




"""simple docstring"""





import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowercase	      =    logging.getLogger(__name__)

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						random.seed(lowerCamelCase		)
						np.random.seed(lowerCamelCase		)
						torch.manual_seed(lowerCamelCase		)
						torch.cuda.manual_seed_all(lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						model.eval()
						snake_case__       :					Any										=					next(model.parameters()		).device
						snake_case__       :					Union[str, Any]										=					1
						snake_case__       :					Optional[int]										=					torch.zeros((eval_batch_size, context_len)      ,			dtype=torch.long      ,			device=lowerCamelCase		)
						snake_case__       :					Optional[Any]										=					DataLoader(lowerCamelCase      ,			shuffle=lowerCamelCase      ,			batch_size=lowerCamelCase		)
						snake_case__       :					List[Any]										=					torch.zeros(1      ,			device=lowerCamelCase		)
						snake_case__       :					Optional[Any]										=					0
						for batch in eval_dataloader:
												batch.to(lowerCamelCase		)
												# pad
												context.zero_()
												for i in range(lowerCamelCase		):
																		snake_case__       :					Dict										=					batch[i]
												snake_case__       :					Union[str, Any]										=					model(lowerCamelCase      ,			labels=lowerCamelCase		)
												eval_loss += outputs[0].sum().item()
												nb_eval_examples += batch.size(0		)
						snake_case__       :					int										=					eval_loss / nb_eval_examples
						snake_case__       :					List[str]										=					torch.exp(lowerCamelCase		)
						model.train()
						return perplexity

def 		lowerCAmelCase							( lowerCamelCase="gpt2"		):
						'''simple docstring'''


						snake_case__       :					str										=					GPTaLMHeadModel.from_pretrained(lowerCamelCase      ,			output_hidden_states=lowerCamelCase		)
						torch.save(model.state_dict()      ,			model_name + '''local.pt'''		)
						return model

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Dict										=					copy.deepcopy(lowerCamelCase		)
						model.to(lowerCamelCase		)

						snake_case__       :					Optional[Any]										=					['''bias''', '''LayerNorm.weight''']
						snake_case__       :					Dict										=					[
						    {
						        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
						        '''weight_decay''': 0.0,
						    },
						    {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)], '''weight_decay''': 0.0},
						]
						snake_case__       :					Optional[Any]										=					AdamW(lowerCamelCase      ,			lr=5e-5      ,			eps=1e-8		)
						snake_case__       :					Optional[int]										=					get_linear_schedule_with_warmup(lowerCamelCase      ,			0      ,			lowerCamelCase		)
						torch.cuda.empty_cache()
						return model, lm_optimizer, lm_scheduler

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Dict										=					np.array(lowerCamelCase		).mean()
						snake_case__       :					Dict										=					np.array(lowerCamelCase		).std()
						snake_case__       :					Optional[Any]										=					(real_perps - avg) / std
						snake_case__       :					List[Any]										=					list(zip(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)		)
						joblib.dump(lowerCamelCase      ,			lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase="dev.jbl"      ,			lowerCamelCase=recopy_gpta      ,			):
						'''simple docstring'''


						snake_case__       :					List[Any]										=					[]
						snake_case__       :					List[Any]										=					[]
						snake_case__       :					List[str]										=					[]

						# Initialize the transformer model
						snake_case__       :					Optional[Any]										=					copy.deepcopy(lowerCamelCase		)
						orig_model.to(device='''cpu'''		)
						torch.cuda.empty_cache()

						# Compute perplexity of initial transformer model for comparison
						model.train()
						snake_case__			,	snake_case__			,	snake_case__       :					Optional[Any]										=					recopy_model(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)

						for step in tqdm(range(lowerCamelCase		)		):
												snake_case__       :					List[str]										=					torch.zeros((1, context_len)      ,			dtype=torch.long      ,			device=lowerCamelCase		)
												snake_case__       :					Optional[Any]										=					random.choice(lowerCamelCase		)
												snake_case__       :					List[Any]										=					random.randint(0      ,			len(story[0]		) - context_len - 1		)
												snake_case__       :					List[str]										=					story[0][start : start + context_len]
												lm_optimizer.zero_grad()
												snake_case__       :					List[str]										=					model(lowerCamelCase      ,			labels=lowerCamelCase		)
												snake_case__       :					Dict										=					outputs[0]
												snake_case__       :					Union[str, Any]										=					compute_perplexity(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)
												model.train()
												lm_loss.backward()
												# Do LM backprop
												torch.nn.utils.clip_grad_norm_(model.parameters()      ,			3.0		)
												lm_optimizer.step()
												lm_scheduler.step()  # Update learning rate schedule

												# Compute perplexity after back-propagating on the selected context
												snake_case__       :					List[Any]										=					compute_perplexity(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)

												# Periodically save the stored (X, IG(X)) pairs
												if step % 1000 == 0 and step > 1:
																		intermittent_save(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)

												# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
												snake_case__			,	snake_case__			,	snake_case__       :					List[Any]										=					recopy_model(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)

												past_perps.append(past_perp.item()		)
												real_perps.append(orig_perp - real_perp.item()		)
												contexts.append(np.array(context.cpu()		)		)

						intermittent_save(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl"      ,			lowerCamelCase=100      ,			lowerCamelCase=1026      ,			lowerCamelCase=True		):
						'''simple docstring'''


						snake_case__       :					Tuple										=					joblib.load(lowerCamelCase		)
						print('''data loaded'''		)
						snake_case__       :					List[Any]										=					[]
						if trim:
												for i, example in enumerate(lowerCamelCase		):
																		if len(example[0]		) > min_len:
																								snake_case__       :					Optional[Any]										=					random.randint(0      ,			len(example[0]		) - context_len - 1		)
																								objective_set.append(example[0, start : start + context_len]		)
																		if len(lowerCamelCase		) >= number:
																								break
												snake_case__       :					List[str]										=					[]
												for j in range(i + 1      ,			len(lowerCamelCase		)		):
																		if len(data[j][0]		) > min_len:
																								train_data.append(data[j]		)
						else:
												snake_case__       :					int										=					data[0:number]
												snake_case__       :					str										=					data[number:]

						joblib.dump(lowerCamelCase      ,			'''objective_set.jbl'''		)
						print('''objective set saved'''		)
						return train_data, objective_set

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase=50      ,			lowerCamelCase="secondary_learner.pt"		):
						'''simple docstring'''


						snake_case__       :					str										=					torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)
						# We will use the first 512 pairs from our dataset as a test set for
						# our secondary learner and the rest to train
						snake_case__       :					Tuple										=					train_dataset[:512]
						snake_case__       :					Any										=					train_dataset[512:]
						snake_case__       :					List[Any]										=					DataLoader(lowerCamelCase      ,			shuffle=lowerCamelCase      ,			batch_size=lowerCamelCase		)
						snake_case__       :					str										=					DataLoader(lowerCamelCase      ,			shuffle=lowerCamelCase      ,			batch_size=lowerCamelCase		)

						# secondary learner model set up
						snake_case__       :					str										=					nn.MSELoss()
						snake_case__       :					Dict										=					nn.MSELoss(reduction='''sum'''		)
						secondary_learner.to(lowerCamelCase		)
						snake_case__       :					Optional[Any]										=					torch.optim.Adam(secondary_learner.parameters()      ,			lr=0.00_001		)
						secondary_learner.train()

						# TODO in original code this is written as number of actual batches seen
						# not number of items seen but other places it is number of items instead.
						# improve consistency! changed this to epochs for clarity
						snake_case__       :					str										=					float('''inf'''		)
						# Iterate through batches until we've used max_steps batches
						for epoch in range(int(lowerCamelCase		)		):
												snake_case__       :					int										=					0.0
												secondary_learner.train()
												for step, batch in enumerate(lowerCamelCase		):
																		snake_case__       :					Optional[int]										=					batch[0].to(lowerCamelCase		)
																		snake_case__       :					Union[str, Any]										=					batch[1].to(lowerCamelCase		)
																		snake_case__       :					Optional[int]										=					secondary_learner(lowerCamelCase		)
																		q_optimizer.zero_grad()
																		snake_case__       :					List[Any]										=					loss(lowerCamelCase      ,			real_q.float()		)
																		q_loss.backward()
																		q_optimizer.step()
																		tr_q_loss += q_loss.item()

																		# model trains fairly quickly so we won't wait for a full epoch
																		# eval is triggered at eval_freq and end of epochs
																		if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCamelCase		)):
																								snake_case__       :					List[str]										=					tr_q_loss / (step + 1)

																								secondary_learner.eval()
																								snake_case__       :					Optional[Any]										=					0.0
																								snake_case__       :					List[Any]										=					0.0
																								snake_case__       :					Optional[int]										=					[]
																								snake_case__       :					Dict										=					[]
																								# Compute performance of the secondary learner after this batch
																								for stepa, batcha in enumerate(lowerCamelCase		):
																														snake_case__       :					List[Any]										=					batcha[0].to(lowerCamelCase		)
																														snake_case__       :					List[Any]										=					batcha[1].to(lowerCamelCase		)
																														snake_case__       :					Optional[Any]										=					secondary_learner(lowerCamelCase		)
																														q_lossa += test_loss(lowerCamelCase      ,			lowerCamelCase		).item()
																														sum_qa += torch.sum(lowerCamelCase		).item()
																														for ei, i in enumerate(predicted_qa.cpu().detach().numpy()		):
																																				predicted.append(i.item()		)
																														for ei, i in enumerate(real_qa.cpu().detach().numpy()		):
																																				actual.append(i.item()		)

																								q_lossa /= len(lowerCamelCase		)
																								print(
																								    '''Epoch: '''      ,			lowerCamelCase      ,			'''step: '''      ,			lowerCamelCase      ,			'''Avg. q:'''      ,			sum_qa / len(lowerCamelCase		)      ,			'''Train Loss: '''      ,			lowerCamelCase      ,			'''Test Loss: '''      ,			lowerCamelCase      ,			)
																								if q_lossa < best_test_loss:
																														joblib.dump((predicted, actual)      ,			'''pred_vs_actual.jbl'''		)
																														torch.save(secondary_learner.state_dict()      ,			lowerCamelCase		)
																														snake_case__       :					List[str]										=					q_lossa

																		secondary_learner.train()
						return secondary_learner

class       __lowercase       (			nn.Module	):


				'''simple docstring'''


				def __init__(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Dict      ):
										super(SCREAMING_SNAKE_CASE_      ,self      ).__init__()
										snake_case__       :					Tuple										=					model.transformer.wte
										snake_case__       :					Dict										=					copy.deepcopy(model.transformer.wte.weight      )
										snake_case__       :					List[str]										=					nn.Convad(self.embeddings.weight.size(1      )      ,2_5_6      ,3      ,padding=1      )
										snake_case__       :					Optional[int]										=					nn.Sequential(nn.Linear(2_5_6      ,3_2      )      ,nn.Dropout(p=0.1      )      ,nn.Linear(3_2      ,3_2      )      ,nn.Linear(3_2      ,1      )      )

				def       _lowerCamelCase					(   self   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ):
										snake_case__       :					Optional[int]										=					torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE_      ).squeeze(1      ).transpose(1      ,2      )      )      ,2      )[0]
										snake_case__       :					str										=					self.fc(SCREAMING_SNAKE_CASE_      )
										return qs.squeeze(1      )





				@classmethod
				def       _lowerCamelCase					(   cls   :     str      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ):
										snake_case__       :					str										=					cls(SCREAMING_SNAKE_CASE_      )  # this calls __init__
										snake_case__       :					List[str]										=					torch.load(SCREAMING_SNAKE_CASE_      )
										secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE_      )
										snake_case__       :					List[str]										=					model.transformer.wte
										snake_case__       :					Any										=					copy.deepcopy(model.transformer.wte.weight      )
										return secondary_learner








"""simple docstring"""





from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


_lowercase	      =    logging.get_logger(__name__)

class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				def __init__(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Union["PreTrainedModel", nn.Module] = None      ,SCREAMING_SNAKE_CASE_   :     "TrainingArguments" = None      ,SCREAMING_SNAKE_CASE_   :     Optional["DataCollator"] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Dataset] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Union[Dataset, Dict[str, Dataset]]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional["PreTrainedTokenizerBase"] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[[], "PreTrainedModel"]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[["EvalPrediction"], Dict]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[List["TrainerCallback"]] = None      ,SCREAMING_SNAKE_CASE_   :     Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None      ,):
										super().__init__(
										    model=SCREAMING_SNAKE_CASE_      ,args=SCREAMING_SNAKE_CASE_      ,data_collator=SCREAMING_SNAKE_CASE_      ,train_dataset=SCREAMING_SNAKE_CASE_      ,eval_dataset=SCREAMING_SNAKE_CASE_      ,tokenizer=SCREAMING_SNAKE_CASE_      ,model_init=SCREAMING_SNAKE_CASE_      ,compute_metrics=SCREAMING_SNAKE_CASE_      ,callbacks=SCREAMING_SNAKE_CASE_      ,optimizers=SCREAMING_SNAKE_CASE_      ,preprocess_logits_for_metrics=SCREAMING_SNAKE_CASE_      ,)

										# Override self.model.generation_config if a GenerationConfig is specified in args.
										# Priority: args.generation_config > model.generation_config > default GenerationConfig.
										if self.args.generation_config is not None:
																snake_case__       :					List[Any]										=					self.load_generation_config(self.args.generation_config      )
																snake_case__       :					Optional[int]										=					gen_config

				@staticmethod
				def       _lowerCamelCase					(   SCREAMING_SNAKE_CASE_   :     Union[str, GenerationConfig]      ):

										# GenerationConfig provided, nothing to do
										if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ):
																return deepcopy(SCREAMING_SNAKE_CASE_      )

										# str or Path
										snake_case__       :					Optional[int]										=					Path(SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) else gen_config_arg
										snake_case__       :					Optional[Any]										=					None

										# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
										# This step is required in order to determine config_file_name
										if pretrained_model_name.is_file():
																snake_case__       :					List[Any]										=					pretrained_model_name.name
																snake_case__       :					Any										=					pretrained_model_name.parent
										# dir path
										elif pretrained_model_name.is_dir():
																pass
										# model id or URL
										else:
																snake_case__       :					Optional[Any]										=					gen_config_arg

										snake_case__       :					Optional[Any]										=					GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )
										return gen_config

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[Dataset] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,SCREAMING_SNAKE_CASE_   :     str = "eval"      ,**SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					List[str]										=					gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Optional[int]										=					self.args.generation_max_length
										snake_case__       :					Optional[Any]										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
										)
										snake_case__       :					Optional[Any]										=					gen_kwargs

										return super().evaluate(SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      ,metric_key_prefix=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Dataset      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,SCREAMING_SNAKE_CASE_   :     str = "test"      ,**SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					Union[str, Any]										=					gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Any										=					self.args.generation_max_length
										snake_case__       :					str										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
										)
										snake_case__       :					List[str]										=					gen_kwargs

										return super().predict(SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      ,metric_key_prefix=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     nn.Module      ,SCREAMING_SNAKE_CASE_   :     Dict[str, Union[torch.Tensor, Any]]      ,SCREAMING_SNAKE_CASE_   :     bool      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,**SCREAMING_SNAKE_CASE_   :     List[Any]      ,):

										if not self.args.predict_with_generate or prediction_loss_only:
																return super().prediction_step(
																    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,prediction_loss_only=SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      )

										snake_case__       :					List[Any]										=					'''labels''' in inputs
										snake_case__       :					Tuple										=					self._prepare_inputs(SCREAMING_SNAKE_CASE_      )

										# XXX: adapt synced_gpus for fairscale as well
										# Priority (handled in generate):
										# gen_kwargs > model.generation_config > default GenerationConfig()

										if len(SCREAMING_SNAKE_CASE_      ) == 0 and hasattr(self      ,'''_gen_kwargs'''      ):
																snake_case__       :					Any										=					self._gen_kwargs.copy()

										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Any										=					self.model.config.max_length
										snake_case__       :					int										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.model.config.num_beams
										)
										snake_case__       :					Optional[int]										=					True if is_deepspeed_zeroa_enabled() else False
										snake_case__       :					Any										=					(
										    gen_kwargs['''synced_gpus'''] if gen_kwargs.get('''synced_gpus'''      ) is not None else default_synced_gpus
										)

										# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
										# (otherwise, it would continue generating from the padded `decoder_input_ids`)
										if (
										    "labels" in inputs
										    and "decoder_input_ids" in inputs
										    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
										):
																snake_case__       :					List[str]										=					{k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
										snake_case__       :					List[str]										=					self.model.generate(**SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      )

										# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
										# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
										# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
										if self.model.generation_config._from_model_config:
																snake_case__       :					int										=					False

										# Retrieves GenerationConfig from model.generation_config
										snake_case__       :					Dict										=					self.model.generation_config
										# in case the batch is shorter than max length, the output should be padded
										if generated_tokens.shape[-1] < gen_config.max_length:
																snake_case__       :					Tuple										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_length      )
										elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
																snake_case__       :					Any										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_new_tokens + 1      )

										with torch.no_grad():
																if has_labels:
																						with self.compute_loss_context_manager():
																												snake_case__       :					Union[str, Any]										=					model(**SCREAMING_SNAKE_CASE_      )
																						if self.label_smoother is not None:
																												snake_case__       :					str										=					self.label_smoother(SCREAMING_SNAKE_CASE_      ,inputs['''labels''']      ).mean().detach()
																						else:
																												snake_case__       :					Optional[Any]										=					(outputs['''loss'''] if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) else outputs[0]).mean().detach()
																else:
																						snake_case__       :					Optional[Any]										=					None

										if self.args.prediction_loss_only:
																return loss, None, None

										if has_labels:
																snake_case__       :					Tuple										=					inputs['''labels''']
																if labels.shape[-1] < gen_config.max_length:
																						snake_case__       :					List[str]										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_length      )
																elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																						snake_case__       :					Optional[int]										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_new_tokens + 1      )
										else:
																snake_case__       :					List[str]										=					None

										return loss, generated_tokens, labels





				def       _lowerCamelCase					(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Dict      ):
										if self.tokenizer is not None and hasattr(self.tokenizer      ,'''pad_token_id'''      ):
																# If PAD token is not defined at least EOS token has to be defined
																snake_case__       :					List[str]										=					(
																    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
																)
										else:
																if self.model.config.pad_token_id is not None:
																						snake_case__       :					Any										=					self.model.config.pad_token_id
																else:
																						raise ValueError('''Pad_token_id must be set in the configuration of the model, in order to pad tensors'''      )

										snake_case__       :					str										=					pad_token_id * torch.ones(
										    (tensor.shape[0], max_length)      ,dtype=tensor.dtype      ,device=tensor.device      )
										snake_case__       :					str										=					tensor
										return padded_tensor


