


"""simple docstring"""






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		=       logging.get_logger(__name__)


@add_end_docstrings(a__				)
class     __lowerCamelCase  (					a__				):

     '''simple docstring'''


     def __init__(      self    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          super().__init__(**__UpperCAmelCase							)

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

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


     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							Tuple:
          if "text_queries" in kwargs:
               _a    	=				kwargs.pop('''text_queries'''							)

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


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							str:
          _a    	=				{}
          if "threshold" in kwargs:
               _a    	=				kwargs['''threshold''']
          if "top_k" in kwargs:
               _a    	=				kwargs['''top_k''']
          return {}, {}, postprocess_params


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Dict:
          _a    	=				load_image(inputs['''image''']							)
          _a    	=				inputs['''candidate_labels''']
          if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							):
               _a    	=				candidate_labels.split(''','''							)

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				model_inputs.pop('''target_size'''							)
          _a    	=				model_inputs.pop('''candidate_label'''							)
          _a    	=				model_inputs.pop('''is_last'''							)

          _a    	=				self.model(**__UpperCAmelCase							)

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=None							)       ->							Optional[int]:
          _a    	=				[]
          for model_output in model_outputs:
               _a    	=				model_output['''candidate_label''']
               _a    	=				BaseModelOutput(__UpperCAmelCase							)
               _a    	=				self.image_processor.post_process_object_detection(
                   outputs=__UpperCAmelCase    ,				threshold=__UpperCAmelCase    ,				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(__UpperCAmelCase							)

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

          return results





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Dict[str, int]:
          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



"""simple docstring"""






from pathlib import Path

import fire
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				Path(_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       )
     dest_dir.mkdir(exist_ok=_lowerCAmelCase       )
     for path in src_dir.iterdir():
          _a    	=				[x.rstrip() for x in list(path.open().readlines()       )][:n]
          _a    	=				dest_dir.joinpath(path.name       )
          print(_lowerCAmelCase       )
          dest_path.open('''w'''       ).write('''\n'''.join(_lowerCAmelCase       )       )


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