



'''simple docstring'''




import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


UpperCamelCase__       :	int    = '''\

'''

UpperCamelCase__       :	Dict    = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

UpperCamelCase__       :	Dict    = '''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,					_KWARGS_DESCRIPTION    )
class 						_UpperCamelCase  (							datasets.Metric    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					return datasets.MetricInfo(
					    description=_DESCRIPTION							,     citation=_CITATION							,     inputs_description=_KWARGS_DESCRIPTION							,     features=datasets.Features(
					        {
					            """input_texts""": datasets.Value("""string"""						),
					        }						)							,     reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""]							,     )



			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : int = 1_6							,     lowerCAmelCase__     : bool = True							,     lowerCAmelCase__     : Union[str, Any]=None						):





					"""simple docstring"""






					if device is not None:
							assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
							if device == "gpu":
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		"""cuda"""
					else:
							__SCREAMING_SNAKE_CASE       :		int           =		"""cuda""" if torch.cuda.is_available() else """cpu"""

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		AutoModelForCausalLM.from_pretrained(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model.to(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		AutoTokenizer.from_pretrained(lowerCAmelCase__						)

					# if batch_size > 1 (which generally leads to padding being required), and
					# if there is not an already assigned pad_token, assign an existing
					# special token to also be the padding token
					if tokenizer.pad_token is None and batch_size > 1:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		list(tokenizer.special_tokens_map_extended.values()						)
							# check that the model already has at least one special token defined
							assert (
							    len(lowerCAmelCase__						) > 0
							), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
							# assign one of the special tokens to also be the pad token
							tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]}						)

					if add_start_token:
							# leave room for <BOS> token to be added:
							assert (
							    tokenizer.bos_token is not None
							), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
							__SCREAMING_SNAKE_CASE       :		List[str]           =		model.config.max_length - 1
					else:
							__SCREAMING_SNAKE_CASE       :		List[str]           =		model.config.max_length

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		tokenizer(
					    lowerCAmelCase__							,     add_special_tokens=lowerCAmelCase__							,     padding=lowerCAmelCase__							,     truncation=lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     return_tensors="""pt"""							,     return_attention_mask=lowerCAmelCase__							,     ).to(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		encodings["""input_ids"""]
					__SCREAMING_SNAKE_CASE       :		str           =		encodings["""attention_mask"""]

					# check that each input is long enough:
					if add_start_token:
							assert torch.all(torch.ge(attn_masks.sum(1						)							,     1						)						), "Each input text must be at least one token long."
					else:
							assert torch.all(
							    torch.ge(attn_masks.sum(1						)							,     2						)						), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		[]
					__SCREAMING_SNAKE_CASE       :		Dict           =		CrossEntropyLoss(reduction="""none"""						)

					for start_index in logging.tqdm(range(0							,     len(lowerCAmelCase__						)							,     lowerCAmelCase__						)						):
							__SCREAMING_SNAKE_CASE       :		str           =		min(start_index + batch_size							,     len(lowerCAmelCase__						)						)
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		encoded_texts[start_index:end_index]
							__SCREAMING_SNAKE_CASE       :		Tuple           =		attn_masks[start_index:end_index]

							if add_start_token:
									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0						)						).to(lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE       :		Any           =		torch.cat([bos_tokens_tensor, encoded_batch]							,     dim=1						)
									__SCREAMING_SNAKE_CASE       :		Dict           =		torch.cat(
									    [torch.ones(bos_tokens_tensor.size()							,     dtype=torch.intaa						).to(lowerCAmelCase__						), attn_mask]							,     dim=1						)

							__SCREAMING_SNAKE_CASE       :		List[Any]           =		encoded_batch

							with torch.no_grad():
									__SCREAMING_SNAKE_CASE       :		List[str]           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__						).logits

							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		out_logits[..., :-1, :].contiguous()
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		labels[..., 1:].contiguous()
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		attn_mask[..., 1:].contiguous()

							__SCREAMING_SNAKE_CASE       :		List[str]           =		torch.expa(
							    (loss_fct(shift_logits.transpose(1							,     2						)							,     lowerCAmelCase__						) * shift_attention_mask_batch).sum(1						)
							    / shift_attention_mask_batch.sum(1						)						)

							ppls += perplexity_batch.tolist()

					return {"perplexities": ppls, "mean_perplexity": np.mean(lowerCAmelCase__						)}




'''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)