
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A     :	Dict    = R'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''







@add_start_docstrings(lowerCamelCase__			)
class 			__A( lowerCamelCase__			):
				snake_case_						   =    '''rag'''
				snake_case_						   =    True




				def __init__(     self		,     _snake_case=None		,     _snake_case=True		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=" / "		,     _snake_case=" // "		,     _snake_case=5		,     _snake_case=300		,     _snake_case=768		,     _snake_case=8		,     _snake_case="wiki_dpr"		,     _snake_case="train"		,     _snake_case="compressed"		,     _snake_case=None		,     _snake_case=None		,     _snake_case=False		,     _snake_case=False		,     _snake_case=0.0		,     _snake_case=True		,     _snake_case=False		,     _snake_case=False		,     _snake_case=False		,     _snake_case=True		,     _snake_case=None		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								super().__init__(
								    bos_token_id=lowerCAmelCase__		,     pad_token_id=lowerCAmelCase__		,     eos_token_id=lowerCAmelCase__		,     decoder_start_token_id=lowerCAmelCase__		,     forced_eos_token_id=lowerCAmelCase__		,     is_encoder_decoder=lowerCAmelCase__		,     prefix=lowerCAmelCase__		,     vocab_size=lowerCAmelCase__		,     **lowerCAmelCase__		,     )
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								__a										=    kwargs.pop('''question_encoder'''				)
								__a										=    question_encoder_config.pop('''model_type'''				)
								__a										=    kwargs.pop('''generator'''				)
								__a										=    decoder_config.pop('''model_type'''				)

								from ..auto.configuration_auto import AutoConfig

								__a										=    AutoConfig.for_model(lowerCAmelCase__		,     **lowerCAmelCase__				)
								__a										=    AutoConfig.for_model(lowerCAmelCase__		,     **lowerCAmelCase__				)

								__a										=    reduce_loss
								__a										=    label_smoothing
								__a										=    exclude_bos_score
								__a										=    do_marginalize

								__a										=    title_sep
								__a										=    doc_sep
								__a										=    n_docs
								__a										=    max_combined_length

								__a										=    dataset
								__a										=    dataset_split
								__a										=    index_name

								__a										=    retrieval_vector_size
								__a										=    retrieval_batch_size
								__a										=    passages_path
								__a										=    index_path
								__a										=    use_dummy_dataset

								__a										=    output_retrieved

								__a										=    do_deduplication

								__a										=    use_cache

								if self.forced_eos_token_id is None:
												__a										=    getattr(self.generator		,     '''forced_eos_token_id'''		,     lowerCAmelCase__				)




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								return cls(question_encoder=question_encoder_config.to_dict()		,     generator=generator_config.to_dict()		,     **lowerCAmelCase__				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    copy.deepcopy(self.__dict__				)
								__a										=    self.question_encoder.to_dict()
								__a										=    self.generator.to_dict()
								__a										=    self.__class__.model_type
								return output




'''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__       :	str    = logging.getLogger(__name__)






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Union[str, Any]			    =		'''summarization'''
			_A      :		Optional[Any]			    =		['''loss''']
			_A      :		Tuple			    =		ROUGE_KEYS
			_A      :		int			    =		'''rouge2'''

			def __init__(							self     : int							,     lowerCAmelCase__     : Union[str, Any]							,     **lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					if hparams.sortish_sampler and hparams.gpus > 1:
							__SCREAMING_SNAKE_CASE       :		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__(lowerCAmelCase__							,     num_labels=lowerCAmelCase__							,     mode=self.mode							,     **lowerCAmelCase__						)
					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       :		Optional[Any]           =		Path(self.output_dir						) / """hparams.pkl"""
					pickle_save(self.hparams							,     self.hparams_save_path						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		0
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		defaultdict(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.config.model_type
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		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       :		List[Any]           =		{
					    """train""": self.hparams.n_train,
					    """val""": self.hparams.n_val,
					    """test""": self.hparams.n_test,
					}
					__SCREAMING_SNAKE_CASE       :		Any           =		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

					__SCREAMING_SNAKE_CASE       :		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()						)

					__SCREAMING_SNAKE_CASE       :		Any           =		get_git_info()["""repo_sha"""]
					__SCREAMING_SNAKE_CASE       :		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							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
							__SCREAMING_SNAKE_CASE       :		Any           =		self.decoder_start_token_id
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(
					    SeqaSeqDataset if hasattr(self.tokenizer							,     """prepare_seq2seq_batch"""						) else LegacySeqaSeqDataset
					)
					__SCREAMING_SNAKE_CASE       :		Dict           =		False
					__SCREAMING_SNAKE_CASE       :		Any           =		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       :		Optional[int]           =		self.hparams.eval_max_gen_length
					else:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model.config.max_length
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		{
					    k: self.tokenizer.batch_decode(v.tolist()						) if """mask""" not in k else v.shape for k, v in batch.items()
					}
					save_json(lowerCAmelCase__							,     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       :		Optional[int]           =		True
					return readable_batch

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Any							,     **lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					return self.model(lowerCAmelCase__							,     **lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.batch_decode(
					    lowerCAmelCase__							,     skip_special_tokens=lowerCAmelCase__							,     clean_up_tokenization_spaces=lowerCAmelCase__						)
					return lmap(str.strip							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.pad_token_id
					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		str           =		batch["""input_ids"""], batch["""attention_mask"""]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		batch["""labels"""]
					if isinstance(self.model							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		str           =		self.model._shift_right(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		shift_tokens_right(lowerCAmelCase__							,     lowerCAmelCase__						)
					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(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		int           =		self(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     decoder_input_ids=lowerCAmelCase__							,     use_cache=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		outputs["""logits"""]
					if self.hparams.label_smoothing == 0:
							# Same behavior as modeling_bart.py, besides ignoring pad_token_id
							__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.CrossEntropyLoss(ignore_index=lowerCAmelCase__						)

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

			@property
			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					return self.tokenizer.pad_token_id

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self._step(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		dict(zip(self.loss_names							,     lowerCAmelCase__						)						)
					# tokens per batch
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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       :		str           =		batch["""input_ids"""].eq(self.pad						).sum()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		batch["""input_ids"""].eq(self.pad						).float().mean()
					# TODO(SS): make a wandb summary metric for this
					return {"loss": loss_tensors[0], "log": logs}

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					return self._generative_step(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Union[str, Any]="val"						):





					"""simple docstring"""






					self.step_count += 1
					__SCREAMING_SNAKE_CASE       :		int           =		{k: torch.stack([x[k] for x in outputs]						).mean() for k in self.loss_names}
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		losses["""loss"""]
					__SCREAMING_SNAKE_CASE       :		int           =		{
					    k: np.array([x[k] for x in outputs]						).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
					}
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		(
					    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
					)
					__SCREAMING_SNAKE_CASE       :		torch.FloatTensor           =		torch.tensor(lowerCAmelCase__						).type_as(lowerCAmelCase__						)
					generative_metrics.update({k: v.item() for k, v in losses.items()}						)
					losses.update(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		{F"{prefix}_avg_{k}": x for k, x in losses.items()}
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.step_count
					self.metrics[prefix].append(lowerCAmelCase__						)  # 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 UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






					return calculate_rouge(lowerCAmelCase__							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		time.time()

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

			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					return self._generative_step(lowerCAmelCase__						)

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





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.n_obs[type_path]
					__SCREAMING_SNAKE_CASE       :		str           =		self.target_lens[type_path]
					__SCREAMING_SNAKE_CASE       :		str           =		self.dataset_class(
					    self.tokenizer							,     type_path=lowerCAmelCase__							,     n_obs=lowerCAmelCase__							,     max_target_length=lowerCAmelCase__							,     **self.dataset_kwargs							,     )
					return dataset

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.get_dataset(lowerCAmelCase__						)

					if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		dataset.make_sortish_sampler(lowerCAmelCase__							,     distributed=self.hparams.gpus > 1						)
							return DataLoader(
							    lowerCAmelCase__							,     batch_size=lowerCAmelCase__							,     collate_fn=dataset.collate_fn							,     shuffle=lowerCAmelCase__							,     num_workers=self.num_workers							,     sampler=lowerCAmelCase__							,     )

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

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.get_dataloader("""train"""							,     batch_size=self.hparams.train_batch_size							,     shuffle=lowerCAmelCase__						)
					return dataloader

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





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






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



			@staticmethod
			def UpperCamelCase__					(							lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					BaseTransformer.add_model_specific_args(lowerCAmelCase__							,     lowerCAmelCase__						)
					add_generic_args(lowerCAmelCase__							,     lowerCAmelCase__						)
					parser.add_argument(
					    """--max_source_length"""							,     default=1_0_2_4							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--max_target_length"""							,     default=5_6							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--val_max_target_length"""							,     default=1_4_2							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--test_max_target_length"""							,     default=1_4_2							,     type=lowerCAmelCase__							,     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=lowerCAmelCase__						)
					parser.add_argument("""--overwrite_output_dir"""							,     action="""store_true"""							,     default=lowerCAmelCase__						)
					parser.add_argument("""--max_tokens_per_batch"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__						)
					parser.add_argument("""--logger_name"""							,     type=lowerCAmelCase__							,     choices=["""default""", """wandb""", """wandb_shared"""]							,     default="""default"""						)
					parser.add_argument("""--n_train"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--n_val"""							,     type=lowerCAmelCase__							,     default=5_0_0							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--n_test"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument(
					    """--task"""							,     type=lowerCAmelCase__							,     default="""summarization"""							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--label_smoothing"""							,     type=lowerCAmelCase__							,     default=0.0							,     required=lowerCAmelCase__						)
					parser.add_argument("""--src_lang"""							,     type=lowerCAmelCase__							,     default=""""""							,     required=lowerCAmelCase__						)
					parser.add_argument("""--tgt_lang"""							,     type=lowerCAmelCase__							,     default=""""""							,     required=lowerCAmelCase__						)
					parser.add_argument("""--eval_beams"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     required=lowerCAmelCase__						)
					parser.add_argument(
					    """--val_metric"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     required=lowerCAmelCase__							,     choices=["""bleu""", """rouge2""", """loss""", None]						)
					parser.add_argument("""--eval_max_gen_length"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     help="""never generate more than n tokens"""						)
					parser.add_argument("""--save_top_k"""							,     type=lowerCAmelCase__							,     default=1							,     required=lowerCAmelCase__							,     help="""How many checkpoints to save"""						)
					parser.add_argument(
					    """--early_stopping_patience"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help=(
					        """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
					        """ val_check_interval will effect it."""
					    )							,     )
					return parser






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		List[Any]			    =		'''translation'''
			_A      :		int			    =		['''loss''']
			_A      :		Union[str, Any]			    =		['''bleu''']
			_A      :		Dict			    =		'''bleu'''

			def __init__(							self     : Any							,     lowerCAmelCase__     : int							,     **lowerCAmelCase__     : Any						):





					"""simple docstring"""






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



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





					"""simple docstring"""






					return calculate_bleu(lowerCAmelCase__							,     lowerCAmelCase__						)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]       ,      _lowerCamelCase:   str=None					):
		Path(args.output_dir					).mkdir(exist_ok=_lowerCamelCase					)
		check_output_dir(_lowerCamelCase       ,      expected_items=3					)

		if model is None:
				if "summarization" in args.task:
						__SCREAMING_SNAKE_CASE       :		SummarizationModule           =		SummarizationModule(_lowerCamelCase					)
				else:
						__SCREAMING_SNAKE_CASE       :		SummarizationModule           =		TranslationModule(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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       :		str           =		True  # don't pollute wandb logs unnecessarily
		elif args.logger_name == "wandb":
				from pytorch_lightning.loggers import WandbLogger

				__SCREAMING_SNAKE_CASE       :		Any           =		os.environ.get("""WANDB_PROJECT"""       ,      _lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		str           =		WandbLogger(name=model.output_dir.name       ,      project=_lowerCamelCase					)

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

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		WandbLogger(name=model.output_dir.name       ,      project=F"hf_{dataset}"					)

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

		__SCREAMING_SNAKE_CASE       :		Dict           =		args.val_metric == """loss"""
		__SCREAMING_SNAKE_CASE       :		pl.Trainer           =		generic_train(
		    _lowerCamelCase       ,      _lowerCamelCase       ,      logging_callback=SeqaSeqLoggingCallback()       ,      checkpoint_callback=get_checkpoint_callback(
		        args.output_dir       ,      model.val_metric       ,      args.save_top_k       ,      _lowerCamelCase					)       ,      early_stopping_callback=_lowerCamelCase       ,      logger=_lowerCamelCase       ,      )
		pickle_save(model.hparams       ,      model.output_dir / """hparams.pkl"""					)
		if not args.do_predict:
				return model

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

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

						main(args)