





from pathlib import Path

import fire
from tqdm import tqdm






def lowerCamelCase__   (							snake_case_     :  List[Any]="ro"	,				snake_case_     :  Optional[int]="en"	,				snake_case_     :  str="wmt16"	,				snake_case_     :  Union[str, Any]=None	)				->    None:
   try:
      import datasets
   except (ModuleNotFoundError, ImportError):
      raise ImportError('''run pip install datasets'''	)
   __snake_case								=       f"""{src_lang}-{tgt_lang}"""
   print(f"""Converting {dataset}-{pair}"""	)
   __snake_case								=       datasets.load_dataset(snake_case_	,				snake_case_	)
   if save_dir is None:
      __snake_case								=       f"""{dataset}-{pair}"""
   __snake_case								=       Path(snake_case_	)
   save_dir.mkdir(exist_ok=snake_case_	)

   for split in ds.keys():
      print(f"""Splitting {split} with {ds[split].num_rows} records"""	)

      # to save to val.source, val.target like summary datasets
      __snake_case								=       '''val''' if split == '''validation''' else split
      __snake_case								=       save_dir.joinpath(f"""{fn}.source"""	)
      __snake_case								=       save_dir.joinpath(f"""{fn}.target"""	)
      __snake_case								=       src_path.open('''w+'''	)
      __snake_case								=       tgt_path.open('''w+'''	)

      # reader is the bottleneck so writing one record at a time doesn't slow things down
      for x in tqdm(ds[split]	):
         __snake_case								=       x['''translation''']
         src_fp.write(ex[src_lang] + '''\n'''	)
         tgt_fp.write(ex[tgt_lang] + '''\n'''	)

   print(f"""Saved {dataset} dataset to {save_dir}"""	)


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








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, 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():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



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


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

