



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


_lowercase						  =		logging.getLogger(__name__)



class     _UpperCAmelCase			(       __lowercase     ):
     UpperCamelCase__		    =				'''summarization'''
     UpperCamelCase__		    =				['''loss''']
     UpperCamelCase__		    =				ROUGE_KEYS
     UpperCamelCase__		    =				'''rouge2'''




     def __init__(				self ,						a__ ,						**a__):
         if hparams.sortish_sampler and hparams.gpus > 1:
             A__		 =					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__(_A ,						num_labels=_A ,						mode=self.mode ,						**_A)
         use_task_specific_params(self.model ,						'''summarization''')
         save_git_info(self.hparams.output_dir)
         A__		 =					Path(self.output_dir) / '''metrics.json'''
         A__		 =					Path(self.output_dir) / '''hparams.pkl'''
         pickle_save(self.hparams ,						self.hparams_save_path)
         A__		 =					0
         A__		 =					defaultdict(_A)
         A__		 =					self.config.model_type
         A__		 =					self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

         A__		 =					{
             '''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())

         A__		 =					get_git_info()['''repo_sha''']
         A__		 =					hparams.num_workers
         A__		 =					None  # default to config
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,						_A):
             A__		 =					self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             A__		 =					self.decoder_start_token_id
         A__		 =					(
             SeqaSeqDataset if hasattr(self.tokenizer ,						'''prepare_seq2seq_batch''') else LegacySeqaSeqDataset
         )
         A__		 =					False
         A__		 =					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:
             A__		 =					self.hparams.eval_max_gen_length
         else:
             A__		 =					self.model.config.max_length
         A__		 =					self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




     def 		snake_case_		(				self ,						a__):
         A__		 =					{
             k: self.tokenizer.batch_decode(v.tolist()) if '''mask''' not in k else v.shape for k, v in batch.items()
         }
         save_json(_A ,						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''')

         A__		 =					True
         return readable_batch




     def 		snake_case_		(				self ,						a__ ,						**a__):
         return self.model(_A ,						**_A)




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.tokenizer.batch_decode(
             _A ,						skip_special_tokens=_A ,						clean_up_tokenization_spaces=_A)
         return lmap(str.strip ,						_A)




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

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

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




     @property
     def 		snake_case_		(				self):
         return self.tokenizer.pad_token_id




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					self._step(_A)

         A__		 =					dict(zip(self.loss_names ,						_A))
         # tokens per batch
         A__		 =					batch['''input_ids'''].ne(self.pad).sum() + batch['''labels'''].ne(self.pad).sum()
         A__		 =					batch['''input_ids'''].shape[0]
         A__		 =					batch['''input_ids'''].eq(self.pad).sum()
         A__		 =					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 		snake_case_		(				self ,						a__ ,						a__):
         return self._generative_step(_A)




     def 		snake_case_		(				self ,						a__ ,						a__="val"):
         self.step_count += 1
         A__		 =					{k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
         A__		 =					losses['''loss''']
         A__		 =					{
             k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
         }
         A__		 =					(
             generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
         )
         A__		 =					torch.tensor(_A).type_as(_A)
         generative_metrics.update({k: v.item() for k, v in losses.items()})
         losses.update(_A)
         A__		 =					{F"{prefix}_avg_{k}": x for k, x in losses.items()}
         A__		 =					self.step_count
         self.metrics[prefix].append(_A)  # callback writes this to self.metrics_save_path
         A__		 =					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 		snake_case_		(				self ,						a__ ,						a__):
         return calculate_rouge(_A ,						_A)




     def 		snake_case_		(				self ,						a__):
         A__		 =					time.time()

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




     def 		snake_case_		(				self ,						a__ ,						a__):
         return self._generative_step(_A)




     def 		snake_case_		(				self ,						a__):
         return self.validation_epoch_end(_A ,						prefix='''test''')




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.n_obs[type_path]
         A__		 =					self.target_lens[type_path]
         A__		 =					self.dataset_class(
             self.tokenizer ,						type_path=_A ,						n_obs=_A ,						max_target_length=_A ,						**self.dataset_kwargs ,						)
         return dataset




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = False):
         A__		 =					self.get_dataset(_A)

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

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




     def 		snake_case_		(				self):
         A__		 =					self.get_dataloader('''train''' ,						batch_size=self.hparams.train_batch_size ,						shuffle=_A)
         return dataloader




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




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




     @staticmethod
     def 		snake_case_		(				a__ ,						a__):
         BaseTransformer.add_model_specific_args(_A ,						_A)
         add_generic_args(_A ,						_A)
         parser.add_argument(
             '''--max_source_length''' ,						default=1_0_2_4 ,						type=_A ,						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=5_6 ,						type=_A ,						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=1_4_2 ,						type=_A ,						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=1_4_2 ,						type=_A ,						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=_A)
         parser.add_argument('''--overwrite_output_dir''' ,						action='''store_true''' ,						default=_A)
         parser.add_argument('''--max_tokens_per_batch''' ,						type=_A ,						default=_A)
         parser.add_argument('''--logger_name''' ,						type=_A ,						choices=['''default''', '''wandb''', '''wandb_shared'''] ,						default='''default''')
         parser.add_argument('''--n_train''' ,						type=_A ,						default=-1 ,						required=_A ,						help='''# examples. -1 means use all.''')
         parser.add_argument('''--n_val''' ,						type=_A ,						default=5_0_0 ,						required=_A ,						help='''# examples. -1 means use all.''')
         parser.add_argument('''--n_test''' ,						type=_A ,						default=-1 ,						required=_A ,						help='''# examples. -1 means use all.''')
         parser.add_argument(
             '''--task''' ,						type=_A ,						default='''summarization''' ,						required=_A ,						help='''# examples. -1 means use all.''')
         parser.add_argument('''--label_smoothing''' ,						type=_A ,						default=0.0 ,						required=_A)
         parser.add_argument('''--src_lang''' ,						type=_A ,						default='''''' ,						required=_A)
         parser.add_argument('''--tgt_lang''' ,						type=_A ,						default='''''' ,						required=_A)
         parser.add_argument('''--eval_beams''' ,						type=_A ,						default=_A ,						required=_A)
         parser.add_argument(
             '''--val_metric''' ,						type=_A ,						default=_A ,						required=_A ,						choices=['''bleu''', '''rouge2''', '''loss''', None])
         parser.add_argument('''--eval_max_gen_length''' ,						type=_A ,						default=_A ,						help='''never generate more than n tokens''')
         parser.add_argument('''--save_top_k''' ,						type=_A ,						default=1 ,						required=_A ,						help='''How many checkpoints to save''')
         parser.add_argument(
             '''--early_stopping_patience''' ,						type=_A ,						default=-1 ,						required=_A ,						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     _UpperCAmelCase			(       __lowercase     ):
     UpperCamelCase__		    =				'''translation'''
     UpperCamelCase__		    =				['''loss''']
     UpperCamelCase__		    =				['''bleu''']
     UpperCamelCase__		    =				'''bleu'''




     def __init__(				self ,						a__ ,						**a__):
         super().__init__(_A ,						**_A)
         A__		 =					hparams.src_lang
         A__		 =					hparams.tgt_lang




     def 		snake_case_		(				self ,						a__ ,						a__):
         return calculate_bleu(_A ,						_A)







def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       Optional[Any]=None	)->  Any:
    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:
            A__		 =					SummarizationModule(__snake_case	)
        else:
            A__		 =					TranslationModule(__snake_case	)
    A__		 =					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'''	)
    ):
        A__		 =					True  # don't pollute wandb logs unnecessarily
    elif args.logger_name == "wandb":
        from pytorch_lightning.loggers import WandbLogger

        A__		 =					os.environ.get('''WANDB_PROJECT''' ,       __snake_case	)
        A__		 =					WandbLogger(name=model.output_dir.name ,       project=__snake_case	)

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

        A__		 =					WandbLogger(name=model.output_dir.name ,       project=f"hf_{dataset}"	)

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

    A__		 =					args.val_metric == '''loss'''
    A__		 =					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

    A__		 =					''''''
    A__		 =					sorted(glob.glob(os.path.join(args.output_dir ,       '''*.ckpt'''	) ,       recursive=__snake_case	)	)
    if checkpoints:
        A__		 =					checkpoints[-1]
        A__		 =					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__":
   _lowercase						  =		argparse.ArgumentParser()
   _lowercase						  =		pl.Trainer.add_argparse_args(parser)
   _lowercase						  =		SummarizationModule.add_model_specific_args(parser, os.getcwd())

   _lowercase						  =		parser.parse_args()

   main(args)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
   )
   parser.add_argument(
       "--original_config_file",
       type=str,
       required=True,
       help="The YAML config file corresponding to the original architecture.",
   )
   parser.add_argument(
       "--num_in_channels",
       default=None,
       type=int,
       help="The number of input channels. If `None` number of input channels will be automatically inferred.",
   )
   parser.add_argument(
       "--image_size",
       default=512,
       type=int,
       help=(
           "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
           " Base. Use 768 for Stable Diffusion v2."
       ),
   )
   parser.add_argument(
       "--extract_ema",
       action="store_true",
       help=(
           "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
           " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
           " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
       ),
   )
   parser.add_argument(
       "--upcast_attention",
       action="store_true",
       help=(
           "Whether the attention computation should always be upcasted. This is necessary when running stable"
           " diffusion 2.1."
       ),
   )
   parser.add_argument(
       "--from_safetensors",
       action="store_true",
       help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
   )
   parser.add_argument(
       "--to_safetensors",
       action="store_true",
       help="Whether to store pipeline in safetensors format or not.",
   )
   parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
   parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")



   def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       if string == "True":
           return True
       elif string == "False":
           return False
       else:
           raise ValueError(f"could not parse string as bool {string}"	)

   parser.add_argument(
       "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
   )

   parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		download_controlnet_from_original_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       extract_ema=args.extract_ema,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       use_linear_projection=args.use_linear_projection,
       cross_attention_dim=args.cross_attention_dim,
   )



   controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
