteps(lowerCamelCase_)

        for t in self.progress_bar(self.scheduler.timesteps):
            # predict the noise residual
            __UpperCamelCase					:Optional[Any]        =		self.unet(lowerCamelCase_			,			lowerCamelCase_).sample

            # compute the previous noisy sample x_t -> x_t-1
            __UpperCamelCase					:Any        =		self.scheduler.step(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_).prev_sample

        __UpperCamelCase					:Optional[Any]        =		bits_to_decimal(lowerCamelCase_)

        if output_type == "pil":
            __UpperCamelCase					:List[Any]        =		self.numpy_to_pil(lowerCamelCase_)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=lowerCamelCase_)






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)





