



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


__snake_case						:Tuple						=					logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__snake_case						:Optional[int]						=					{
    '''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
__snake_case						:Dict						=					{
    '''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
}
__snake_case						:Optional[Any]						=					sorted(arg_to_scheduler.keys())
__snake_case						:Tuple						=					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class    _A	(     pl.LightningModule   ):



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					argparse.Namespace					,		__SCREAMING_SNAKE_CASE							:					Any=None					,		__SCREAMING_SNAKE_CASE							:					List[Any]="base"					,		__SCREAMING_SNAKE_CASE							:					Dict=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=None					,		__SCREAMING_SNAKE_CASE							:					List[str]=None					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]					,		):


           '''simple docstring'''

           super().__init__()
           # TODO: move to self.save_hyperparameters()
           # self.save_hyperparameters()
           # can also expand arguments into trainer signature for easier reading

           self.save_hyperparameters(__SCREAMING_SNAKE_CASE)
           __a							=    0
           __a							=    Path(self.hparams.output_dir)
           __a							=    self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
               __a							=    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=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           else:
               __a							=    config

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

           if tokenizer is None:
               __a							=    AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,		cache_dir=__SCREAMING_SNAKE_CASE					,		)
           else:
               __a							=    tokenizer
           __a							=    MODEL_MODES[mode]
           if model is None:
               __a							=    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=__SCREAMING_SNAKE_CASE					,		)
           else:
               __a							=    model



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		*__SCREAMING_SNAKE_CASE							:					str					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    self.model
           __a							=    ['''bias''', '''LayerNorm.weight''']
           __a							=    [
               {
                   '''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:
               __a							=    Adafactor(
                   __SCREAMING_SNAKE_CASE					,		lr=self.hparams.learning_rate					,		scale_parameter=__SCREAMING_SNAKE_CASE					,		relative_step=__SCREAMING_SNAKE_CASE)

           else:
               __a							=    AdamW(
                   __SCREAMING_SNAKE_CASE					,		lr=self.hparams.learning_rate					,		eps=self.hparams.adam_epsilon)
           __a							=    optimizer

           __a							=    self.get_lr_scheduler()

           return [optimizer], [scheduler]



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           return self.validation_step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.validation_end(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    max(1					,		self.hparams.gpus)  # TODO: consider num_tpu_cores
           __a							=    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							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           if stage == "test":
               __a							=    len(self.test_dataloader().dataset)
           else:
               __a							=    self.get_dataloader('''train'''					,		self.hparams.train_batch_size					,		shuffle=__SCREAMING_SNAKE_CASE)
               __a							=    len(self.train_dataloader().dataset)



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					bool = False):


           '''simple docstring'''

           raise NotImplementedError('''You must implement this for your task''')



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           return self.train_loader



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return self.get_dataloader('''dev'''					,		self.hparams.eval_batch_size					,		shuffle=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           return self.get_dataloader('''test'''					,		self.hparams.eval_batch_size					,		shuffle=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return os.path.join(
               self.hparams.data_dir					,		'''cached_{}_{}_{}'''.format(
                   __SCREAMING_SNAKE_CASE					,		list(filter(__SCREAMING_SNAKE_CASE					,		self.hparams.model_name_or_path.split('''/'''))).pop()					,		str(self.hparams.max_seq_length)					,		)					,		)



       @pl.utilities.rank_zero_only
       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict[str, Any]):


           '''simple docstring'''

           __a							=    self.output_dir.joinpath('''best_tfmr''')
           __a							=    self.step_count
           self.model.save_pretrained(__SCREAMING_SNAKE_CASE)
           self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)





       @staticmethod
       def 		_lowerCamelCase      (		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

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


class    _A	(     pl.Callback   ):



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           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    _A	(     pl.Callback   ):



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                   print(__SCREAMING_SNAKE_CASE)


class    _A	(     pl.Callback   ):



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    trainer.lr_schedulers[0]['''scheduler''']
           __a							=    {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr())}
           pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					pl.Trainer					,		__SCREAMING_SNAKE_CASE							:					pl.LightningModule):


           '''simple docstring'''

           rank_zero_info('''***** Validation results *****''')
           __a							=    trainer.callback_metrics
           # Log results
           for key in sorted(__SCREAMING_SNAKE_CASE):
               if key not in ["log", "progress_bar"]:
                   rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE					,		str(metrics[key])))





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					pl.Trainer					,		__SCREAMING_SNAKE_CASE							:					pl.LightningModule):


           '''simple docstring'''

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





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        '''--output_dir'''       ,			default=str(Path(_UpperCAmelCase    ).parent / '''test_run''' / '''model_checkpoints'''    )       ,			type=_UpperCAmelCase       ,			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=_UpperCAmelCase       ,			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=_UpperCAmelCase    )
    parser.add_argument('''--max_grad_norm'''       ,			dest='''gradient_clip_val'''       ,			default=1.0       ,			type=_UpperCAmelCase       ,			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=_UpperCAmelCase       ,			default=1       ,			help='''Number of updates steps to accumulate before performing a backward/update pass.'''       ,			)
    parser.add_argument('''--seed'''       ,			type=_UpperCAmelCase       ,			default=42       ,			help='''random seed for initialization'''    )
    parser.add_argument(
        '''--data_dir'''       ,			default=str(Path(_UpperCAmelCase    ).parent / '''test_run''' / '''dummy-train-data'''    )       ,			type=_UpperCAmelCase       ,			help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''       ,			)





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

    # init model
    __a							=    Path(model.hparams.output_dir    )
    odir.mkdir(exist_ok=_UpperCAmelCase    )

    # add custom checkpoints
    if checkpoint_callback is None:
        __a							=    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(_UpperCAmelCase    )
    if logging_callback is None:
        __a							=    LoggingCallback()

    __a							=    {}

    if args.fpaa:
        __a							=    16

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

    __a							=    args.accumulate_grad_batches
    __a							=    None
    __a							=    '''auto'''

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

    if args.do_train:
        trainer.fit(_UpperCAmelCase    )

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










from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__snake_case						:Tuple						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)

           if self.framework == "tf":
               raise ValueError(F'The {self.__class__} is only available in PyTorch.')

           requires_backends(self					,		'''vision''')
           self.check_model_type(__SCREAMING_SNAKE_CASE)



       def __call__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, "Image.Image", List[Dict[str, Any]]]					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str]] = None					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		):


           '''simple docstring'''

           if "text_queries" in kwargs:
               __a							=    kwargs.pop('''text_queries''')

           if isinstance(__SCREAMING_SNAKE_CASE					,		(str, Image.Image)):
               __a							=    {'''image''': image, '''candidate_labels''': candidate_labels}
           else:
               __a							=    image
           __a							=    super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return results



       def 		_lowerCamelCase      (		self							:					str					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    {}
           if "threshold" in kwargs:
               __a							=    kwargs['''threshold''']
           if "top_k" in kwargs:
               __a							=    kwargs['''top_k''']
           return {}, {}, postprocess_params



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    load_image(inputs['''image'''])
           __a							=    inputs['''candidate_labels''']
           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               __a							=    candidate_labels.split(''',''')

           __a							=    torch.tensor([[image.height, image.width]]					,		dtype=torch.intaa)
           for i, candidate_label in enumerate(__SCREAMING_SNAKE_CASE):
               __a							=    self.tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
               __a							=    self.image_processor(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
               yield {
                   "is_last": i == len(__SCREAMING_SNAKE_CASE) - 1,
                   "target_size": target_size,
                   "candidate_label": candidate_label,
                   **text_inputs,
                   **image_features,
               }



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    model_inputs.pop('''target_size''')
           __a							=    model_inputs.pop('''candidate_label''')
           __a							=    model_inputs.pop('''is_last''')

           __a							=    self.model(**__SCREAMING_SNAKE_CASE)

           __a							=    {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
           return model_outputs



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.1					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None):


           '''simple docstring'''

           __a							=    []
           for model_output in model_outputs:
               __a							=    model_output['''candidate_label''']
               __a							=    BaseModelOutput(__SCREAMING_SNAKE_CASE)
               __a							=    self.image_processor.post_process_object_detection(
                   outputs=__SCREAMING_SNAKE_CASE					,		threshold=__SCREAMING_SNAKE_CASE					,		target_sizes=model_output['''target_size'''])[0]

               for index in outputs["scores"].nonzero():
                   __a							=    outputs['''scores'''][index].item()
                   __a							=    self._get_bounding_box(outputs['''boxes'''][index][0])

                   __a							=    {'''score''': score, '''label''': label, '''box''': box}
                   results.append(__SCREAMING_SNAKE_CASE)

           __a							=    sorted(__SCREAMING_SNAKE_CASE					,		key=lambda __SCREAMING_SNAKE_CASE: x["score"]					,		reverse=__SCREAMING_SNAKE_CASE)
           if top_k:
               __a							=    results[:top_k]

           return results





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					"torch.Tensor"):


           '''simple docstring'''

           if self.framework != "pt":
               raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.''')
           __a	,       __a	,       __a	,       __a							=    box.int().tolist()
           __a							=    {
               '''xmin''': xmin,
               '''ymin''': ymin,
               '''xmax''': xmax,
               '''ymax''': ymax,
           }
           return bbox





