

'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
      import torch


_lowerCAmelCase         =						logging.get_logger(__name__)






class   _SCREAMING_SNAKE_CASE   (   __SCREAMING_SNAKE_CASE     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					["pixel_values"]



 def __init__(   self		:   Union[str, Any]			,				a__		:   Tuple = True			,				a__		:   str = None			,				a__		:   List[Any] = PILImageResampling.BILINEAR			,				a__		:   Optional[Any] = True			,				a__		:   str = None			,				a__		:   Optional[int] = True			,				a__		:   Tuple = 1 / 255			,				a__		:   Optional[int] = True			,				a__		:   Optional[int] = None			,				a__		:   List[Any] = None			,				**a__		:   int			,				):
      super().__init__(**_snake_case      )
      __magic_name__						       =size if size is not None else {"shortest_edge": 256}
      __magic_name__						       =get_size_dict(_snake_case			,				default_to_square=_snake_case      )
      __magic_name__						       =crop_size if crop_size is not None else {"height": 224, "width": 224}
      __magic_name__						       =get_size_dict(_snake_case			,				param_name='''crop_size'''      )
      __magic_name__						       =do_resize
      __magic_name__						       =size
      __magic_name__						       =resample
      __magic_name__						       =do_center_crop
      __magic_name__						       =crop_size
      __magic_name__						       =do_rescale
      __magic_name__						       =rescale_factor
      __magic_name__						       =do_normalize
      __magic_name__						       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __magic_name__						       =image_std if image_std is not None else IMAGENET_STANDARD_STD



 def 			snake_case__  (   self		:   List[str]			,				a__		:   str			,				a__		:   Optional[Any]			,				a__		:   Optional[Any] = PILImageResampling.BICUBIC			,				a__		:   Union[str, Any] = None			,				**a__		:   Any			,				):
      __magic_name__						       =get_size_dict(_snake_case			,				default_to_square=_snake_case      )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''      )
      __magic_name__						       =get_resize_output_image_size(_snake_case			,				size=size['''shortest_edge''']			,				default_to_square=_snake_case      )
      return resize(_snake_case			,				size=_snake_case			,				resample=_snake_case			,				data_format=_snake_case			,				**_snake_case      )



 def 			snake_case__  (   self		:   Any			,				a__		:   str			,				a__		:   str			,				a__		:   Tuple = None			,				**a__		:   str			,				):
      __magic_name__						       =get_size_dict(_snake_case      )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''      )
      return center_crop(_snake_case			,				size=(size['''height'''], size['''width'''])			,				data_format=_snake_case			,				**_snake_case      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Dict			,				a__		:   Dict			,				a__		:   Dict = None			,				**a__		:   Any      ):
      return rescale(_snake_case			,				scale=_snake_case			,				data_format=_snake_case			,				**_snake_case      )



 def 			snake_case__  (   self		:   Any			,				a__		:   Optional[Any]			,				a__		:   List[str]			,				a__		:   Optional[int]			,				a__		:   Tuple = None			,				**a__		:   Optional[Any]			,				):
      return normalize(_snake_case			,				mean=_snake_case			,				std=_snake_case			,				data_format=_snake_case			,				**_snake_case      )



 def 			snake_case__  (   self		:   Any			,				a__		:   Tuple			,				a__		:   Optional[int] = None			,				a__		:   Dict = None			,				a__		:   Dict = None			,				a__		:   Tuple = None			,				a__		:   List[Any] = None			,				a__		:   Dict = None			,				a__		:   List[str] = None			,				a__		:   Any = None			,				a__		:   int = None			,				a__		:   Tuple = None			,				a__		:   int = None			,				a__		:   Any = ChannelDimension.FIRST			,				**a__		:   Optional[Any]			,				):
      __magic_name__						       =do_resize if do_resize is not None else self.do_resize
      __magic_name__						       =size if size is not None else self.size
      __magic_name__						       =get_size_dict(_snake_case			,				default_to_square=_snake_case      )
      __magic_name__						       =resample if resample is not None else self.resample
      __magic_name__						       =do_center_crop if do_center_crop is not None else self.do_center_crop
      __magic_name__						       =crop_size if crop_size is not None else self.crop_size
      __magic_name__						       =get_size_dict(_snake_case			,				param_name='''crop_size'''      )
      __magic_name__						       =do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__						       =rescale_factor if rescale_factor is not None else self.rescale_factor
      __magic_name__						       =do_normalize if do_normalize is not None else self.do_normalize
      __magic_name__						       =image_mean if image_mean is not None else self.image_mean
      __magic_name__						       =image_std if image_std is not None else self.image_std

      __magic_name__						       =make_list_of_images(_snake_case      )

      if not valid_images(_snake_case      ):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

      if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''      )

      if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

      if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

      # All transformations expect numpy arrays.
      __magic_name__						       =[to_numpy_array(_snake_case      ) for image in images]

      if do_resize:
           __magic_name__						       =[self.resize(image=_snake_case			,				size=_snake_case			,				resample=_snake_case      ) for image in images]

      if do_center_crop:
           __magic_name__						       =[self.center_crop(image=_snake_case			,				size=_snake_case      ) for image in images]

      if do_rescale:
           __magic_name__						       =[self.rescale(image=_snake_case			,				scale=_snake_case      ) for image in images]

      if do_normalize:
           __magic_name__						       =[self.normalize(image=_snake_case			,				mean=_snake_case			,				std=_snake_case      ) for image in images]

      __magic_name__						       =[to_channel_dimension_format(_snake_case			,				_snake_case      ) for image in images]

      __magic_name__						       ={"pixel_values": images}
      return BatchFeature(data=_snake_case			,				tensor_type=_snake_case      )



 def 			snake_case__  (   self		:   Any			,				a__		:   int			,				a__		:   Optional[int] = None      ):
      __magic_name__						       =outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(_snake_case      ) != len(_snake_case      ):
                raise ValueError(
                    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''      )

           if is_torch_tensor(_snake_case      ):
                __magic_name__						       =target_sizes.numpy()

           __magic_name__						       =[]

           for idx in range(len(_snake_case      )      ):
                __magic_name__						       =torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0      )			,				size=target_sizes[idx]			,				mode='''bilinear'''			,				align_corners=_snake_case      )
                __magic_name__						       =resized_logits[0].argmax(dim=0      )
                semantic_segmentation.append(_snake_case      )
      else:
           __magic_name__						       =logits.argmax(dim=1      )
           __magic_name__						       =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

      return semantic_segmentation









'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     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
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    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]			):
               __magic_name__						       =    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)






