



'''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 	__magic_name__						(    __UpperCamelCase   ):
							lowerCAmelCase       :    Union[str, Any]   				=					'summarization'
							lowerCAmelCase       :    str   				=					['loss']
							lowerCAmelCase       :    List[str]   				=					ROUGE_KEYS
							lowerCAmelCase       :    Optional[int]   				=					'rouge2'







							def __init__(							self					:       Tuple	,_UpperCAmelCase					:       Dict	,**_UpperCAmelCase					:       int		):
									if hparams.sortish_sampler and hparams.gpus > 1:
											_a     :      Optional[int]        =							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		)
									_a     :      Dict        =							Path(self.output_dir		) / 'metrics.json'
									_a     :      int        =							Path(self.output_dir		) / 'hparams.pkl'
									pickle_save(self.hparams	,self.hparams_save_path		)
									_a     :      Optional[int]        =							0
									_a     :      Optional[Any]        =							defaultdict(lowerCAmelCase__		)
									_a     :      Union[str, Any]        =							self.config.model_type
									_a     :      List[str]        =							self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

									_a     :      Optional[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()		)

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







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Union[str, Any]		):
									_a     :      List[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'		)

									_a     :      List[Any]        =							True
									return readable_batch







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       int		):
									return self.model(lowerCAmelCase__	,**lowerCAmelCase__		)







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      str        =							self.tokenizer.batch_decode(
									    lowerCAmelCase__	,skip_special_tokens=lowerCAmelCase__	,clean_up_tokenization_spaces=lowerCAmelCase__		)
									return lmap(str.strip	,lowerCAmelCase__		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Union[str, Any]        =							self.tokenizer.pad_token_id
									_a     :      Tuple        =							batch['input_ids'], batch['attention_mask']
									_a     :      int        =							batch['labels']
									if isinstance(self.model	,lowerCAmelCase__		):
											_a     :      Tuple        =							self.model._shift_right(lowerCAmelCase__		)
									else:
											_a     :      str        =							shift_tokens_right(lowerCAmelCase__	,lowerCAmelCase__		)
									if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
											_a     :      Dict        =							decoder_input_ids
											self.save_readable_batch(lowerCAmelCase__		)

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

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







							@property
							def 							__lowercase			(							self					:       Optional[Any]		):
									return self.tokenizer.pad_token_id







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       List[str]		):
									_a     :      Dict        =							self._step(lowerCAmelCase__		)

									_a     :      Optional[Any]        =							dict(zip(self.loss_names	,lowerCAmelCase__		)		)
									# tokens per batch
									_a     :      Optional[Any]        =							batch['input_ids'].ne(self.pad		).sum() + batch['labels'].ne(self.pad		).sum()
									_a     :      int        =							batch['input_ids'].shape[0]
									_a     :      int        =							batch['input_ids'].eq(self.pad		).sum()
									_a     :      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 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       str		):
									return self._generative_step(lowerCAmelCase__		)







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







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       List[str]		):
									_a     :      Dict        =							time.time()

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







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[Any]		):
									return self._generative_step(lowerCAmelCase__		)







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[Any]		):
									return self.validation_epoch_end(lowerCAmelCase__	,prefix='test'		)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Any		):
									_a     :      List[str]        =							self.n_obs[type_path]
									_a     :      List[str]        =							self.target_lens[type_path]
									_a     :      int        =							self.dataset_class(
									    self.tokenizer	,type_path=lowerCAmelCase__	,n_obs=lowerCAmelCase__	,max_target_length=lowerCAmelCase__	,**self.dataset_kwargs	,)
									return dataset







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Any = False		):
									_a     :      str        =							self.get_dataset(lowerCAmelCase__		)

									if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
											_a     :      Optional[Any]        =							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":
											_a     :      List[str]        =							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 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Optional[int]        =							self.get_dataloader('train'	,batch_size=self.hparams.train_batch_size	,shuffle=lowerCAmelCase__		)
									return dataloader







							def 							__lowercase			(							self					:       int		):
									return self.get_dataloader('val'	,batch_size=self.hparams.eval_batch_size		)







							def 							__lowercase			(							self					:       int		):
									return self.get_dataloader('test'	,batch_size=self.hparams.eval_batch_size		)







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Dict		):
									BaseTransformer.add_model_specific_args(lowerCAmelCase__	,lowerCAmelCase__		)
									add_generic_args(lowerCAmelCase__	,lowerCAmelCase__		)
									parser.add_argument(
									    '--max_source_length'	,default=1024	,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=56	,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=142	,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=142	,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=500	,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 	__magic_name__						(    __UpperCamelCase   ):
							lowerCAmelCase       :    Any   				=					'translation'
							lowerCAmelCase       :    str   				=					['loss']
							lowerCAmelCase       :    Optional[Any]   				=					['bleu']
							lowerCAmelCase       :    Optional[int]   				=					'bleu'







							def __init__(							self					:       str	,_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       Union[str, Any]		):
									super().__init__(lowerCAmelCase__	,**lowerCAmelCase__		)
									_a     :      Dict        =							hparams.src_lang
									_a     :      List[Any]        =							hparams.tgt_lang







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Optional[Any]		):
									return calculate_bleu(lowerCAmelCase__	,lowerCAmelCase__		)



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=None  )			->      SummarizationModule:
		Path(args.output_dir  ).mkdir(exist_ok=lowerCAmelCase_  )
		check_output_dir(lowerCAmelCase_						,			expected_items=3  )

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

				_a     :      Union[str, Any]        =							os.environ.get('WANDB_PROJECT'						,			lowerCAmelCase_  )
				_a     :      Tuple        =							WandbLogger(name=model.output_dir.name						,			project=lowerCAmelCase_  )

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

				_a     :      Union[str, Any]        =							WandbLogger(name=model.output_dir.name						,			project=f"""hf_{dataset}"""  )

		if args.early_stopping_patience >= 0:
				_a     :      int        =							get_early_stopping_callback(model.val_metric						,			args.early_stopping_patience  )
		else:
				_a     :      int        =							False

		_a     :      int        =							args.val_metric == 'loss'
		_a     :      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

		_a     :      Union[str, Any]        =							''
		_a     :      Union[str, Any]        =							sorted(glob.glob(os.path.join(args.output_dir						,			'*.ckpt'  )						,			recursive=lowerCAmelCase_  )  )
		if checkpoints:
				_a     :      Optional[Any]        =							checkpoints[-1]
				_a     :      Any        =							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 collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

__lowerCAmelCase		       =							logging.get_logger(__name__)





@add_end_docstrings(__UpperCamelCase      )
class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, **lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__ )

        requires_backends(self				, 'vision' )
        self.check_model_type(
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
 def __call__(   self				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        return super().__call__(lowerCAmelCase__				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   int  			=   {}
        if "candidate_labels" in kwargs:
               lowercase__:   Dict  			=   kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
               lowercase__:   List[Any]  			=   kwargs['hypothesis_template']

        return preprocess_params, {}, {}
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__=None				, lowerCAmelCase__="This is a photo of {}." )					->				int:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   load_image(lowerCAmelCase__ )
        lowercase__:   Dict  			=   self.image_processor(images=[image]				, return_tensors=self.framework )
        lowercase__:   Tuple  			=   candidate_labels
        lowercase__:   List[str]  			=   [hypothesis_template.format(lowerCAmelCase__ ) for x in candidate_labels]
        lowercase__:   Optional[Any]  			=   self.tokenizer(lowerCAmelCase__				, return_tensors=self.framework				, padding=lowerCAmelCase__ )
        lowercase__:   str  			=   [text_inputs]
        return inputs
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   str  			=   model_inputs.pop('candidate_labels' )
        lowercase__:   List[str]  			=   model_inputs.pop('text_inputs' )
        if isinstance(text_inputs[0]				, lowerCAmelCase__ ):
               lowercase__:   Any  			=   text_inputs[0]
        else:
               # Batching case.
               lowercase__:   Optional[int]  			=   text_inputs[0][0]

        lowercase__:   Tuple  			=   self.model(**lowerCAmelCase__				, **lowerCAmelCase__ )

        lowercase__:   Any  			=   {
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_image,
        }
        return model_outputs
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   model_outputs.pop('candidate_labels' )
        lowercase__:   Dict  			=   model_outputs['logits'][0]
        if self.framework == "pt":
               lowercase__:   Optional[int]  			=   logits.softmax(dim=-1 ).squeeze(-1 )
               lowercase__:   Dict  			=   probs.tolist()
               if not isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
                      lowercase__:   Dict  			=   [scores]
        elif self.framework == "tf":
               lowercase__:   Optional[int]  			=   stable_softmax(lowerCAmelCase__				, axis=-1 )
               lowercase__:   Union[str, Any]  			=   probs.numpy().tolist()
        else:
               raise ValueError(F'Unsupported framework: {self.framework}' )

        lowercase__:   List[Any]  			=   [
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCAmelCase__				, lowerCAmelCase__ )				, key=lambda lowerCAmelCase__ : -x[0] )
        ]
        return result


