





'''simple docstring'''




import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowercase__       : Optional[Any]         =       logging.getLogger(__name__)


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''sequence-classification'''


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if type(_UpperCAmelCase) == dict:
               __A		:					List[str]						=			Namespace(**_UpperCAmelCase)
          __A		:					str						=			glue_output_modes[hparams.task]
          __A		:					str						=			glue_tasks_num_labels[hparams.task]

          super().__init__(_UpperCAmelCase		,      _UpperCAmelCase		,      self.mode)


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






          return self.model(**_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

          if self.config.model_type not in ["distilbert", "bart"]:
               __A		:					Any						=			batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

          __A		:					str						=			self(**_UpperCAmelCase)
          __A		:					Optional[int]						=			outputs[0]

          __A		:					Optional[int]						=			self.trainer.lr_schedulers[0]['scheduler']
          __A		:					Tuple						=			{'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
          return {"loss": loss, "log": tensorboard_logs}


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.hparams
          __A		:					str						=			processors[args.task]()
          __A		:					Tuple						=			processor.get_labels()

          for mode in ["train", "dev"]:
               __A		:					Optional[int]						=			self._feature_file(_UpperCAmelCase)
               if os.path.exists(_UpperCAmelCase) and not args.overwrite_cache:
                    logger.info('Loading features from cached file %s'		,      _UpperCAmelCase)
               else:
                    logger.info('Creating features from dataset file at %s'		,      args.data_dir)
                    __A		:					Dict						=			(
                        processor.get_dev_examples(args.data_dir)
                        if mode == 'dev'
                        else processor.get_train_examples(args.data_dir)
                    )
                    __A		:					List[str]						=			convert_examples_to_features(
                        _UpperCAmelCase		,      self.tokenizer		,      max_length=args.max_seq_length		,      label_list=self.labels		,      output_mode=args.glue_output_mode		,      )
                    logger.info('Saving features into cached file %s'		,      _UpperCAmelCase)
                    torch.save(_UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = False):

          '''simple docstring'''






          __A		:					Dict						=			'dev' if mode == 'test' else mode

          __A		:					Any						=			self._feature_file(_UpperCAmelCase)
          logger.info('Loading features from cached file %s'		,      _UpperCAmelCase)
          __A		:					Optional[int]						=			torch.load(_UpperCAmelCase)
          __A		:					Any						=			torch.tensor([f.input_ids for f in features]		,      dtype=torch.long)
          __A		:					List[Any]						=			torch.tensor([f.attention_mask for f in features]		,      dtype=torch.long)
          __A		:					Any						=			torch.tensor([f.token_type_ids for f in features]		,      dtype=torch.long)
          if self.hparams.glue_output_mode == "classification":
               __A		:					int						=			torch.tensor([f.label for f in features]		,      dtype=torch.long)
          elif self.hparams.glue_output_mode == "regression":
               __A		:					List[str]						=			torch.tensor([f.label for f in features]		,      dtype=torch.float)

          return DataLoader(
              TensorDataset(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)		,      batch_size=_UpperCAmelCase		,      shuffle=_UpperCAmelCase		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[str]						=			{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

          if self.config.model_type not in ["distilbert", "bart"]:
               __A		:					Any						=			batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

          __A		:					str						=			self(**_UpperCAmelCase)
          __A					,__A		:					Optional[int]						=			outputs[:2]
          __A		:					Dict						=			logits.detach().cpu().numpy()
          __A		:					Union[str, Any]						=			inputs['labels'].detach().cpu().numpy()

          return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[str]						=			torch.stack([x['val_loss'] for x in outputs]).mean().detach().cpu().item()
          __A		:					Optional[Any]						=			np.concatenate([x['pred'] for x in outputs]		,      axis=0)

          if self.hparams.glue_output_mode == "classification":
               __A		:					str						=			np.argmax(_UpperCAmelCase		,      axis=1)
          elif self.hparams.glue_output_mode == "regression":
               __A		:					Any						=			np.squeeze(_UpperCAmelCase)

          __A		:					Union[str, Any]						=			np.concatenate([x['target'] for x in outputs]		,      axis=0)
          __A		:					List[Any]						=			[[] for _ in range(out_label_ids.shape[0])]
          __A		:					List[str]						=			[[] for _ in range(out_label_ids.shape[0])]

          __A		:					int						=			{**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,      _UpperCAmelCase		,      _UpperCAmelCase)}

          __A		:					Optional[Any]						=			dict(results.items())
          __A		:					Tuple						=			results
          return ret, preds_list, out_label_list


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A					,__A					,__A		:					str						=			self._eval_end(_UpperCAmelCase)
          __A		:					int						=			ret['log']
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A					,__A					,__A		:					List[str]						=			self._eval_end(_UpperCAmelCase)
          __A		:					Union[str, 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 				SCREAMING_SNAKE_CASE    (		_UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          BaseTransformer.add_model_specific_args(_UpperCAmelCase		,      _UpperCAmelCase)
          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(
              '--task'		,      default=''		,      type=_UpperCAmelCase		,      required=_UpperCAmelCase		,      help='The GLUE task to run'		,      )
          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






def 			_lowerCAmelCase     (    )	->	List[Any]:
     __A		:					str						=			argparse.ArgumentParser()
     add_generic_args(__snake_case					,				os.getcwd()     )
     __A		:					Union[str, Any]						=			GLUETransformer.add_model_specific_args(__snake_case					,				os.getcwd()     )
     __A		:					Any						=			parser.parse_args()

     # If output_dir not provided, a folder will be generated in pwd
     if args.output_dir is None:
          __A		:					Optional[int]						=			os.path.join(
              './results'					,				f'{args.task}_{time.strftime("%Y%m%d_%H%M%S"     )}'					,				)
          os.makedirs(args.output_dir     )

     __A		:					int						=			GLUETransformer(__snake_case     )
     __A		:					int						=			generic_train(__snake_case					,				__snake_case     )

     # Optionally, predict on dev set and write to output_dir
     if args.do_predict:
          __A		:					Union[str, Any]						=			sorted(glob.glob(os.path.join(args.output_dir					,				'checkpoint-epoch=*.ckpt'     )					,				recursive=__snake_case     )     )
          __A		:					Tuple						=			model.load_from_checkpoint(checkpoints[-1]     )
          return trainer.test(__snake_case     )


if __name__ == "__main__":
  main()






'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block


@dataclass
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			42




class 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):


     @register_to_config
     def __init__(		self		,      _UpperCAmelCase = 6_5536		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 2		,      _UpperCAmelCase = 2		,      _UpperCAmelCase = 0		,      _UpperCAmelCase = "fourier"		,      _UpperCAmelCase = True		,      _UpperCAmelCase = False		,      _UpperCAmelCase = 0.0		,      _UpperCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,      _UpperCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,      _UpperCAmelCase = "UNetMidBlock1D"		,      _UpperCAmelCase = None		,      _UpperCAmelCase = (32, 32, 64)		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 8		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = False		,      ):

          '''simple docstring'''






          super().__init__()
          __A		:					Dict						=			sample_size

          # time
          if time_embedding_type == "fourier":
               __A		:					int						=			GaussianFourierProjection(
                   embedding_size=8		,      set_W_to_weight=_UpperCAmelCase		,      log=_UpperCAmelCase		,      flip_sin_to_cos=_UpperCAmelCase)
               __A		:					Any						=			2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               __A		:					List[str]						=			Timesteps(
                   block_out_channels[0]		,      flip_sin_to_cos=_UpperCAmelCase		,      downscale_freq_shift=_UpperCAmelCase)
               __A		:					List[str]						=			block_out_channels[0]

          if use_timestep_embedding:
               __A		:					Optional[Any]						=			block_out_channels[0] * 4
               __A		:					Optional[int]						=			TimestepEmbedding(
                   in_channels=_UpperCAmelCase		,      time_embed_dim=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      out_dim=block_out_channels[0]		,      )

          __A		:					Dict						=			nn.ModuleList([])
          __A		:					Dict						=			None
          __A		:					Tuple						=			nn.ModuleList([])
          __A		:					Tuple						=			None

          # down
          __A		:					Any						=			in_channels
          for i, down_block_type in enumerate(_UpperCAmelCase):
               __A		:					Tuple						=			output_channel
               __A		:					Optional[Any]						=			block_out_channels[i]

               if i == 0:
                    input_channel += extra_in_channels

               __A		:					List[str]						=			i == len(_UpperCAmelCase) - 1

               __A		:					int						=			get_down_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_downsample=not is_final_block or downsample_each_block		,      )
               self.down_blocks.append(_UpperCAmelCase)

          # mid
          __A		:					str						=			get_mid_block(
              _UpperCAmelCase		,      in_channels=block_out_channels[-1]		,      mid_channels=block_out_channels[-1]		,      out_channels=block_out_channels[-1]		,      embed_dim=block_out_channels[0]		,      num_layers=_UpperCAmelCase		,      add_downsample=_UpperCAmelCase		,      )

          # up
          __A		:					Optional[int]						=			list(reversed(_UpperCAmelCase))
          __A		:					Optional[int]						=			reversed_block_out_channels[0]
          if out_block_type is None:
               __A		:					str						=			out_channels
          else:
               __A		:					List[Any]						=			block_out_channels[0]

          for i, up_block_type in enumerate(_UpperCAmelCase):
               __A		:					Optional[Any]						=			output_channel
               __A		:					Optional[Any]						=			(
                   reversed_block_out_channels[i + 1] if i < len(_UpperCAmelCase) - 1 else final_upsample_channels
               )

               __A		:					Dict						=			i == len(_UpperCAmelCase) - 1

               __A		:					str						=			get_up_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_upsample=not is_final_block		,      )
               self.up_blocks.append(_UpperCAmelCase)
               __A		:					Optional[int]						=			output_channel

          # out
          __A		:					str						=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,      32)
          __A		:					Optional[Any]						=			get_out_block(
              out_block_type=_UpperCAmelCase		,      num_groups_out=_UpperCAmelCase		,      embed_dim=block_out_channels[0]		,      out_channels=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      fc_dim=block_out_channels[-1] // 4		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = True		,      ):

          '''simple docstring'''






          __A		:					Any						=			timestep
          if not torch.is_tensor(_UpperCAmelCase):
               __A		:					Any						=			torch.tensor([timesteps]		,      dtype=torch.long		,      device=sample.device)
          elif torch.is_tensor(_UpperCAmelCase) and len(timesteps.shape) == 0:
               __A		:					Any						=			timesteps[None].to(sample.device)

          __A		:					List[Any]						=			self.time_proj(_UpperCAmelCase)
          if self.config.use_timestep_embedding:
               __A		:					Dict						=			self.time_mlp(_UpperCAmelCase)
          else:
               __A		:					Dict						=			timestep_embed[..., None]
               __A		:					Tuple						=			timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
               __A		:					List[Any]						=			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

          # 2. down
          __A		:					int						=			()
          for downsample_block in self.down_blocks:
               __A					,__A		:					int						=			downsample_block(hidden_states=_UpperCAmelCase		,      temb=_UpperCAmelCase)
               down_block_res_samples += res_samples

          # 3. mid
          if self.mid_block:
               __A		:					Optional[int]						=			self.mid_block(_UpperCAmelCase		,      _UpperCAmelCase)

          # 4. up
          for i, upsample_block in enumerate(self.up_blocks):
               __A		:					Any						=			down_block_res_samples[-1:]
               __A		:					Optional[int]						=			down_block_res_samples[:-1]
               __A		:					Any						=			upsample_block(_UpperCAmelCase		,      res_hidden_states_tuple=_UpperCAmelCase		,      temb=_UpperCAmelCase)

          # 5. post-process
          if self.out_block:
               __A		:					Dict						=			self.out_block(_UpperCAmelCase		,      _UpperCAmelCase)

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=_UpperCAmelCase)