




import io
import json
import subprocess


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

SCREAMING_SNAKE_CASE__	:				Dict  =       8

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


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












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

       for k in range(t - 1   ):
              # select a column of modified matrix A':
              lowerCamelCase       :				Optional[Any]      			= r[k:, [k]]
              # construct first basis vector
              lowerCamelCase       :				Union[str, Any]      			= np.zeros_like(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				int      			= 1.0
              # determine scaling factor
              lowerCamelCase       :				Any      			= np.linalg.norm(_SCREAMING_SNAKE_CASE   )
              # construct vector v for Householder reflection
              lowerCamelCase       :				List[str]      			= x + np.sign(x[0]   ) * alpha * ea
              v /= np.linalg.norm(_SCREAMING_SNAKE_CASE   )

              # construct the Householder matrix
              lowerCamelCase       :				List[Any]      			= np.eye(m - k   ) - 2.0 * v @ v.T
              # pad with ones and zeros as necessary
              lowerCamelCase       :				Union[str, Any]      			= np.block([[np.eye(_SCREAMING_SNAKE_CASE   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

              lowerCamelCase       :				Any      			= q @ q_k.T
              lowerCamelCase       :				List[Any]      			= q_k @ r

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






