
"""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


UpperCAmelCase__      :    Any    							= logging.getLogger(__name__)



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						int					       =   '''summarization'''
				__UpperCamelCase      :						Tuple					       =   ['''loss''']
				__UpperCamelCase      :						List[Any]					       =   ROUGE_KEYS
				__UpperCamelCase      :						Union[str, Any]					       =   '''rouge2'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											if hparams.sortish_sampler and hparams.gpus > 1:
																		SCREAMING_SNAKE_CASE__						:       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    )
											SCREAMING_SNAKE_CASE__						:       int           =   Path(self.output_dir    ) / """metrics.json"""
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Path(self.output_dir    ) / """hparams.pkl"""
											pickle_save(self.hparams   ,       self.hparams_save_path    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   0
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   defaultdict(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   self.config.model_type
											SCREAMING_SNAKE_CASE__						:       int           =   self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict[str, List[str]]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[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"""    )

											SCREAMING_SNAKE_CASE__						:       int           =   True
											return readable_batch



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											return self.model(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.tokenizer.pad_token_id
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   batch["""input_ids"""], batch["""attention_mask"""]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   batch["""labels"""]
											if isinstance(self.model   ,       SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.model._shift_right(SCREAMING_SNAKE_CASE__    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Union[str, 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
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   decoder_input_ids
																		self.save_readable_batch(SCREAMING_SNAKE_CASE__    )

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

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



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return self.tokenizer.pad_token_id



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self._step(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   dict(zip(self.loss_names   ,       SCREAMING_SNAKE_CASE__    )    )
											# tokens per batch
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   batch["""input_ids"""].ne(self.pad    ).sum() + batch["""labels"""].ne(self.pad    ).sum()
											SCREAMING_SNAKE_CASE__						:       str           =   batch["""input_ids"""].shape[0]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   batch["""input_ids"""].eq(self.pad    ).sum()
											SCREAMING_SNAKE_CASE__						:       List[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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return self._generative_step(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__="val"    )					->		Dict:



											"""simple docstring"""




											self.step_count += 1
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
											SCREAMING_SNAKE_CASE__						:       Tuple           =   losses["""loss"""]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   {
											    k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
											}
											SCREAMING_SNAKE_CASE__						:       Tuple           =   (
											    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
											)
											SCREAMING_SNAKE_CASE__						:       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__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.step_count
											self.metrics[prefix].append(SCREAMING_SNAKE_CASE__    )  # callback writes this to self.metrics_save_path
											SCREAMING_SNAKE_CASE__						:       int           =   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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return calculate_rouge(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   time.time()

											# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
											SCREAMING_SNAKE_CASE__						:       int           =   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   ,       )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   (time.time() - ta) / batch["""input_ids"""].shape[0]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.ids_to_clean_text(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.ids_to_clean_text(batch["""labels"""]    )
											SCREAMING_SNAKE_CASE__						:       str           =   self._step(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   dict(zip(self.loss_names   ,       SCREAMING_SNAKE_CASE__    )    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.calc_generative_metrics(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											return self._generative_step(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return self.validation_epoch_end(SCREAMING_SNAKE_CASE__   ,       prefix="""test"""    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		SeqaSeqDataset:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.n_obs[type_path]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.target_lens[type_path]
											SCREAMING_SNAKE_CASE__						:       Optional[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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = False    )					->		DataLoader:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_dataset(SCREAMING_SNAKE_CASE__    )

											if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   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":
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   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 	__magic_name__    (self    )					->		DataLoader:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_dataloader("""train"""   ,       batch_size=self.hparams.train_batch_size   ,       shuffle=SCREAMING_SNAKE_CASE__    )
											return dataloader



				def 	__magic_name__    (self    )					->		DataLoader:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		DataLoader:



											"""simple docstring"""




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



				@staticmethod
				def 	__magic_name__    (SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											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 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   '''translation'''
				__UpperCamelCase      :						List[Any]					       =   ['''loss''']
				__UpperCamelCase      :						Optional[Any]					       =   ['''bleu''']
				__UpperCamelCase      :						Dict					       =   '''bleu'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											super().__init__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   hparams.src_lang
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   hparams.tgt_lang



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		dict:



											"""simple docstring"""




											return calculate_bleu(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )






def lowercase_  (   _snake_case   ,_snake_case=None						):
							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:
																					SCREAMING_SNAKE_CASE__						:       SummarizationModule           =   SummarizationModule(_snake_case						)
														else:
																					SCREAMING_SNAKE_CASE__						:       SummarizationModule           =   TranslationModule(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   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__						:       int           =   True  # don't pollute wandb logs unnecessarily
							elif args.logger_name == "wandb":
														from pytorch_lightning.loggers import WandbLogger

														SCREAMING_SNAKE_CASE__						:       Dict           =   os.environ.get("""WANDB_PROJECT"""   ,_snake_case						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   WandbLogger(name=model.output_dir.name   ,project=_snake_case						)

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

														SCREAMING_SNAKE_CASE__						:       List[Any]           =   WandbLogger(name=model.output_dir.name   ,project=f'''hf_{dataset}'''						)

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

							SCREAMING_SNAKE_CASE__						:       int           =   args.val_metric == """loss"""
							SCREAMING_SNAKE_CASE__						:       pl.Trainer           =   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

							SCREAMING_SNAKE_CASE__						:       Dict           =   """"""
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   sorted(glob.glob(os.path.join(args.output_dir   ,"""*.ckpt"""						)   ,recursive=_snake_case						)						)
							if checkpoints:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   checkpoints[-1]
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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__":
					UpperCAmelCase__      :    Tuple    							= argparse.ArgumentParser()
					UpperCAmelCase__      :    Optional[int]    							= pl.Trainer.add_argparse_args(parser)
					UpperCAmelCase__      :    int    							= SummarizationModule.add_model_specific_args(parser, os.getcwd())

					UpperCAmelCase__      :    List[str]    							= parser.parse_args()

					main(args)






"""simple docstring"""




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




