"""simple docstring"""






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCAmelCase__  :  int						     =	logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase__  :  Any						     =	{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCAmelCase__  :  int						     =	{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCAmelCase__  :  str						     =	sorted(arg_to_scheduler.keys())
lowerCAmelCase__  :  List[Any]						     =	'{' + ', '.join(arg_to_scheduler_choices) + '}'
class 				snake_case       ( pl.LightningModule				):




					"""simple docstring"""

					def __init__(				self   :	Dict    ,lowerCamelCase__   :	argparse.Namespace    ,lowerCamelCase__   :	Optional[Any]=None    ,lowerCamelCase__   :	Any="base"    ,lowerCamelCase__   :	Optional[int]=None    ,lowerCamelCase__   :	str=None    ,lowerCamelCase__   :	Any=None    ,**lowerCamelCase__   :	Optional[int]    ,):
									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(lowerCamelCase__			)
									UpperCAmelCase__             =							0
									UpperCAmelCase__             =							Path(self.hparams.output_dir			)
									UpperCAmelCase__             =							self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
													UpperCAmelCase__             =							AutoConfig.from_pretrained(
													    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,**({'num_labels': num_labels} if num_labels is not None else {})    ,cache_dir=lowerCamelCase__    ,**lowerCamelCase__    ,)
									else:
													UpperCAmelCase__             =							config

									UpperCAmelCase__             =							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
									for p in extra_model_params:
													if getattr(self.hparams    ,lowerCamelCase__    ,lowerCamelCase__			):
																	assert hasattr(self.config    ,lowerCamelCase__			), f'''model config doesn\'t have a `{p}` attribute'''
																	setattr(self.config    ,lowerCamelCase__    ,getattr(self.hparams    ,lowerCamelCase__			)			)

									if tokenizer is None:
													UpperCAmelCase__             =							AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,cache_dir=lowerCamelCase__    ,)
									else:
													UpperCAmelCase__             =							tokenizer
									UpperCAmelCase__             =							MODEL_MODES[mode]
									if model is None:
													UpperCAmelCase__             =							self.model_type.from_pretrained(
													    self.hparams.model_name_or_path    ,from_tf=bool('.ckpt' in self.hparams.model_name_or_path			)    ,config=self.config    ,cache_dir=lowerCamelCase__    ,)
									else:
													UpperCAmelCase__             =							model







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,*lowerCamelCase__   :	Tuple    ,**lowerCamelCase__   :	Optional[int]			):
									UpperCAmelCase__             =							self.model_type.from_pretrained(*lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							arg_to_scheduler[self.hparams.lr_scheduler]
									UpperCAmelCase__             =							get_schedule_func(
									    self.opt    ,num_warmup_steps=self.hparams.warmup_steps    ,num_training_steps=self.total_steps()			)
									UpperCAmelCase__             =							{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
									return scheduler







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							self.model
									UpperCAmelCase__             =							['bias', 'LayerNorm.weight']
									UpperCAmelCase__             =							[
									    {
									        'params': [
									            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)
									        ],  # check this named paramters
									        'weight_decay': self.hparams.weight_decay,
									    },
									    {
									        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)],
									        'weight_decay': 0.0,
									    },
									]
									if self.hparams.adafactor:
													UpperCAmelCase__             =							Adafactor(
													    lowerCamelCase__    ,lr=self.hparams.learning_rate    ,scale_parameter=lowerCamelCase__    ,relative_step=lowerCamelCase__			)

									else:
													UpperCAmelCase__             =							AdamW(
													    lowerCamelCase__    ,lr=self.hparams.learning_rate    ,eps=self.hparams.adam_epsilon			)
									UpperCAmelCase__             =							optimizer

									UpperCAmelCase__             =							self.get_lr_scheduler()

									return [optimizer], [scheduler]







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	List[Any]			):
									return self.validation_step(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	List[str]			):
									return self.validation_end(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							max(1    ,self.hparams.gpus			)  # TODO: consider num_tpu_cores
									UpperCAmelCase__             =							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	int			):
									if stage == "test":
													UpperCAmelCase__             =							len(self.test_dataloader().dataset			)
									else:
													UpperCAmelCase__             =							self.get_dataloader('train'    ,self.hparams.train_batch_size    ,shuffle=lowerCamelCase__			)
													UpperCAmelCase__             =							len(self.train_dataloader().dataset			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	bool = False			):
									raise NotImplementedError('You must implement this for your task'			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									return self.train_loader







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									return self.get_dataloader('dev'    ,self.hparams.eval_batch_size    ,shuffle=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str			):
									return self.get_dataloader('test'    ,self.hparams.eval_batch_size    ,shuffle=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	List[Any]			):
									return os.path.join(
									    self.hparams.data_dir    ,'cached_{}_{}_{}'.format(
									        lowerCamelCase__    ,list(filter(lowerCamelCase__    ,self.hparams.model_name_or_path.split('/'			)			)			).pop()    ,str(self.hparams.max_seq_length			)    ,)    ,)







					@pl.utilities.rank_zero_only
					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Dict[str, Any]			):
									UpperCAmelCase__             =							self.output_dir.joinpath('best_tfmr'			)
									UpperCAmelCase__             =							self.step_count
									self.model.save_pretrained(lowerCamelCase__			)
									self.tokenizer.save_pretrained(lowerCamelCase__			)







					@staticmethod
					def 		__lowerCAmelCase						(				lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	str			):
									parser.add_argument(
									    '--model_name_or_path'    ,default=lowerCamelCase__    ,type=lowerCamelCase__    ,required=lowerCamelCase__    ,help='Path to pretrained model or model identifier from huggingface.co/models'    ,)
									parser.add_argument(
									    '--config_name'    ,default=''    ,type=lowerCamelCase__    ,help='Pretrained config name or path if not the same as model_name'			)
									parser.add_argument(
									    '--tokenizer_name'    ,default=lowerCamelCase__    ,type=lowerCamelCase__    ,help='Pretrained tokenizer name or path if not the same as model_name'    ,)
									parser.add_argument(
									    '--cache_dir'    ,default=str(Path(lowerCamelCase__			).parent / 'test_run' / 'cache'			)    ,type=lowerCamelCase__    ,help='Where do you want to store the pre-trained models downloaded from huggingface.co'    ,)
									parser.add_argument(
									    '--encoder_layerdrop'    ,type=lowerCamelCase__    ,help='Encoder layer dropout probability (Optional). Goes into model.config'    ,)
									parser.add_argument(
									    '--decoder_layerdrop'    ,type=lowerCamelCase__    ,help='Decoder layer dropout probability (Optional). Goes into model.config'    ,)
									parser.add_argument(
									    '--dropout'    ,type=lowerCamelCase__    ,help='Dropout probability (Optional). Goes into model.config'    ,)
									parser.add_argument(
									    '--attention_dropout'    ,type=lowerCamelCase__    ,help='Attention dropout probability (Optional). Goes into model.config'    ,)
									parser.add_argument('--learning_rate'    ,default=5e-5    ,type=lowerCamelCase__    ,help='The initial learning rate for Adam.'			)
									parser.add_argument(
									    '--lr_scheduler'    ,default='linear'    ,choices=lowerCamelCase__    ,metavar=lowerCamelCase__    ,type=lowerCamelCase__    ,help='Learning rate scheduler'    ,)
									parser.add_argument('--weight_decay'    ,default=0.0    ,type=lowerCamelCase__    ,help='Weight decay if we apply some.'			)
									parser.add_argument('--adam_epsilon'    ,default=1e-8    ,type=lowerCamelCase__    ,help='Epsilon for Adam optimizer.'			)
									parser.add_argument('--warmup_steps'    ,default=0    ,type=lowerCamelCase__    ,help='Linear warmup over warmup_steps.'			)
									parser.add_argument('--num_workers'    ,default=4    ,type=lowerCamelCase__    ,help='kwarg passed to DataLoader'			)
									parser.add_argument('--num_train_epochs'    ,dest='max_epochs'    ,default=3    ,type=lowerCamelCase__			)
									parser.add_argument('--train_batch_size'    ,default=32    ,type=lowerCamelCase__			)
									parser.add_argument('--eval_batch_size'    ,default=32    ,type=lowerCamelCase__			)
									parser.add_argument('--adafactor'    ,action='store_true'			)






class 				snake_case       ( pl.Callback				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	Optional[int]			):
									if (
									    trainer.is_global_zero and trainer.global_rank == 0
									):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
													pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 				snake_case       ( pl.Callback				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Optional[Any]			):
									# print(pl_module.model.rag)
									for name, param in pl_module.model.rag.named_parameters():
													if param.grad is None:
																	print(lowerCamelCase__			)






class 				snake_case       ( pl.Callback				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Union[str, Any]    ,lowerCamelCase__   :	Optional[Any]			):
									UpperCAmelCase__             =							trainer.lr_schedulers[0]['scheduler']
									UpperCAmelCase__             =							{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
									pl_module.logger.log_metrics(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	pl.Trainer    ,lowerCamelCase__   :	pl.LightningModule			):
									rank_zero_info('***** Validation results *****'			)
									UpperCAmelCase__             =							trainer.callback_metrics
									# Log results
									for key in sorted(lowerCamelCase__			):
													if key not in ["log", "progress_bar"]:
																	rank_zero_info('{} = {}\n'.format(lowerCamelCase__    ,str(metrics[key]			)			)			)







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	pl.Trainer    ,lowerCamelCase__   :	pl.LightningModule			):
									rank_zero_info('***** Test results *****'			)
									UpperCAmelCase__             =							trainer.callback_metrics
									# Log and save results to file
									UpperCAmelCase__             =							os.path.join(pl_module.hparams.output_dir    ,'test_results.txt'			)
									with open(lowerCamelCase__    ,'w'			) as writer:
													for key in sorted(lowerCamelCase__			):
																	if key not in ["log", "progress_bar"]:
																					rank_zero_info('{} = {}\n'.format(lowerCamelCase__    ,str(metrics[key]			)			)			)
																					writer.write('{} = {}\n'.format(lowerCamelCase__    ,str(metrics[key]			)			)			)






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '--output_dir'					,			default=str(Path(lowerCamelCase       ).parent / 'test_run' / 'model_checkpoints'       )					,			type=lowerCamelCase					,			help='The output directory where the model predictions and checkpoints will be written.'					,			)
				parser.add_argument(
				    '--fp16'					,			action='store_true'					,			help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'					,			)

				parser.add_argument(
				    '--fp16_opt_level'					,			type=lowerCamelCase					,			default='O2'					,			help=(
				        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
				        'See details at https://nvidia.github.io/apex/amp.html'
				    )					,			)
				parser.add_argument('--n_tpu_cores'					,			dest='tpu_cores'					,			type=lowerCamelCase       )
				parser.add_argument('--max_grad_norm'					,			dest='gradient_clip_val'					,			default=1.0					,			type=lowerCamelCase					,			help='Max gradient norm'       )
				parser.add_argument('--do_train'					,			action='store_true'					,			help='Whether to run training.'       )
				parser.add_argument('--do_predict'					,			action='store_true'					,			help='Whether to run predictions on the test set.'       )
				parser.add_argument(
				    '--gradient_accumulation_steps'					,			dest='accumulate_grad_batches'					,			type=lowerCamelCase					,			default=1					,			help='Number of updates steps to accumulate before performing a backward/update pass.'					,			)
				parser.add_argument('--seed'					,			type=lowerCamelCase					,			default=4_2					,			help='random seed for initialization'       )
				parser.add_argument(
				    '--data_dir'					,			default=str(Path(lowerCamelCase       ).parent / 'test_run' / 'dummy-train-data'       )					,			type=lowerCamelCase					,			help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'					,			)







def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=True					,			lowerCamelCase=[]					,			lowerCamelCase=None					,			lowerCamelCase=None					,			**lowerCamelCase					,			):
				pl.seed_everything(args.seed       )

				# init model
				UpperCAmelCase__             =							Path(model.hparams.output_dir       )
				odir.mkdir(exist_ok=lowerCamelCase       )

				# add custom checkpoints
				if checkpoint_callback is None:
								UpperCAmelCase__             =							pl.callbacks.ModelCheckpoint(
								    filepath=args.output_dir					,			prefix='checkpoint'					,			monitor='val_loss'					,			mode='min'					,			save_top_k=1       )
				if early_stopping_callback:
								extra_callbacks.append(lowerCamelCase       )
				if logging_callback is None:
								UpperCAmelCase__             =							LoggingCallback()

				UpperCAmelCase__             =							{}

				if args.fpaa:
								UpperCAmelCase__             =							1_6

				if args.gpus > 1:
								UpperCAmelCase__             =							'auto'
								UpperCAmelCase__             =							'ddp'

				UpperCAmelCase__             =							args.accumulate_grad_batches
				UpperCAmelCase__             =							None
				UpperCAmelCase__             =							'auto'

				UpperCAmelCase__             =							pl.Trainer.from_argparse_args(
				    lowerCamelCase					,			weights_summary=lowerCamelCase					,			callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,			logger=lowerCamelCase					,			val_check_interval=1					,			num_sanity_val_steps=2					,			**lowerCamelCase					,			)

				if args.do_train:
								trainer.fit(lowerCamelCase       )

				else:
								print('RAG modeling tests with new set functions successfuly executed!'       )
				return trainer




"""simple docstring"""






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

import datasets
from datasets import logging


lowerCAmelCase__  :  List[Any]						     =	'\\n\n'

lowerCAmelCase__  :  Tuple						     =	'\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

lowerCAmelCase__  :  str						     =	'\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION				)
class 				snake_case       ( datasets.Metric				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	str			):
									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 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	int = 16    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	List[str]=None			):
									if device is not None:
													assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
													if device == "gpu":
																	UpperCAmelCase__             =							'cuda'
									else:
													UpperCAmelCase__             =							'cuda' if torch.cuda.is_available() else 'cpu'

									UpperCAmelCase__             =							AutoModelForCausalLM.from_pretrained(lowerCamelCase__			)
									UpperCAmelCase__             =							model.to(lowerCamelCase__			)

									UpperCAmelCase__             =							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:
													UpperCAmelCase__             =							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"
													UpperCAmelCase__             =							model.config.max_length - 1
									else:
													UpperCAmelCase__             =							model.config.max_length

									UpperCAmelCase__             =							tokenizer(
									    lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__    ,max_length=lowerCamelCase__    ,return_tensors='pt'    ,return_attention_mask=lowerCamelCase__    ,).to(lowerCamelCase__			)

									UpperCAmelCase__             =							encodings['input_ids']
									UpperCAmelCase__             =							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."

									UpperCAmelCase__             =							[]
									UpperCAmelCase__             =							CrossEntropyLoss(reduction='none'			)

									for start_index in logging.tqdm(range(0    ,len(lowerCamelCase__			)    ,lowerCamelCase__			)			):
													UpperCAmelCase__             =							min(start_index + batch_size    ,len(lowerCamelCase__			)			)
													UpperCAmelCase__             =							encoded_texts[start_index:end_index]
													UpperCAmelCase__             =							attn_masks[start_index:end_index]

													if add_start_token:
																	UpperCAmelCase__             =							torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0			)			).to(lowerCamelCase__			)
																	UpperCAmelCase__             =							torch.cat([bos_tokens_tensor, encoded_batch]    ,dim=1			)
																	UpperCAmelCase__             =							torch.cat(
																	    [torch.ones(bos_tokens_tensor.size()    ,dtype=torch.intaa			).to(lowerCamelCase__			), attn_mask]    ,dim=1			)

													UpperCAmelCase__             =							encoded_batch

													with torch.no_grad():
																	UpperCAmelCase__             =							model(lowerCamelCase__    ,attention_mask=lowerCamelCase__			).logits

													UpperCAmelCase__             =							out_logits[..., :-1, :].contiguous()
													UpperCAmelCase__             =							labels[..., 1:].contiguous()
													UpperCAmelCase__             =							attn_mask[..., 1:].contiguous()

													UpperCAmelCase__             =							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__			)}



