


'''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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase			:   Any 		=  logging.get_logger(__name__)


if is_vision_available():
       import PIL
class 		UpperCamelCase__    (       lowerCAmelCase_		):



     """simple docstring"""






     __magic_name__				=			['''pixel_values''']







     def __init__(       self					,		snake_case__ = True					,		snake_case__ = None					,		snake_case__ = PILImageResampling.BICUBIC					,		snake_case__ = True					,		snake_case__ = None					,		snake_case__ = True					,		snake_case__ = 1 / 255					,		snake_case__ = True					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = True					,		**snake_case__					,		):

            '''simple docstring'''

            super().__init__(**_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							Any      	=				size if size is not None else {'shortest_edge': 224}
            _lowerCAmelCase			:							Tuple      	=				get_size_dict(_SCREAMING_SNAKE_CASE					,		default_to_square=_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							str      	=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
            _lowerCAmelCase			:							Optional[int]      	=				get_size_dict(_SCREAMING_SNAKE_CASE      )

            _lowerCAmelCase			:							List[str]      	=				do_resize
            _lowerCAmelCase			:							Dict      	=				size
            _lowerCAmelCase			:							Optional[int]      	=				resample
            _lowerCAmelCase			:							Union[str, Any]      	=				do_center_crop
            _lowerCAmelCase			:							Union[str, Any]      	=				crop_size
            _lowerCAmelCase			:							List[str]      	=				do_rescale
            _lowerCAmelCase			:							Dict      	=				rescale_factor
            _lowerCAmelCase			:							Any      	=				do_normalize
            _lowerCAmelCase			:							List[str]      	=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            _lowerCAmelCase			:							Dict      	=				image_std if image_std is not None else OPENAI_CLIP_STD
            _lowerCAmelCase			:							Optional[Any]      	=				do_convert_rgb







     def a		(       self					,		snake_case__					,		snake_case__					,		snake_case__ = PILImageResampling.BICUBIC					,		snake_case__ = None					,		**snake_case__					,		):

            '''simple docstring'''

            _lowerCAmelCase			:							Dict      	=				get_size_dict(_SCREAMING_SNAKE_CASE					,		default_to_square=_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							List[str]      	=				get_resize_output_image_size(
                _SCREAMING_SNAKE_CASE					,		size=(size['height'], size['width'])					,		default_to_square=_SCREAMING_SNAKE_CASE      )
            return resize(_SCREAMING_SNAKE_CASE					,		size=_SCREAMING_SNAKE_CASE					,		resample=_SCREAMING_SNAKE_CASE					,		data_format=_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE      )







     def a		(       self					,		snake_case__					,		snake_case__					,		snake_case__ = None					,		**snake_case__					,		):

            '''simple docstring'''

            _lowerCAmelCase			:							List[Any]      	=				get_size_dict(_SCREAMING_SNAKE_CASE      )
            return center_crop(_SCREAMING_SNAKE_CASE					,		size=(size['height'], size['width'])					,		data_format=_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE      )







     def a		(       self					,		snake_case__					,		snake_case__					,		snake_case__ = None					,		**snake_case__					,		):

            '''simple docstring'''

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







     def a		(       self					,		snake_case__					,		snake_case__					,		snake_case__					,		snake_case__ = None					,		**snake_case__					,		):

            '''simple docstring'''

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







     def a		(       self					,		snake_case__					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = ChannelDimension.FIRST					,		**snake_case__					,		):

            '''simple docstring'''

            _lowerCAmelCase			:							Union[str, Any]      	=				do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase			:							Any      	=				size if size is not None else self.size
            _lowerCAmelCase			:							List[Any]      	=				get_size_dict(_SCREAMING_SNAKE_CASE					,		default_to_square=_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							Optional[int]      	=				resample if resample is not None else self.resample
            _lowerCAmelCase			:							Optional[Any]      	=				do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase			:							List[str]      	=				crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase			:							Tuple      	=				get_size_dict(_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							str      	=				do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase			:							Union[str, Any]      	=				rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase			:							str      	=				do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase			:							Tuple      	=				image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase			:							List[Any]      	=				image_std if image_std is not None else self.image_std
            _lowerCAmelCase			:							Dict      	=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            _lowerCAmelCase			:							Union[str, Any]      	=				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_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.'      )

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                   _lowerCAmelCase			:							Optional[Any]      	=				[convert_to_rgb(_SCREAMING_SNAKE_CASE      ) for image in images]

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

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

            if do_center_crop:
                   _lowerCAmelCase			:							Union[str, Any]      	=				[self.center_crop(image=_SCREAMING_SNAKE_CASE					,		size=_SCREAMING_SNAKE_CASE      ) for image in images]

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

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

            _lowerCAmelCase			:							List[str]      	=				[to_channel_dimension_format(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      ) for image in images]

            _lowerCAmelCase			:							Any      	=				{'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)

