




'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Optional[Any]					,     _a			:							Optional[int]=1_0_2_4					,     _a			:							Dict=1_0_2_4					,     _a			:							str=False					,     **_a			:							Union[str, Any]  ):
    UpperCAmelCase     =	AutoTokenizer.from_pretrained(_a  )
    UpperCAmelCase     =	SeqaSeqDataset(_a					,     _a					,     _a					,     _a					,     type_path='''train'''					,     **_a  )
    UpperCAmelCase     =	tok.pad_token_id

    def get_lens(_a			:							Optional[Any]  ):
        UpperCAmelCase     =	tqdm(
            DataLoader(_a					,     batch_size=5_1_2					,     num_workers=8					,     shuffle=_a					,     collate_fn=ds.collate_fn  )					,     desc=str(ds.len_file  )					,     )
        UpperCAmelCase     =	[]
        for batch in dl:
            UpperCAmelCase     =	batch['''input_ids'''].ne(_a  ).sum(1  ).tolist()
            UpperCAmelCase     =	batch['''labels'''].ne(_a  ).sum(1  ).tolist()
            if consider_target:
                for src, tgt in zip(_a					,     _a  ):
                    max_lens.append(max(_a					,     _a  )  )
            else:
                max_lens.extend(_a  )
        return max_lens

    UpperCAmelCase     =	get_lens(_a  )
    UpperCAmelCase     =	SeqaSeqDataset(_a					,     _a					,     _a					,     _a					,     type_path='''val'''					,     **_a  )
    UpperCAmelCase     =	get_lens(_a  )
    pickle_save(_a					,     train_ds.len_file  )
    pickle_save(_a					,     val_ds.len_file  )


if __name__ == "__main__":
   fire.Fire(save_len_file)






'''simple docstring'''







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

A									=logging.get_logger(__name__)


@add_end_docstrings(__a				)
class     _a						(			__a				):
       def __init__(			self		:			Optional[int]     ,       *lowercase		:			Any     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           super().__init__(*lowercase     ,       **lowercase       )
           self.check_model_type(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str=None     ,       lowercase		:			List[str]=None     ,       lowercase		:			List[str]=None     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	{}, {}
           if padding is not None:
               UpperCAmelCase     =	padding
           if truncation is not None:
               UpperCAmelCase     =	truncation
           if top_k is not None:
               UpperCAmelCase     =	top_k
           return preprocess_params, {}, postprocess_params
       def __call__(			self		:			Optional[Any]     ,       lowercase		:			Union["Image.Image", str]     ,       lowercase		:			str = None     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       (Image.Image, str)       ) and isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	{'''image''': image, '''question''': question}
           else:
               UpperCAmelCase     =	image
           UpperCAmelCase     =	super().__call__(lowercase     ,       **lowercase       )
           return results
       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(inputs['''image''']       )
           UpperCAmelCase     =	self.tokenizer(
               inputs['''question''']     ,       return_tensors=self.framework     ,       padding=lowercase     ,       truncation=lowercase       )
           UpperCAmelCase     =	self.image_processor(images=lowercase     ,       return_tensors=self.framework       )
           model_inputs.update(lowercase       )
           return model_inputs
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model(**lowercase       )
           return model_outputs







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=5       ):



           '''simple docstring'''

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

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

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




