"""simple docstring"""
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

lowercase__					       =	logging.get_logger(__name__)




@add_end_docstrings(__lowerCAmelCase				)
class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			**lowercase)	->			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    ,			lowercase    ,			**lowercase)	->			Dict:





        '''simple docstring'''





        return super().__call__(lowerCamelCase__    ,			**lowerCamelCase__)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			List[str]:





        '''simple docstring'''





        a__:     str										=	{}
        if "candidate_labels" in kwargs:
          a__:     Optional[Any]										=	kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
          a__:     Tuple										=	kwargs['hypothesis_template']

        return preprocess_params, {}, {}



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=None    ,			lowercase="This is a photo of {}.")	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	load_image(lowerCamelCase__)
        a__:     str										=	self.image_processor(images=[image]    ,			return_tensors=self.framework)
        a__:     List[str]										=	candidate_labels
        a__:     Optional[Any]										=	[hypothesis_template.format(lowerCamelCase__) for x in candidate_labels]
        a__:     Dict										=	self.tokenizer(lowerCamelCase__    ,			return_tensors=self.framework    ,			padding=lowerCamelCase__)
        a__:     Tuple										=	[text_inputs]
        return inputs



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     List[str]										=	model_inputs.pop('candidate_labels')
        a__:     Union[str, Any]										=	model_inputs.pop('text_inputs')
        if isinstance(text_inputs[0]    ,			lowerCamelCase__):
          a__:     List[str]										=	text_inputs[0]
        else:
          # Batching case.
          a__:     int										=	text_inputs[0][0]

        a__:     List[str]										=	self.model(**lowerCamelCase__    ,			**lowerCamelCase__)

        a__:     Union[str, Any]										=	{
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_image,
        }
        return model_outputs



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





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

        a__:     str										=	[
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCamelCase__    ,			lowerCamelCase__)    ,			key=lambda lowercase: -x[0])
        ]
        return result








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


