
"""simple docstring"""







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


lowerCamelCase__           =	logging.getLogger(__name__)




class 				A__ (			_lowerCamelCase):
		A_		: str            =			'summarization'
		A_		: Any            =			['loss']
		A_		: Any            =			ROUGE_KEYS
		A_		: List[Any]            =			'rouge2'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			if hparams.sortish_sampler and hparams.gpus > 1:
				__lowerCAmelCase    :		Union[str, Any]		    =   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__(_SCREAMING_SNAKE_CASE ,	num_labels=_SCREAMING_SNAKE_CASE ,	mode=self.mode ,	**_SCREAMING_SNAKE_CASE				)
			use_task_specific_params(self.model ,	'summarization'				)
			save_git_info(self.hparams.output_dir				)
			__lowerCAmelCase    :		str		    =   Path(self.output_dir				) / 'metrics.json'
			__lowerCAmelCase    :		Tuple		    =   Path(self.output_dir				) / 'hparams.pkl'
			pickle_save(self.hparams ,	self.hparams_save_path				)
			__lowerCAmelCase    :		Union[str, Any]		    =   0
			__lowerCAmelCase    :		Optional[Any]		    =   defaultdict(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   self.config.model_type
			__lowerCAmelCase    :		Union[str, Any]		    =   self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

			__lowerCAmelCase    :		Optional[Any]		    =   {
			    '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()				)

			__lowerCAmelCase    :		str		    =   get_git_info()['repo_sha']
			__lowerCAmelCase    :		int		    =   hparams.num_workers
			__lowerCAmelCase    :		Any		    =   None  # default to config
			if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		List[Any]		    =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
				__lowerCAmelCase    :		Optional[Any]		    =   self.decoder_start_token_id
			__lowerCAmelCase    :		Optional[Any]		    =   (
			    SeqaSeqDataset if hasattr(self.tokenizer ,	'prepare_seq2seq_batch'				) else LegacySeqaSeqDataset
			)
			__lowerCAmelCase    :		Tuple		    =   False
			__lowerCAmelCase    :		int		    =   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:
				__lowerCAmelCase    :		Union[str, Any]		    =   self.hparams.eval_max_gen_length
			else:
				__lowerCAmelCase    :		List[Any]		    =   self.model.config.max_length
			__lowerCAmelCase    :		int		    =   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   {
			    k: self.tokenizer.batch_decode(v.tolist()				) if 'mask' not in k else v.shape for k, v in batch.items()
			}
			save_json(_SCREAMING_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'				)

			__lowerCAmelCase    :		List[str]		    =   True
			return readable_batch

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.model(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.tokenizer.batch_decode(
			    _SCREAMING_SNAKE_CASE ,	skip_special_tokens=_SCREAMING_SNAKE_CASE ,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE				)
			return lmap(str.strip ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Tuple		    =   self.tokenizer.pad_token_id
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   batch['input_ids'], batch['attention_mask']
			__lowerCAmelCase    :		Optional[Any]		    =   batch['labels']
			if isinstance(self.model ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		str		    =   self.model._shift_right(_SCREAMING_SNAKE_CASE				)
			else:
				__lowerCAmelCase    :		Any		    =   shift_tokens_right(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
				__lowerCAmelCase    :		Optional[int]		    =   decoder_input_ids
				self.save_readable_batch(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[Any]		    =   self(_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE ,	decoder_input_ids=_SCREAMING_SNAKE_CASE ,	use_cache=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   outputs['logits']
			if self.hparams.label_smoothing == 0:
				# Same behavior as modeling_bart.py, besides ignoring pad_token_id
				__lowerCAmelCase    :		List[Any]		    =   nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE				)

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

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[int]		    =   self._step(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   dict(zip(self.loss_names ,	_SCREAMING_SNAKE_CASE				)				)
			# tokens per batch
			__lowerCAmelCase    :		Dict		    =   batch['input_ids'].ne(self.pad				).sum() + batch['labels'].ne(self.pad				).sum()
			__lowerCAmelCase    :		str		    =   batch['input_ids'].shape[0]
			__lowerCAmelCase    :		Optional[int]		    =   batch['input_ids'].eq(self.pad				).sum()
			__lowerCAmelCase    :		Union[str, Any]		    =   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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return self._generative_step(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE="val"				):
			self.step_count += 1
			__lowerCAmelCase    :		List[Any]		    =   {k: torch.stack([x[k] for x in outputs]				).mean() for k in self.loss_names}
			__lowerCAmelCase    :		Optional[Any]		    =   losses['loss']
			__lowerCAmelCase    :		int		    =   {
			    k: np.array([x[k] for x in outputs]				).mean() for k in self.metric_names + ['gen_time', 'gen_len']
			}
			__lowerCAmelCase    :		Union[str, Any]		    =   (
			    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
			)
			__lowerCAmelCase    :		torch.FloatTensor		    =   torch.tensor(_SCREAMING_SNAKE_CASE				).type_as(_SCREAMING_SNAKE_CASE				)
			generative_metrics.update({k: v.item() for k, v in losses.items()}				)
			losses.update(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		int		    =   {f"{prefix}_avg_{k}": x for k, x in losses.items()}
			__lowerCAmelCase    :		Any		    =   self.step_count
			self.metrics[prefix].append(_SCREAMING_SNAKE_CASE				)  # callback writes this to self.metrics_save_path
			__lowerCAmelCase    :		Tuple		    =   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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return calculate_rouge(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   time.time()

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return self._generative_step(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.validation_epoch_end(_SCREAMING_SNAKE_CASE ,	prefix='test'				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		int		    =   self.n_obs[type_path]
			__lowerCAmelCase    :		int		    =   self.target_lens[type_path]
			__lowerCAmelCase    :		Union[str, Any]		    =   self.dataset_class(
			    self.tokenizer ,	type_path=_SCREAMING_SNAKE_CASE ,	n_obs=_SCREAMING_SNAKE_CASE ,	max_target_length=_SCREAMING_SNAKE_CASE ,	**self.dataset_kwargs ,	)
			return dataset

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = False				):
			__lowerCAmelCase    :		Any		    =   self.get_dataset(_SCREAMING_SNAKE_CASE				)

			if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
				__lowerCAmelCase    :		Union[str, Any]		    =   dataset.make_sortish_sampler(_SCREAMING_SNAKE_CASE ,	distributed=self.hparams.gpus > 1				)
				return DataLoader(
				    _SCREAMING_SNAKE_CASE ,	batch_size=_SCREAMING_SNAKE_CASE ,	collate_fn=dataset.collate_fn ,	shuffle=_SCREAMING_SNAKE_CASE ,	num_workers=self.num_workers ,	sampler=_SCREAMING_SNAKE_CASE ,	)

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.get_dataloader('train' ,	batch_size=self.hparams.train_batch_size ,	shuffle=_SCREAMING_SNAKE_CASE				)
			return dataloader

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

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







		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			BaseTransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			add_generic_args(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			parser.add_argument(
			    '--max_source_length' ,	default=10_24 ,	type=_SCREAMING_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=_SCREAMING_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=1_42 ,	type=_SCREAMING_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=1_42 ,	type=_SCREAMING_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=_SCREAMING_SNAKE_CASE				)
			parser.add_argument('--overwrite_output_dir' ,	action='store_true' ,	default=_SCREAMING_SNAKE_CASE				)
			parser.add_argument('--max_tokens_per_batch' ,	type=_SCREAMING_SNAKE_CASE ,	default=_SCREAMING_SNAKE_CASE				)
			parser.add_argument('--logger_name' ,	type=_SCREAMING_SNAKE_CASE ,	choices=['default', 'wandb', 'wandb_shared'] ,	default='default'				)
			parser.add_argument('--n_train' ,	type=_SCREAMING_SNAKE_CASE ,	default=-1 ,	required=_SCREAMING_SNAKE_CASE ,	help='# examples. -1 means use all.'				)
			parser.add_argument('--n_val' ,	type=_SCREAMING_SNAKE_CASE ,	default=5_00 ,	required=_SCREAMING_SNAKE_CASE ,	help='# examples. -1 means use all.'				)
			parser.add_argument('--n_test' ,	type=_SCREAMING_SNAKE_CASE ,	default=-1 ,	required=_SCREAMING_SNAKE_CASE ,	help='# examples. -1 means use all.'				)
			parser.add_argument(
			    '--task' ,	type=_SCREAMING_SNAKE_CASE ,	default='summarization' ,	required=_SCREAMING_SNAKE_CASE ,	help='# examples. -1 means use all.'				)
			parser.add_argument('--label_smoothing' ,	type=_SCREAMING_SNAKE_CASE ,	default=0.0 ,	required=_SCREAMING_SNAKE_CASE				)
			parser.add_argument('--src_lang' ,	type=_SCREAMING_SNAKE_CASE ,	default='' ,	required=_SCREAMING_SNAKE_CASE				)
			parser.add_argument('--tgt_lang' ,	type=_SCREAMING_SNAKE_CASE ,	default='' ,	required=_SCREAMING_SNAKE_CASE				)
			parser.add_argument('--eval_beams' ,	type=_SCREAMING_SNAKE_CASE ,	default=_SCREAMING_SNAKE_CASE ,	required=_SCREAMING_SNAKE_CASE				)
			parser.add_argument(
			    '--val_metric' ,	type=_SCREAMING_SNAKE_CASE ,	default=_SCREAMING_SNAKE_CASE ,	required=_SCREAMING_SNAKE_CASE ,	choices=['bleu', 'rouge2', 'loss', None]				)
			parser.add_argument('--eval_max_gen_length' ,	type=_SCREAMING_SNAKE_CASE ,	default=_SCREAMING_SNAKE_CASE ,	help='never generate more than n tokens'				)
			parser.add_argument('--save_top_k' ,	type=_SCREAMING_SNAKE_CASE ,	default=1 ,	required=_SCREAMING_SNAKE_CASE ,	help='How many checkpoints to save'				)
			parser.add_argument(
			    '--early_stopping_patience' ,	type=_SCREAMING_SNAKE_CASE ,	default=-1 ,	required=_SCREAMING_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 				A__ (			_lowerCamelCase):
		A_		: str            =			'translation'
		A_		: List[Any]            =			['loss']
		A_		: Optional[Any]            =			['bleu']
		A_		: Dict            =			'bleu'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			super().__init__(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   hparams.src_lang
			__lowerCAmelCase    :		Optional[Any]		    =   hparams.tgt_lang







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return calculate_bleu(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None	):
	Path(args.output_dir	).mkdir(exist_ok=_UpperCamelCase	)
	check_output_dir(_UpperCamelCase ,						expected_items=3	)

	if model is None:
		if "summarization" in args.task:
			__lowerCAmelCase    :		SummarizationModule		    =   SummarizationModule(_UpperCamelCase	)
		else:
			__lowerCAmelCase    :		SummarizationModule		    =   TranslationModule(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[int]		    =   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'	)
	):
		__lowerCAmelCase    :		List[str]		    =   True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		__lowerCAmelCase    :		Optional[int]		    =   os.environ.get('WANDB_PROJECT' ,						_UpperCamelCase	)
		__lowerCAmelCase    :		Optional[Any]		    =   WandbLogger(name=model.output_dir.name ,						project=_UpperCamelCase	)

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

		__lowerCAmelCase    :		Any		    =   WandbLogger(name=model.output_dir.name ,						project=F"hf_{dataset}"	)

	if args.early_stopping_patience >= 0:
		__lowerCAmelCase    :		List[str]		    =   get_early_stopping_callback(model.val_metric ,						args.early_stopping_patience	)
	else:
		__lowerCAmelCase    :		List[Any]		    =   False

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

	__lowerCAmelCase    :		Dict		    =   ''
	__lowerCAmelCase    :		Optional[int]		    =   sorted(glob.glob(os.path.join(args.output_dir ,						'*.ckpt'	) ,						recursive=_UpperCamelCase	)	)
	if checkpoints:
		__lowerCAmelCase    :		Optional[Any]		    =   checkpoints[-1]
		__lowerCAmelCase    :		Tuple		    =   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__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							lowerCamelCase__           =	pl.Trainer.add_argparse_args(parser)
							lowerCamelCase__           =	SummarizationModule.add_model_specific_args(parser, os.getcwd())

							lowerCamelCase__           =	parser.parse_args()

							main(args)

"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# initialize config
	if "resnet-50" in model_name:
		__lowerCAmelCase    :		int		    =   ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		__lowerCAmelCase    :		Union[str, Any]		    =   ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	__lowerCAmelCase    :		Union[str, Any]		    =   DetrConfig(use_timm_backbone=_UpperCamelCase ,						backbone_config=_UpperCamelCase	)

	# set label attributes
	__lowerCAmelCase    :		Optional[int]		    =   'panoptic' in model_name
	if is_panoptic:
		__lowerCAmelCase    :		Dict		    =   250
	else:
		__lowerCAmelCase    :		List[str]		    =   91
		__lowerCAmelCase    :		Tuple		    =   'huggingface/label-files'
		__lowerCAmelCase    :		str		    =   'coco-detection-id2label.json'
		__lowerCAmelCase    :		Optional[int]		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
		__lowerCAmelCase    :		List[Any]		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
		__lowerCAmelCase    :		List[Any]		    =   idalabel
		__lowerCAmelCase    :		Dict		    =   {v: k for k, v in idalabel.items()}

	return config, is_panoptic
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	__lowerCAmelCase    :		Tuple		    =   []

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')	)
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')	)
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')	)
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')	)
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')	)
	# stages
	for stage_idx in range(len(config.backbone_config.depths	)	):
		for layer_idx in range(config.backbone_config.depths[stage_idx]	):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
				    )	)
			# 3 convs
			for i in range(3	):
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
				    )	)
    # fmt: on

	for i in range(config.encoder_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
		        F"encoder.layers.{i}.self_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias")	)
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
		        F"decoder.layers.{i}.self_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")	)
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
		        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
		        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
		    )	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias")	)

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ('input_proj.weight', 'input_projection.weight'),
	        ('input_proj.bias', 'input_projection.bias'),
	        ('query_embed.weight', 'query_position_embeddings.weight'),
	        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
	        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
	        ('class_embed.weight', 'class_labels_classifier.weight'),
	        ('class_embed.bias', 'class_labels_classifier.bias'),
	        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
	        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
	        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
	        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
	        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
	        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
	    ]	)

	return rename_keys
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   state_dict.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   val
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		Tuple		    =   ''
	if is_panoptic:
		__lowerCAmelCase    :		Union[str, Any]		    =   'detr.'

	# first: transformer encoder
	for i in range(6	):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		__lowerCAmelCase    :		Optional[int]		    =   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		List[Any]		    =   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		int		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		Tuple		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Dict		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		int		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[-256:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6	):
		# read in weights + bias of input projection layer of self-attention
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		List[str]		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		List[str]		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Any		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		Any		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		Any		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		__lowerCAmelCase    :		Union[str, Any]		    =   state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"	)
		__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight_cross_attn[:256, :]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias_cross_attn[:256]
		__lowerCAmelCase    :		Optional[Any]		    =   in_proj_weight_cross_attn[256:512, :]
		__lowerCAmelCase    :		Any		    =   in_proj_bias_cross_attn[256:512]
		__lowerCAmelCase    :		str		    =   in_proj_weight_cross_attn[-256:, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias_cross_attn[-256:]
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Any		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)

	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False	):
	__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   get_detr_config(_UpperCamelCase	)

	# load original model from torch hub
	__lowerCAmelCase    :		List[str]		    =   {
	    'detr-resnet-50': 'detr_resnet50',
	    'detr-resnet-101': 'detr_resnet101',
	}
	logger.info(F"Converting model {model_name}..."	)
	__lowerCAmelCase    :		List[Any]		    =   torch.hub.load('facebookresearch/detr' ,						model_name_to_original_name[model_name] ,						pretrained=_UpperCamelCase	).eval()
	__lowerCAmelCase    :		str		    =   detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(_UpperCamelCase	):
		if is_panoptic:
			__lowerCAmelCase    :		List[str]		    =   'detr.' + src
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(_UpperCamelCase ,						is_panoptic=_UpperCamelCase	)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	__lowerCAmelCase    :		Optional[Any]		    =   'detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'	)
			    and not key.startswith('class_labels_classifier'	)
			    and not key.startswith('bbox_predictor'	)
			):
				__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Tuple		    =   val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__lowerCAmelCase    :		List[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Union[str, Any]		    =   val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Union[str, Any]		    =   val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				__lowerCAmelCase    :		Dict		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Optional[int]		    =   val

    # finally, create HuggingFace model and load state dict
	__lowerCAmelCase    :		Tuple		    =   DetrForSegmentation(_UpperCamelCase	) if is_panoptic else DetrForObjectDetection(_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase	)
	model.eval()

	# verify our conversion on an image
	__lowerCAmelCase    :		Optional[Any]		    =   'coco_panoptic' if is_panoptic else 'coco_detection'
	__lowerCAmelCase    :		List[str]		    =   DetrImageProcessor(format=_UpperCamelCase	)

	__lowerCAmelCase    :		Union[str, Any]		    =   processor(images=prepare_img() ,						return_tensors='pt'	)
	__lowerCAmelCase    :		Optional[int]		    =   encoding['pixel_values']

	__lowerCAmelCase    :		Optional[Any]		    =   detr(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   model(_UpperCamelCase	)

	assert torch.allclose(outputs.logits ,						original_outputs['pred_logits'] ,						atol=1e-3	)
	assert torch.allclose(outputs.pred_boxes ,						original_outputs['pred_boxes'] ,						atol=1e-3	)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks ,						original_outputs['pred_masks'] ,						atol=1e-4	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."	)
		Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
		model.save_pretrained(_UpperCamelCase	)
		processor.save_pretrained(_UpperCamelCase	)

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'	)
		model.push_to_hub(F"nielsr/{model_name}"	)
		processor.push_to_hub(F"nielsr/{model_name}"	)


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

							parser.add_argument(
							    """--model_name""",
							    default="""detr-resnet-50""",
							    type=str,
							    choices=["""detr-resnet-50""", """detr-resnet-101"""],
							    help="""Name of the DETR model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
							lowerCamelCase__           =	parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)