
'''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


lowerCAmelCase_			:			Dict			   =     logging.get_logger(__name__)







class 	__lowerCAmelCase    (  __a				):
     snake_case   :    List[Any]							      =      ["""pixel_values"""]



     def __init__(self						,	lowerCAmelCase__ = True						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = PILImageResampling.BILINEAR						,	lowerCAmelCase__ = True						,	lowerCAmelCase__ = 1 / 2_5_5						,	lowerCAmelCase__ = True						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):
          super().__init__(**lowerCAmelCase__	)
          _UpperCAmelCase					:  Any         =    size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
          _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     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = PILImageResampling.BILINEAR						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):
          _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     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__	):
          return rescale(lowerCAmelCase__						,	scale=lowerCAmelCase__						,	data_format=lowerCAmelCase__						,	**lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):
          return normalize(lowerCAmelCase__						,	mean=lowerCAmelCase__						,	std=lowerCAmelCase__						,	data_format=lowerCAmelCase__						,	**lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = ChannelDimension.FIRST						,	**lowerCAmelCase__						,	):
          _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)


