

'''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


_lowerCamelCase      					=     logging.getLogger(__name__)
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	Any        ="summarization"
			__A  :	Dict        =["loss"]
			__A  :	Dict        =ROUGE_KEYS
			__A  :	Union[str, Any]        ="rouge2"






			def __init__(  self			,_snake_case			,**_snake_case     ):
				if hparams.sortish_sampler and hparams.gpus > 1:
					UpperCAmelCase_     :      str						=  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__(_snake_case			,num_labels=_snake_case			,mode=self.mode			,**_snake_case     )
				use_task_specific_params(self.model			,"summarization"     )
				save_git_info(self.hparams.output_dir     )
				UpperCAmelCase_     :      List[Any]						=  Path(self.output_dir     ) / "metrics.json"
				UpperCAmelCase_     :      Union[str, Any]						=  Path(self.output_dir     ) / "hparams.pkl"
				pickle_save(self.hparams			,self.hparams_save_path     )
				UpperCAmelCase_     :      List[Any]						=  0
				UpperCAmelCase_     :      int						=  defaultdict(_snake_case     )
				UpperCAmelCase_     :      str						=  self.config.model_type
				UpperCAmelCase_     :      int						=  self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

				UpperCAmelCase_     :      int						=  {
				    "train": self.hparams.max_target_length,
				    "val": self.hparams.val_max_target_length,
				    "test": self.hparams.test_max_target_length,
				}
				assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
				assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
				if self.hparams.freeze_embeds:
					freeze_embeds(self.model     )
				if self.hparams.freeze_encoder:
					freeze_params(self.model.get_encoder()     )
					assert_all_frozen(self.model.get_encoder()     )

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






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  {
				    k: self.tokenizer.batch_decode(v.tolist()     ) if "mask" not in k else v.shape for k, v in batch.items()
				}
				save_json(_snake_case			,Path(self.output_dir     ) / "text_batch.json"     )
				save_json({k: v.tolist() for k, v in batch.items()}			,Path(self.output_dir     ) / "tok_batch.json"     )

				UpperCAmelCase_     :      Optional[Any]						=  True
				return readable_batch






			def 		UpperCamelCase__							(  self			,_snake_case			,**_snake_case     ):
				return self.model(_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      List[Any]						=  self.tokenizer.batch_decode(
				    _snake_case			,skip_special_tokens=_snake_case			,clean_up_tokenization_spaces=_snake_case     )
				return lmap(str.strip			,_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      List[Any]						=  self.tokenizer.pad_token_id
				UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  batch["input_ids"], batch["attention_mask"]
				UpperCAmelCase_     :      Optional[Any]						=  batch["labels"]
				if isinstance(self.model			,_snake_case     ):
					UpperCAmelCase_     :      str						=  self.model._shift_right(_snake_case     )
				else:
					UpperCAmelCase_     :      int						=  shift_tokens_right(_snake_case			,_snake_case     )
				if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
					UpperCAmelCase_     :      Tuple						=  decoder_input_ids
					self.save_readable_batch(_snake_case     )

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

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






			@property
			def 		UpperCamelCase__							(  self     ):
				return self.tokenizer.pad_token_id






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  self._step(_snake_case     )

				UpperCAmelCase_     :      Any						=  dict(zip(self.loss_names			,_snake_case     )     )
				# tokens per batch
				UpperCAmelCase_     :      Optional[int]						=  batch["input_ids"].ne(self.pad     ).sum() + batch["labels"].ne(self.pad     ).sum()
				UpperCAmelCase_     :      Tuple						=  batch["input_ids"].shape[0]
				UpperCAmelCase_     :      List[Any]						=  batch["input_ids"].eq(self.pad     ).sum()
				UpperCAmelCase_     :      List[Any]						=  batch["input_ids"].eq(self.pad     ).float().mean()
				# TODO(SS): make a wandb summary metric for this
				return {"loss": loss_tensors[0], "log": logs}






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				return self._generative_step(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case="val"     ):
				self.step_count += 1
				UpperCAmelCase_     :      Optional[int]						=  {k: torch.stack([x[k] for x in outputs]     ).mean() for k in self.loss_names}
				UpperCAmelCase_     :      Tuple						=  losses["loss"]
				UpperCAmelCase_     :      int						=  {
				    k: np.array([x[k] for x in outputs]     ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
				}
				UpperCAmelCase_     :      List[Any]						=  (
				    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
				)
				UpperCAmelCase_     :      torch.FloatTensor						=  torch.tensor(_snake_case     ).type_as(_snake_case     )
				generative_metrics.update({k: v.item() for k, v in losses.items()}     )
				losses.update(_snake_case     )
				UpperCAmelCase_     :      str						=  {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
				UpperCAmelCase_     :      Dict						=  self.step_count
				self.metrics[prefix].append(_snake_case     )  # callback writes this to self.metrics_save_path
				UpperCAmelCase_     :      Optional[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			,_snake_case			,_snake_case     ):
				return calculate_rouge(_snake_case			,_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      int						=  time.time()

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				return self._generative_step(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return self.validation_epoch_end(_snake_case			,prefix="test"     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      Any						=  self.n_obs[type_path]
				UpperCAmelCase_     :      List[str]						=  self.target_lens[type_path]
				UpperCAmelCase_     :      Union[str, Any]						=  self.dataset_class(
				    self.tokenizer			,type_path=_snake_case			,n_obs=_snake_case			,max_target_length=_snake_case			,**self.dataset_kwargs			,)
				return dataset






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = False     ):
				UpperCAmelCase_     :      Any						=  self.get_dataset(_snake_case     )

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

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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  self.get_dataloader("train"			,batch_size=self.hparams.train_batch_size			,shuffle=_snake_case     )
				return dataloader






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






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






			@staticmethod
			def 		UpperCamelCase__							(  _snake_case			,_snake_case     ):
				BaseTransformer.add_model_specific_args(_snake_case			,_snake_case     )
				add_generic_args(_snake_case			,_snake_case     )
				parser.add_argument(
				    "--max_source_length"			,default=10_24			,type=_snake_case			,help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )			,)
				parser.add_argument(
				    "--max_target_length"			,default=56			,type=_snake_case			,help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )			,)
				parser.add_argument(
				    "--val_max_target_length"			,default=1_42			,type=_snake_case			,help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )			,)
				parser.add_argument(
				    "--test_max_target_length"			,default=1_42			,type=_snake_case			,help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )			,)
				parser.add_argument("--freeze_encoder"			,action="store_true"     )
				parser.add_argument("--freeze_embeds"			,action="store_true"     )
				parser.add_argument("--sortish_sampler"			,action="store_true"			,default=_snake_case     )
				parser.add_argument("--overwrite_output_dir"			,action="store_true"			,default=_snake_case     )
				parser.add_argument("--max_tokens_per_batch"			,type=_snake_case			,default=_snake_case     )
				parser.add_argument("--logger_name"			,type=_snake_case			,choices=["default", "wandb", "wandb_shared"]			,default="default"     )
				parser.add_argument("--n_train"			,type=_snake_case			,default=-1			,required=_snake_case			,help="# examples. -1 means use all."     )
				parser.add_argument("--n_val"			,type=_snake_case			,default=5_00			,required=_snake_case			,help="# examples. -1 means use all."     )
				parser.add_argument("--n_test"			,type=_snake_case			,default=-1			,required=_snake_case			,help="# examples. -1 means use all."     )
				parser.add_argument(
				    "--task"			,type=_snake_case			,default="summarization"			,required=_snake_case			,help="# examples. -1 means use all."     )
				parser.add_argument("--label_smoothing"			,type=_snake_case			,default=0.0			,required=_snake_case     )
				parser.add_argument("--src_lang"			,type=_snake_case			,default=""			,required=_snake_case     )
				parser.add_argument("--tgt_lang"			,type=_snake_case			,default=""			,required=_snake_case     )
				parser.add_argument("--eval_beams"			,type=_snake_case			,default=_snake_case			,required=_snake_case     )
				parser.add_argument(
				    "--val_metric"			,type=_snake_case			,default=_snake_case			,required=_snake_case			,choices=["bleu", "rouge2", "loss", None]     )
				parser.add_argument("--eval_max_gen_length"			,type=_snake_case			,default=_snake_case			,help="never generate more than n tokens"     )
				parser.add_argument("--save_top_k"			,type=_snake_case			,default=1			,required=_snake_case			,help="How many checkpoints to save"     )
				parser.add_argument(
				    "--early_stopping_patience"			,type=_snake_case			,default=-1			,required=_snake_case			,help=(
				        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
				        " val_check_interval will effect it."
				    )			,)
				return parser




class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	Any        ="translation"
			__A  :	Optional[int]        =["loss"]
			__A  :	str        =["bleu"]
			__A  :	int        ="bleu"






			def __init__(  self			,_snake_case			,**_snake_case     ):
				super().__init__(_snake_case			,**_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  hparams.src_lang
				UpperCAmelCase_     :      Optional[Any]						=  hparams.tgt_lang






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				return calculate_bleu(_snake_case			,_snake_case     )




def  a__       (    _SCREAMING_SNAKE_CASE   :       Tuple	,				_SCREAMING_SNAKE_CASE   :       int=None				)     ->					SummarizationModule:


	"""simple docstring"""





	Path(args.output_dir				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
	check_output_dir(_SCREAMING_SNAKE_CASE	,				expected_items=3				)

	if model is None:
		if "summarization" in args.task:
			UpperCAmelCase_     :      SummarizationModule						=  SummarizationModule(_SCREAMING_SNAKE_CASE				)
		else:
			UpperCAmelCase_     :      SummarizationModule						=  TranslationModule(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      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"				)
	):
		UpperCAmelCase_     :      Tuple						=  True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		UpperCAmelCase_     :      str						=  os.environ.get("WANDB_PROJECT"	,				_SCREAMING_SNAKE_CASE				)
		UpperCAmelCase_     :      str						=  WandbLogger(name=model.output_dir.name	,				project=_SCREAMING_SNAKE_CASE				)

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

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

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

	UpperCAmelCase_     :      List[Any]						=  args.val_metric == "loss"
	UpperCAmelCase_     :      pl.Trainer						=  generic_train(
	    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				logging_callback=SeqaSeqLoggingCallback()	,				checkpoint_callback=get_checkpoint_callback(
	        args.output_dir	,				model.val_metric	,				args.save_top_k	,				_SCREAMING_SNAKE_CASE				)	,				early_stopping_callback=_SCREAMING_SNAKE_CASE	,				logger=_SCREAMING_SNAKE_CASE	,				)
	pickle_save(model.hparams	,				model.output_dir / "hparams.pkl"				)
	if not args.do_predict:
		return model

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

					_lowerCamelCase      					=     parser.parse_args()

					main(args)











from typing import Union

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


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase_						)
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      *__lowerCamelCase							:     Optional[int]					,      **__lowerCamelCase							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			super().__init__(*__lowerCamelCase					,      **__lowerCamelCase   )
			self.check_model_type(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     List[Any]=None					,      __lowerCamelCase							:     Any=None					,      **__lowerCamelCase							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     {}, {}
			if padding is not None:
					__lowercase											=     padding
			if truncation is not None:
					__lowercase											=     truncation
			if top_k is not None:
					__lowercase											=     top_k
			return preprocess_params, {}, postprocess_params




	def __call__(						self							:     str					,      __lowerCamelCase							:     Union["Image.Image", str]					,      __lowerCamelCase							:     str = None					,      **__lowerCamelCase							:     Dict   )       -> Tuple:

			'''simple docstring'''




			if isinstance(__lowerCamelCase					,      (Image.Image, str)   ) and isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					__lowercase											=     {'image': image, 'question': question}
			else:
					__lowercase											=     image
			__lowercase											=     super().__call__(__lowerCamelCase					,      **__lowerCamelCase   )
			return results




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Optional[Any]=False   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     load_image(inputs['image']   )
			__lowercase											=     self.tokenizer(
			    inputs['question']					,      return_tensors=self.framework					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase   )
			__lowercase											=     self.image_processor(images=__lowerCamelCase					,      return_tensors=self.framework   )
			model_inputs.update(__lowerCamelCase   )
			return model_inputs




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.model(**__lowerCamelCase   )
			return model_outputs






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     int=5   )       -> Tuple:

			'''simple docstring'''




			if top_k > self.model.config.num_labels:
					__lowercase											=     self.model.config.num_labels

			if self.framework == "pt":
					__lowercase											=     model_outputs.logits.sigmoid()[0]
					__lowercase						,      __lowercase											=     probs.topk(__lowerCamelCase   )
			else:
					raise ValueError(F"Unsupported framework: {self.framework}"   )

			__lowercase											=     scores.tolist()
			__lowercase											=     ids.tolist()
			return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCamelCase					,      __lowerCamelCase   )]




