





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 							_UpperCamelCase    (		lowerCAmelCase_   ):
       _UpperCamelCase :    Tuple   			=      ['''pixel_values''']

       def __init__(						self:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       bool = True    ,      _SCREAMING_SNAKE_CASE:       Optional[Dict[str, int]] = None    ,      _SCREAMING_SNAKE_CASE:       PILImageResampling = PILImageResampling.BILINEAR    ,      _SCREAMING_SNAKE_CASE:       bool = True    ,      _SCREAMING_SNAKE_CASE:       Union[int, float] = 1 / 255    ,      _SCREAMING_SNAKE_CASE:       bool = True    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[float, List[float]]] = None    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[float, List[float]]] = None    ,      **_SCREAMING_SNAKE_CASE:       Optional[Any]    ,      )				->       None:




              """simple docstring"""


              super().__init__(**_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     size if size is not None else {"height": 224, "width": 224}
              UpperCamelCase_      				=     get_size_dict(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     do_resize
              UpperCamelCase_      				=     do_rescale
              UpperCamelCase_      				=     do_normalize
              UpperCamelCase_      				=     size
              UpperCamelCase_      				=     resample
              UpperCamelCase_      				=     rescale_factor
              UpperCamelCase_      				=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              UpperCamelCase_      				=     image_std if image_std is not None else IMAGENET_STANDARD_STD

       def lowercase       (						self:       List[str]    ,      _SCREAMING_SNAKE_CASE:       np.ndarray    ,      _SCREAMING_SNAKE_CASE:       Dict[str, int]    ,      _SCREAMING_SNAKE_CASE:       PILImageResampling = PILImageResampling.BILINEAR    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[str, ChannelDimension]] = None    ,      **_SCREAMING_SNAKE_CASE:       Optional[int]    ,      )				->       np.ndarray:




              """simple docstring"""


              UpperCamelCase_      				=     get_size_dict(_SCREAMING_SNAKE_CASE						)
              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(
                  _SCREAMING_SNAKE_CASE    ,      size=(size["height"], size["width"])    ,      resample=_SCREAMING_SNAKE_CASE    ,      data_format=_SCREAMING_SNAKE_CASE    ,      **_SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       List[str]    ,      _SCREAMING_SNAKE_CASE:       np.ndarray    ,      _SCREAMING_SNAKE_CASE:       float    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[str, ChannelDimension]] = None    ,      **_SCREAMING_SNAKE_CASE:       List[Any]						)				->       np.ndarray:




              """simple docstring"""


              return rescale(_SCREAMING_SNAKE_CASE    ,      scale=_SCREAMING_SNAKE_CASE    ,      data_format=_SCREAMING_SNAKE_CASE    ,      **_SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       np.ndarray    ,      _SCREAMING_SNAKE_CASE:       Union[float, List[float]]    ,      _SCREAMING_SNAKE_CASE:       Union[float, List[float]]    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[str, ChannelDimension]] = None    ,      **_SCREAMING_SNAKE_CASE:       Dict    ,      )				->       np.ndarray:




              """simple docstring"""


              return normalize(_SCREAMING_SNAKE_CASE    ,      mean=_SCREAMING_SNAKE_CASE    ,      std=_SCREAMING_SNAKE_CASE    ,      data_format=_SCREAMING_SNAKE_CASE    ,      **_SCREAMING_SNAKE_CASE						)



       def lowercase       (						self:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       ImageInput    ,      _SCREAMING_SNAKE_CASE:       Optional[bool] = None    ,      _SCREAMING_SNAKE_CASE:       Dict[str, int] = None    ,      _SCREAMING_SNAKE_CASE:       PILImageResampling = None    ,      _SCREAMING_SNAKE_CASE:       Optional[bool] = None    ,      _SCREAMING_SNAKE_CASE:       Optional[float] = None    ,      _SCREAMING_SNAKE_CASE:       Optional[bool] = None    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[float, List[float]]] = None    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[float, List[float]]] = None    ,      _SCREAMING_SNAKE_CASE:       Optional[Union[str, TensorType]] = None    ,      _SCREAMING_SNAKE_CASE:       Union[str, ChannelDimension] = ChannelDimension.FIRST    ,      **_SCREAMING_SNAKE_CASE:       List[Any]    ,      )				->       Dict:




              """simple docstring"""


              UpperCamelCase_      				=     do_resize if do_resize is not None else self.do_resize
              UpperCamelCase_      				=     do_rescale if do_rescale is not None else self.do_rescale
              UpperCamelCase_      				=     do_normalize if do_normalize is not None else self.do_normalize
              UpperCamelCase_      				=     resample if resample is not None else self.resample
              UpperCamelCase_      				=     rescale_factor if rescale_factor is not None else self.rescale_factor
              UpperCamelCase_      				=     image_mean if image_mean is not None else self.image_mean
              UpperCamelCase_      				=     image_std if image_std is not None else self.image_std

              UpperCamelCase_      				=     size if size is not None else self.size
              UpperCamelCase_      				=     get_size_dict(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     make_list_of_images(_SCREAMING_SNAKE_CASE						)

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

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

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

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

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

              UpperCamelCase_      				=     [to_channel_dimension_format(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						) for image in images]

              UpperCamelCase_      				=     {"pixel_values": images}
              return BatchFeature(data=_SCREAMING_SNAKE_CASE    ,      tensor_type=_SCREAMING_SNAKE_CASE						)








import io
import json
import subprocess


_UpperCAmelCase				       = [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

_UpperCAmelCase				       = 8




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						Union[str, Any]:
       UpperCamelCase_      				=     {}
       for src, tgt in pairs:
              UpperCamelCase_      				=     F'''{src}-{tgt}'''
              UpperCamelCase_      				=     F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
              UpperCamelCase_      				=     subprocess.run(UpperCamelCase_    ,						stdout=subprocess.PIPE     ).stdout.decode("utf-8"     ).splitlines()
              UpperCamelCase_      				=     F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
              UpperCamelCase_      				=     subprocess.run(UpperCamelCase_    ,						stdout=subprocess.PIPE     ).stdout.decode("utf-8"     ).splitlines()
              UpperCamelCase_      				=     {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
       return text


_UpperCAmelCase				       = get_all_data(pairs, n_objs)
_UpperCAmelCase				       = './fsmt_val_data.json'
with io.open(filename, 'w', encoding='utf-8') as f:
     _UpperCAmelCase				       = json.dump(text, f, indent=2, ensure_ascii=False)

