




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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 						__lowerCAmelCase      (      _a					):
							UpperCamelCase__   	=    42







class 						__lowerCAmelCase      (      _a ,			_a					):
							@register_to_config
							def __init__(		self		:Tuple						, __magic_name__		:int = 3						, __magic_name__		:int = 3						, __magic_name__		:Tuple[str] = ("DownEncoderBlock2D",)						, __magic_name__		:Tuple[str] = ("UpDecoderBlock2D",)						, __magic_name__		:Tuple[int] = (64,)						, __magic_name__		:int = 1						, __magic_name__		:str = "silu"						, __magic_name__		:int = 3						, __magic_name__		:int = 32						, __magic_name__		:int = 256						, __magic_name__		:int = 32						, __magic_name__		:Optional[int] = None						, __magic_name__		:float = 0.18215						, __magic_name__		:str = "group"						, ):







								'''simple docstring'''







								super().__init__()

								# pass init params to Encoder
								a									=				Encoder(
								    in_channels=__magic_name__						, out_channels=__magic_name__						, down_block_types=__magic_name__						, block_out_channels=__magic_name__						, layers_per_block=__magic_name__						, act_fn=__magic_name__						, norm_num_groups=__magic_name__						, double_z=__magic_name__						, )

								a									=				vq_embed_dim if vq_embed_dim is not None else latent_channels

								a									=				nn.Convad(__magic_name__						, __magic_name__						, 1							)
								a									=				VectorQuantizer(__magic_name__						, __magic_name__						, beta=0.25						, remap=__magic_name__						, sane_index_shape=__magic_name__							)
								a									=				nn.Convad(__magic_name__						, __magic_name__						, 1							)

								# pass init params to Decoder
								a									=				Decoder(
								    in_channels=__magic_name__						, out_channels=__magic_name__						, up_block_types=__magic_name__						, block_out_channels=__magic_name__						, layers_per_block=__magic_name__						, act_fn=__magic_name__						, norm_num_groups=__magic_name__						, norm_type=__magic_name__						, )
							@apply_forward_hook
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = True							):







								'''simple docstring'''







								a									=				self.encoder(__magic_name__							)
								a									=				self.quant_conv(__magic_name__							)

								if not return_dict:
									return (h,)

								return VQEncoderOutput(latents=__magic_name__							)
							@apply_forward_hook
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = False						, __magic_name__		:bool = True							):







								'''simple docstring'''







								if not force_not_quantize:
									a							,					a							,					a									=				self.quantize(__magic_name__							)
								else:
									a									=				h
								a									=				self.post_quant_conv(__magic_name__							)
								a									=				self.decoder(__magic_name__						, quant if self.config.norm_type == """spatial""" else None							)

								if not return_dict:
									return (dec,)

								return DecoderOutput(sample=__magic_name__							)





							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = True							):







								'''simple docstring'''







								a									=				sample
								a									=				self.encode(__magic_name__							).latents
								a									=				self.decode(__magic_name__							).sample

								if not return_dict:
									return (dec,)

								return DecoderOutput(sample=__magic_name__							)









import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_SCREAMING_SNAKE_CASE   =  logging.getLogger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		int					='''summarization'''
       __lowerCAmelCase    :		str					=['''loss''']
       __lowerCAmelCase    :		Dict					=ROUGE_KEYS
       __lowerCAmelCase    :		str					='''rouge2'''





       def __init__(				self	:Dict,      snake_case	:List[Any],      **snake_case	:Tuple):



          """simple docstring"""

          if hparams.sortish_sampler and hparams.gpus > 1:
             _lowercase              =False
          elif hparams.max_tokens_per_batch is not None:
             if hparams.gpus > 1:
                raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training')
             if hparams.sortish_sampler:
                raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously')

          super().__init__(snake_case,      num_labels=snake_case,      mode=self.mode,      **snake_case)
          use_task_specific_params(self.model,      'summarization')
          save_git_info(self.hparams.output_dir)
          _lowercase              =Path(self.output_dir) / 'metrics.json'
          _lowercase              =Path(self.output_dir) / 'hparams.pkl'
          pickle_save(self.hparams,      self.hparams_save_path)
          _lowercase              =0
          _lowercase              =defaultdict(snake_case)
          _lowercase              =self.config.model_type
          _lowercase              =self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

          _lowercase              ={
              "data_dir": self.hparams.data_dir,
              "max_source_length": self.hparams.max_source_length,
              "prefix": self.model.config.prefix or "",
          }
          _lowercase              ={
              'train': self.hparams.n_train,
              'val': self.hparams.n_val,
              'test': self.hparams.n_test,
          }
          _lowercase              ={k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

          _lowercase              ={
              'train': self.hparams.max_target_length,
              'val': self.hparams.val_max_target_length,
              'test': self.hparams.test_max_target_length,
          }
          assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
          assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
          if self.hparams.freeze_embeds:
             freeze_embeds(self.model)
          if self.hparams.freeze_encoder:
             freeze_params(self.model.get_encoder())
             assert_all_frozen(self.model.get_encoder())

          _lowercase              =get_git_info()['repo_sha']
          _lowercase              =hparams.num_workers
          _lowercase              =None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer,      snake_case):
             _lowercase              =self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             _lowercase              =self.decoder_start_token_id
          _lowercase              =(
              SeqaSeqDataset if hasattr(self.tokenizer,      'prepare_seq2seq_batch') else LegacySeqaSeqDataset
          )
          _lowercase              =False
          _lowercase              =self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
          if self.hparams.eval_max_gen_length is not None:
             _lowercase              =self.hparams.eval_max_gen_length
          else:
             _lowercase              =self.model.config.max_length
          _lowercase              =self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Dict[str, torch.Tensor]):



          """simple docstring"""

          _lowercase              ={
              k: self.tokenizer.batch_decode(v.tolist()) if 'mask' not in k else v.shape for k, v in batch.items()
          }
          save_json(snake_case,      Path(self.output_dir) / 'text_batch.json')
          save_json({k: v.tolist() for k, v in batch.items()},      Path(self.output_dir) / 'tok_batch.json')

          _lowercase              =True
          return readable_batch





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[str],      **snake_case	:List[Any]):



          """simple docstring"""

          return self.model(snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:List[int]):



          """simple docstring"""

          _lowercase              =self.tokenizer.batch_decode(
              snake_case,      skip_special_tokens=snake_case,      clean_up_tokenization_spaces=snake_case)
          return lmap(str.strip,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:dict):



          """simple docstring"""

          _lowercase              =self.tokenizer.pad_token_id
          _lowercase       ,       _lowercase              =batch['input_ids'], batch['attention_mask']
          _lowercase              =batch['labels']
          if isinstance(self.model,      snake_case):
             _lowercase              =self.model._shift_right(snake_case)
          else:
             _lowercase              =shift_tokens_right(snake_case,      snake_case)
          if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
             _lowercase              =decoder_input_ids
             self.save_readable_batch(snake_case)

          _lowercase              =self(snake_case,      attention_mask=snake_case,      decoder_input_ids=snake_case,      use_cache=snake_case)
          _lowercase              =outputs['logits']
          if self.hparams.label_smoothing == 0:
             # Same behavior as modeling_bart.py, besides ignoring pad_token_id
             _lowercase              =nn.CrossEntropyLoss(ignore_index=snake_case)

             assert lm_logits.shape[-1] == self.vocab_size
             _lowercase              =ce_loss_fct(lm_logits.view(-1,      lm_logits.shape[-1]),      tgt_ids.view(-1))
          else:
             _lowercase              =nn.functional.log_softmax(snake_case,      dim=-1)
             _lowercase       ,       _lowercase              =label_smoothed_nll_loss(
                 snake_case,      snake_case,      self.hparams.label_smoothing,      ignore_index=snake_case)
          return (loss,)





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.tokenizer.pad_token_id





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Dict,      snake_case	:str):



          """simple docstring"""

          _lowercase              =self._step(snake_case)

          _lowercase              =dict(zip(self.loss_names,      snake_case))
          # tokens per batch
          _lowercase              =batch['input_ids'].ne(self.pad).sum() + batch['labels'].ne(self.pad).sum()
          _lowercase              =batch['input_ids'].shape[0]
          _lowercase              =batch['input_ids'].eq(self.pad).sum()
          _lowercase              =batch['input_ids'].eq(self.pad).float().mean()
          # TODO(SS): make a wandb summary metric for this
          return {"loss": loss_tensors[0], "log": logs}





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Dict,      snake_case	:List[Any]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:List[str],      snake_case	:str="val"):



          """simple docstring"""

          self.step_count += 1
          _lowercase              ={k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
          _lowercase              =losses['loss']
          _lowercase              ={
              k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['gen_time', 'gen_len']
          }
          _lowercase              =(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          _lowercase              =torch.tensor(snake_case).type_as(snake_case)
          generative_metrics.update({k: v.item() for k, v in losses.items()})
          losses.update(snake_case)
          _lowercase              ={f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
          _lowercase              =self.step_count
          self.metrics[prefix].append(snake_case)  # callback writes this to self.metrics_save_path
          _lowercase              =flatten_list([x['preds'] for x in outputs])
          return {
              "log": all_metrics,
              "preds": preds,
              f'''{prefix}_loss''': loss,
              f'''{prefix}_{self.val_metric}''': metric_tensor,
          }





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:str,      snake_case	:List[str]):



          """simple docstring"""

          return calculate_rouge(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:str,      snake_case	:dict):



          """simple docstring"""

          _lowercase              =time.time()

          # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
          _lowercase              =self.model.generate(
              batch['input_ids'],      attention_mask=batch['attention_mask'],      use_cache=snake_case,      decoder_start_token_id=self.decoder_start_token_id,      num_beams=self.eval_beams,      max_length=self.eval_max_length,      )
          _lowercase              =(time.time() - ta) / batch['input_ids'].shape[0]
          _lowercase              =self.ids_to_clean_text(snake_case)
          _lowercase              =self.ids_to_clean_text(batch['labels'])
          _lowercase              =self._step(snake_case)
          _lowercase              =dict(zip(self.loss_names,      snake_case))
          _lowercase              =self.calc_generative_metrics(snake_case,      snake_case)
          _lowercase              =np.mean(lmap(snake_case,      snake_case))
          base_metrics.update(gen_time=snake_case,      gen_len=snake_case,      preds=snake_case,      target=snake_case,      **snake_case)
          return base_metrics





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:str,      snake_case	:Optional[int]):



          """simple docstring"""

          return self._generative_step(snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:List[str]):



          """simple docstring"""

          return self.validation_epoch_end(snake_case,      prefix='test')





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:str):



          """simple docstring"""

          _lowercase              =self.n_obs[type_path]
          _lowercase              =self.target_lens[type_path]
          _lowercase              =self.dataset_class(
              self.tokenizer,      type_path=snake_case,      n_obs=snake_case,      max_target_length=snake_case,      **self.dataset_kwargs,      )
          return dataset





       def 				UpperCamelCase__						(				self	:str,      snake_case	:str,      snake_case	:int,      snake_case	:bool = False):



          """simple docstring"""

          _lowercase              =self.get_dataset(snake_case)

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
             _lowercase              =dataset.make_sortish_sampler(snake_case,      distributed=self.hparams.gpus > 1)
             return DataLoader(
                 snake_case,      batch_size=snake_case,      collate_fn=dataset.collate_fn,      shuffle=snake_case,      num_workers=self.num_workers,      sampler=snake_case,      )

          elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
             _lowercase              =dataset.make_dynamic_sampler(
                 self.hparams.max_tokens_per_batch,      distributed=self.hparams.gpus > 1)
             return DataLoader(
                 snake_case,      batch_sampler=snake_case,      collate_fn=dataset.collate_fn,      num_workers=self.num_workers,      )
          else:
             return DataLoader(
                 snake_case,      batch_size=snake_case,      collate_fn=dataset.collate_fn,      shuffle=snake_case,      num_workers=self.num_workers,      sampler=snake_case,      )





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.get_dataloader('train',      batch_size=self.hparams.train_batch_size,      shuffle=snake_case)
          return dataloader





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.get_dataloader('val',      batch_size=self.hparams.eval_batch_size)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          return self.get_dataloader('test',      batch_size=self.hparams.eval_batch_size)





       @staticmethod
       def 				UpperCamelCase__						(				snake_case	:Dict,      snake_case	:List[str]):



          """simple docstring"""

          BaseTransformer.add_model_specific_args(snake_case,      snake_case)
          add_generic_args(snake_case,      snake_case)
          parser.add_argument(
              '--max_source_length',      default=1024,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--max_target_length',      default=56,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--val_max_target_length',      default=142,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument(
              '--test_max_target_length',      default=142,      type=snake_case,      help=(
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              ),      )
          parser.add_argument('--freeze_encoder',      action='store_true')
          parser.add_argument('--freeze_embeds',      action='store_true')
          parser.add_argument('--sortish_sampler',      action='store_true',      default=snake_case)
          parser.add_argument('--overwrite_output_dir',      action='store_true',      default=snake_case)
          parser.add_argument('--max_tokens_per_batch',      type=snake_case,      default=snake_case)
          parser.add_argument('--logger_name',      type=snake_case,      choices=['default', 'wandb', 'wandb_shared'],      default='default')
          parser.add_argument('--n_train',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_val',      type=snake_case,      default=500,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--n_test',      type=snake_case,      default=-1,      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument(
              '--task',      type=snake_case,      default='summarization',      required=snake_case,      help='# examples. -1 means use all.')
          parser.add_argument('--label_smoothing',      type=snake_case,      default=0.0,      required=snake_case)
          parser.add_argument('--src_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--tgt_lang',      type=snake_case,      default='',      required=snake_case)
          parser.add_argument('--eval_beams',      type=snake_case,      default=snake_case,      required=snake_case)
          parser.add_argument(
              '--val_metric',      type=snake_case,      default=snake_case,      required=snake_case,      choices=['bleu', 'rouge2', 'loss', None])
          parser.add_argument('--eval_max_gen_length',      type=snake_case,      default=snake_case,      help='never generate more than n tokens')
          parser.add_argument('--save_top_k',      type=snake_case,      default=1,      required=snake_case,      help='How many checkpoints to save')
          parser.add_argument(
              '--early_stopping_patience',      type=snake_case,      default=-1,      required=snake_case,      help=(
                  '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
                  ' val_check_interval will effect it.'
              ),      )
          return parser






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[str, Any]					='''translation'''
       __lowerCAmelCase    :		Any					=['''loss''']
       __lowerCAmelCase    :		Optional[int]					=['''bleu''']
       __lowerCAmelCase    :		Any					='''bleu'''





       def __init__(				self	:str,      snake_case	:Union[str, Any],      **snake_case	:Any):



          """simple docstring"""

          super().__init__(snake_case,      **snake_case)
          _lowercase              =hparams.src_lang
          _lowercase              =hparams.tgt_lang





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Union[str, Any],      snake_case	:Any):



          """simple docstring"""

          return calculate_bleu(snake_case,      snake_case)





def        _snake_case   (_snake_case				:       Dict      ,    _snake_case				:       int=None)					->	SummarizationModule:
   Path(args.output_dir).mkdir(exist_ok=_snake_case)
   check_output_dir(_snake_case      ,    expected_items=3)

   if model is None:
      if "summarization" in args.task:
         _lowercase              =SummarizationModule(_snake_case)
      else:
         _lowercase              =TranslationModule(_snake_case)
   _lowercase              =Path(args.data_dir).name
   if (
       args.logger_name == "default"
       or args.fast_dev_run
       or str(args.output_dir).startswith('/tmp')
       or str(args.output_dir).startswith('/var')
   ):
      _lowercase              =True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      _lowercase              =os.environ.get('WANDB_PROJECT'      ,    _snake_case)
      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=_snake_case)

   elif args.logger_name == "wandb_shared":
      from pytorch_lightning.loggers import WandbLogger

      _lowercase              =WandbLogger(name=model.output_dir.name      ,    project=f'''hf_{dataset}''')

   if args.early_stopping_patience >= 0:
      _lowercase              =get_early_stopping_callback(model.val_metric      ,    args.early_stopping_patience)
   else:
      _lowercase              =False

   _lowercase              =args.val_metric == 'loss'
   _lowercase              =generic_train(
       _snake_case      ,    _snake_case      ,    logging_callback=SeqaSeqLoggingCallback()      ,    checkpoint_callback=get_checkpoint_callback(
           args.output_dir      ,    model.val_metric      ,    args.save_top_k      ,    _snake_case)      ,    early_stopping_callback=_snake_case      ,    logger=_snake_case      ,    )
   pickle_save(model.hparams      ,    model.output_dir / 'hparams.pkl')
   if not args.do_predict:
      return model

   _lowercase              =''
   _lowercase              =sorted(glob.glob(os.path.join(args.output_dir      ,    '*.ckpt')      ,    recursive=_snake_case))
   if checkpoints:
      _lowercase              =checkpoints[-1]
      _lowercase              =checkpoints[-1]
   trainer.logger.log_hyperparams(model.hparams)

   # test() without a model tests using the best checkpoint automatically
   trainer.test()
   return model


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  argparse.ArgumentParser()
    _SCREAMING_SNAKE_CASE   =  pl.Trainer.add_argparse_args(parser)
    _SCREAMING_SNAKE_CASE   =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

    _SCREAMING_SNAKE_CASE   =  parser.parse_args()

    main(args)


