

"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import 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, logging


__UpperCAmelCase       = logging.get_logger(__name__)







class    __lowercase	(			__a    ):
  snake_case_ 				=				["""pixel_values"""]

  def __init__(     self						:	str    ,A						:	Optional[Any] = True    ,A						:	str = None    ,A						:	Union[str, Any] = PILImageResampling.BILINEAR    ,A						:	Optional[int] = True    ,A						:	Optional[Any] = 1 / 255    ,A						:	Optional[int] = True    ,A						:	Optional[int] = None    ,A						:	Optional[Any] = None    ,**A						:	Any    ,):






   '''simple docstring'''
   super().__init__(**lowerCAmelCase__			)
   UpperCAmelCase__    :     Any          =    size if size is not None else {"""height""": 224, """width""": 224}
   UpperCAmelCase__    :     Optional[int]          =    get_size_dict(lowerCAmelCase__			)
   UpperCAmelCase__    :     Dict          =    do_resize
   UpperCAmelCase__    :     int          =    do_rescale
   UpperCAmelCase__    :     int          =    do_normalize
   UpperCAmelCase__    :     Optional[int]          =    size
   UpperCAmelCase__    :     Dict          =    resample
   UpperCAmelCase__    :     List[Any]          =    rescale_factor
   UpperCAmelCase__    :     List[str]          =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase__    :     int          =    image_std if image_std is not None else IMAGENET_STANDARD_STD

  def       __lowercase				(     self						:	str    ,A						:	Optional[int]    ,A						:	Union[str, Any]    ,A						:	List[str] = PILImageResampling.BILINEAR    ,A						:	Optional[Any] = None    ,**A						:	List[str]    ,):






   '''simple docstring'''
   UpperCAmelCase__    :     Union[str, Any]          =    get_size_dict(lowerCAmelCase__			)
   if "height" not in size or "width" not in size:
    raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"			)
   return resize(
       lowerCAmelCase__    ,size=(size["""height"""], size["""width"""])    ,resample=lowerCAmelCase__    ,data_format=lowerCAmelCase__    ,**lowerCAmelCase__			)

  def       __lowercase				(     self						:	str    ,A						:	Any    ,A						:	Any    ,A						:	Union[str, Any] = None    ,**A						:	int			):






   '''simple docstring'''
   return rescale(lowerCAmelCase__    ,scale=lowerCAmelCase__    ,data_format=lowerCAmelCase__    ,**lowerCAmelCase__			)

  def       __lowercase				(     self						:	Any    ,A						:	List[str]    ,A						:	Any    ,A						:	Tuple    ,A						:	List[Any] = None    ,**A						:	List[Any]    ,):






   '''simple docstring'''
   return normalize(lowerCAmelCase__    ,mean=lowerCAmelCase__    ,std=lowerCAmelCase__    ,data_format=lowerCAmelCase__    ,**lowerCAmelCase__			)




  def       __lowercase				(     self						:	int    ,A						:	Tuple    ,A						:	Tuple = None    ,A						:	List[Any] = None    ,A						:	Any = None    ,A						:	str = None    ,A						:	List[Any] = None    ,A						:	List[str] = None    ,A						:	Dict = None    ,A						:	str = None    ,A						:	Union[str, Any] = None    ,A						:	Optional[Any] = ChannelDimension.FIRST    ,**A						:	str    ,):






   '''simple docstring'''
   UpperCAmelCase__    :     Dict          =    do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__    :     int          =    do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__    :     List[Any]          =    do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase__    :     Optional[int]          =    resample if resample is not None else self.resample
   UpperCAmelCase__    :     Union[str, Any]          =    rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__    :     Dict          =    image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase__    :     Any          =    image_std if image_std is not None else self.image_std

   UpperCAmelCase__    :     Any          =    size if size is not None else self.size
   UpperCAmelCase__    :     int          =    get_size_dict(lowerCAmelCase__			)

   UpperCAmelCase__    :     List[Any]          =    make_list_of_images(lowerCAmelCase__			)

   if not valid_images(lowerCAmelCase__			):
    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_rescale and rescale_factor is None:
    raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

   # All transformations expect numpy arrays.
   UpperCAmelCase__    :     List[str]          =    [to_numpy_array(lowerCAmelCase__			) for image in images]

   if do_resize:
    UpperCAmelCase__    :     Tuple          =    [self.resize(image=lowerCAmelCase__    ,size=lowerCAmelCase__    ,resample=lowerCAmelCase__			) for image in images]

   if do_rescale:
    UpperCAmelCase__    :     Optional[int]          =    [self.rescale(image=lowerCAmelCase__    ,scale=lowerCAmelCase__			) for image in images]

   if do_normalize:
    UpperCAmelCase__    :     List[Any]          =    [self.normalize(image=lowerCAmelCase__    ,mean=lowerCAmelCase__    ,std=lowerCAmelCase__			) for image in images]

   UpperCAmelCase__    :     Union[str, Any]          =    [to_channel_dimension_format(lowerCAmelCase__    ,lowerCAmelCase__			) for image in images]

   UpperCAmelCase__    :     Tuple          =    {"""pixel_values""": images}
   return BatchFeature(data=lowerCAmelCase__    ,tensor_type=lowerCAmelCase__			)







'''simple docstring'''

import io
import json
import subprocess


lowerCAmelCase_			:			Tuple			   =     [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

lowerCAmelCase_			:			str			   =     8







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Union[str, Any]         =    {}
     for src, tgt in pairs:
          _UpperCAmelCase					:  Union[str, Any]         =    f"{src}-{tgt}"
          _UpperCAmelCase					:  List[Any]         =    f"sacrebleu -t wmt19 -l {pair} --echo src".split()
          _UpperCAmelCase					:  List[str]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  str         =    f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
          _UpperCAmelCase					:  List[Any]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  int         =    {"""src""": src_lines[:n_objs], """tgt""": tgt_lines[:n_objs]}
     return text


lowerCAmelCase_			:			Optional[int]			   =     get_all_data(pairs, n_objs)
lowerCAmelCase_			:			str			   =     '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   lowerCAmelCase_			:			Tuple			   =     json.dump(text, f, indent=2, ensure_ascii=False)


