




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


a_		:						int             =    logging.getLogger(__name__)

class 		_snake_case  (  __A					):
			_lowercase		:    List[str]			     =							'''summarization'''
			_lowercase		:    List[str]			     =							['''loss''']
			_lowercase		:    Any			     =							ROUGE_KEYS
			_lowercase		:    Optional[Any]			     =							'''rouge2'''






			def __init__(       self		,					a		,					**a)      ->       Any:





						if hparams.sortish_sampler and hparams.gpus > 1:
									SCREAMING_SNAKE_CASE						= 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)
						SCREAMING_SNAKE_CASE						= Path(self.output_dir) / 'metrics.json'
						SCREAMING_SNAKE_CASE						= Path(self.output_dir) / 'hparams.pkl'
						pickle_save(self.hparams		,					self.hparams_save_path)
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= defaultdict(a)
						SCREAMING_SNAKE_CASE						= self.config.model_type
						SCREAMING_SNAKE_CASE						= self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

						SCREAMING_SNAKE_CASE						= {
						    '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())

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       str:
						SCREAMING_SNAKE_CASE						= {
						    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')

						SCREAMING_SNAKE_CASE						= True
						return readable_batch






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					**a)      ->       List[str]:
						return self.model(a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self.tokenizer.batch_decode(
						    a		,					skip_special_tokens=a		,					clean_up_tokenization_spaces=a)
						return lmap(str.strip		,					a)






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

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

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






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						return self.tokenizer.pad_token_id






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self._step(a)

						SCREAMING_SNAKE_CASE						= dict(zip(self.loss_names		,					a))
						# tokens per batch
						SCREAMING_SNAKE_CASE						= batch['input_ids'].ne(self.pad).sum() + batch['labels'].ne(self.pad).sum()
						SCREAMING_SNAKE_CASE						= batch['input_ids'].shape[0]
						SCREAMING_SNAKE_CASE						= batch['input_ids'].eq(self.pad).sum()
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Any:
						return self._generative_step(a)






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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= time.time()

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Union[str, Any]:
						return self._generative_step(a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						return self.validation_epoch_end(a		,					prefix='test')






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.n_obs[type_path]
						SCREAMING_SNAKE_CASE						= self.target_lens[type_path]
						SCREAMING_SNAKE_CASE						= self.dataset_class(
						    self.tokenizer		,					type_path=a		,					n_obs=a		,					max_target_length=a		,					**self.dataset_kwargs		,					)
						return dataset






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = False)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_dataset(a)

						if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
									SCREAMING_SNAKE_CASE						= 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":
									SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.get_dataloader('train'		,					batch_size=self.hparams.train_batch_size		,					shuffle=a)
						return dataloader






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						return self.get_dataloader('val'		,					batch_size=self.hparams.eval_batch_size)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						return self.get_dataloader('test'		,					batch_size=self.hparams.eval_batch_size)







			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a		,					a)      ->       List[Any]:
						BaseTransformer.add_model_specific_args(a		,					a)
						add_generic_args(a		,					a)
						parser.add_argument(
						    '--max_source_length'		,					default=1024		,					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=56		,					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=142		,					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=142		,					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=500		,					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 		_snake_case  (  __A					):
			_lowercase		:    str			     =							'''translation'''
			_lowercase		:    Dict			     =							['''loss''']
			_lowercase		:    int			     =							['''bleu''']
			_lowercase		:    int			     =							'''bleu'''






			def __init__(       self		,					a		,					**a)      ->       List[Any]:
						super().__init__(a		,					**a)
						SCREAMING_SNAKE_CASE						= hparams.src_lang
						SCREAMING_SNAKE_CASE						= hparams.tgt_lang







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Any:
						return calculate_bleu(a		,					a)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=None):
			Path(args.output_dir).mkdir(exist_ok=__lowercase)
			check_output_dir(__lowercase							,       expected_items=3)

			if model is None:
						if "summarization" in args.task:
									SCREAMING_SNAKE_CASE						= SummarizationModule(__lowercase)
						else:
									SCREAMING_SNAKE_CASE						= TranslationModule(__lowercase)
			SCREAMING_SNAKE_CASE						= 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')
			):
						SCREAMING_SNAKE_CASE						= True  # don't pollute wandb logs unnecessarily
			elif args.logger_name == "wandb":
						from pytorch_lightning.loggers import WandbLogger

						SCREAMING_SNAKE_CASE						= os.environ.get('WANDB_PROJECT'							,       __lowercase)
						SCREAMING_SNAKE_CASE						= WandbLogger(name=model.output_dir.name							,       project=__lowercase)

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

						SCREAMING_SNAKE_CASE						= WandbLogger(name=model.output_dir.name							,       project=F'''hf_{dataset}''')

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

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

			SCREAMING_SNAKE_CASE						= ''
			SCREAMING_SNAKE_CASE						= sorted(glob.glob(os.path.join(args.output_dir							,       '*.ckpt')							,       recursive=__lowercase))
			if checkpoints:
						SCREAMING_SNAKE_CASE						= checkpoints[-1]
						SCREAMING_SNAKE_CASE						= 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__":
				a_		:						Union[str, Any]             =    argparse.ArgumentParser()
				a_		:						List[Any]             =    pl.Trainer.add_argparse_args(parser)
				a_		:						int             =    SummarizationModule.add_model_specific_args(parser, os.getcwd())

				a_		:						str             =    parser.parse_args()

				main(args)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


a_		:						int             =    {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




