




'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCamelCase_		:    List[str]									=		logging.get_logger(__name__)


if is_vision_available():
   import PIL

class      _a       (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					Dict					       =		["""pixel_values"""]






     def __init__(				self      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = False      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = 1 / 255      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    None:
          super().__init__(**_SCREAMING_SNAKE_CASE					)

          _snake_case							=   size if size is not None else {"height": 2_560, "width": 1_920}
          if isinstance(_SCREAMING_SNAKE_CASE      ,(tuple, list)					):
               # The previous feature extractor size parameter was in (width, height) format
               _snake_case							=   size[::-1]
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   do_resize
          _snake_case							=   size
          _snake_case							=   resample
          _snake_case							=   do_thumbnail
          _snake_case							=   do_align_long_axis
          _snake_case							=   do_pad
          _snake_case							=   do_rescale
          _snake_case							=   rescale_factor
          _snake_case							=   do_normalize
          _snake_case							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _snake_case							=   image_std if image_std is not None else IMAGENET_STANDARD_STD






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    np.ndarray:
          _snake_case							,      _snake_case							=   get_image_size(_SCREAMING_SNAKE_CASE					)
          _snake_case							,      _snake_case							=   size["height"], size["width"]

          if (output_width < output_height and input_width > input_height) or (
              output_width > output_height and input_width < input_height
          ):
               _snake_case							=   np.rotaa(_SCREAMING_SNAKE_CASE      ,3					)

          if data_format is not None:
               _snake_case							=   to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)

          return image






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = False      ,_SCREAMING_SNAKE_CASE = None      ,)							->    np.ndarray:
          _snake_case							,      _snake_case							=   size["height"], size["width"]
          _snake_case							,      _snake_case							=   get_image_size(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   output_width - input_width
          _snake_case							=   output_height - input_height

          if random_padding:
               _snake_case							=   np.random.randint(low=0      ,high=delta_height + 1					)
               _snake_case							=   np.random.randint(low=0      ,high=delta_width + 1					)
          else:
               _snake_case							=   delta_height // 2
               _snake_case							=   delta_width // 2

          _snake_case							=   delta_height - pad_top
          _snake_case							=   delta_width - pad_left

          _snake_case							=   ((pad_top, pad_bottom), (pad_left, pad_right))
          return pad(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)							->    str:
          logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."					)
          return self.pad_image(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          _snake_case							,      _snake_case							=   get_image_size(_SCREAMING_SNAKE_CASE					)
          _snake_case							,      _snake_case							=   size["height"], size["width"]

          # We always resize to the smallest of either the input or output size.
          _snake_case							=   min(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   min(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)

          if height == input_height and width == input_width:
               return image

          if input_height > input_width:
               _snake_case							=   int(input_width * height / input_height					)
          elif input_width > input_height:
               _snake_case							=   int(input_height * width / input_width					)

          return resize(
              _SCREAMING_SNAKE_CASE      ,size=(height, width)      ,resample=_SCREAMING_SNAKE_CASE      ,reducing_gap=2.0      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   min(size["height"]      ,size["width"]					)
          _snake_case							=   get_resize_output_image_size(_SCREAMING_SNAKE_CASE      ,size=_SCREAMING_SNAKE_CASE      ,default_to_square=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   resize(_SCREAMING_SNAKE_CASE      ,size=_SCREAMING_SNAKE_CASE      ,resample=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)
          return resized_image






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    Tuple:
          return rescale(_SCREAMING_SNAKE_CASE      ,scale=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          return normalize(_SCREAMING_SNAKE_CASE      ,mean=_SCREAMING_SNAKE_CASE      ,std=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = False      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,**_SCREAMING_SNAKE_CASE      ,)							->    PIL.Image.Image:
          _snake_case							=   do_resize if do_resize is not None else self.do_resize
          _snake_case							=   size if size is not None else self.size
          if isinstance(_SCREAMING_SNAKE_CASE      ,(tuple, list)					):
               # Previous feature extractor had size in (width, height) format
               _snake_case							=   size[::-1]
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   resample if resample is not None else self.resample
          _snake_case							=   do_thumbnail if do_thumbnail is not None else self.do_thumbnail
          _snake_case							=   do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
          _snake_case							=   do_pad if do_pad is not None else self.do_pad
          _snake_case							=   do_rescale if do_rescale is not None else self.do_rescale
          _snake_case							=   rescale_factor if rescale_factor is not None else self.rescale_factor
          _snake_case							=   do_normalize if do_normalize is not None else self.do_normalize
          _snake_case							=   image_mean if image_mean is not None else self.image_mean
          _snake_case							=   image_std if image_std is not None else self.image_std

          _snake_case							=   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."					)

          if do_pad and size is None:
               raise ValueError("Size must be specified if do_pad 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.
          _snake_case							=   [to_numpy_array(_SCREAMING_SNAKE_CASE					) for image in images]

          if do_align_long_axis:
               _snake_case							=   [self.align_long_axis(_SCREAMING_SNAKE_CASE      ,size=_SCREAMING_SNAKE_CASE					) for image in images]

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

          if do_thumbnail:
               _snake_case							=   [self.thumbnail(image=_SCREAMING_SNAKE_CASE      ,size=_SCREAMING_SNAKE_CASE					) for image in images]

          if do_pad:
               _snake_case							=   [self.pad_image(image=_SCREAMING_SNAKE_CASE      ,size=_SCREAMING_SNAKE_CASE      ,random_padding=_SCREAMING_SNAKE_CASE					) for image in images]

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

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

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

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








'''simple docstring'''





import io
import json
import subprocess


UpperCamelCase_		:    Any									=		[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase_		:    Union[str, Any]									=		8





def 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					Optional[Any]					)		->				Optional[int]:

     """simple docstring"""




     _snake_case							=   {}
     for src, tgt in pairs:
          _snake_case							=   F"""{src}-{tgt}"""
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
     return text


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


