




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViltImageProcessor
class 							__lowercase   (			unittest.TestCase    ):



     def __init__(  self   :							Optional[int]      ,		lowercase__   :							Tuple      ,		lowercase__   :							Union[str, Any]=7      ,		lowercase__   :							Tuple=3      ,		lowercase__   :							List[Any]=1_8      ,		lowercase__   :							Optional[int]=3_0      ,		lowercase__   :							str=4_0_0      ,		lowercase__   :							Optional[Any]=True      ,		lowercase__   :							List[Any]=None      ,		lowercase__   :							Dict=2      ,		lowercase__   :							List[str]=True      ,		lowercase__   :							List[str]=[0.5, 0.5, 0.5]      ,		lowercase__   :							List[str]=[0.5, 0.5, 0.5]      ,		):
       a_		    =  size if size is not None else {'''shortest_edge''': 3_0}
       a_		    =  parent
       a_		    =  batch_size
       a_		    =  num_channels
       a_		    =  image_size
       a_		    =  min_resolution
       a_		    =  max_resolution
       a_		    =  do_resize
       a_		    =  size
       a_		    =  size_divisor
       a_		    =  do_normalize
       a_		    =  image_mean
       a_		    =  image_std



     def 				__magic_name__		(  self   :							int       ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }



     def 				__magic_name__		(  self   :							Dict      ,		lowercase__   :							str      ,		lowercase__   :							Union[str, Any]=False       ):
       if not batched:
         a_		    =  self.size['''shortest_edge''']
         a_		    =  image_inputs[0]
         if isinstance(lowercase__      ,		Image.Image       ):
           a_    , a_		    =  image.size
         else:
           a_    , a_		    =  image.shape[1], image.shape[2]
         a_		    =  size / min(lowercase__      ,		lowercase__       )
         if h < w:
           a_    , a_		    =  size, scale * w
         else:
           a_    , a_		    =  scale * h, size

         a_		    =  int((1_3_3_3 / 8_0_0) * size       )
         if max(lowercase__      ,		lowercase__       ) > max_size:
           a_		    =  max_size / max(lowercase__      ,		lowercase__       )
           a_		    =  newh * scale
           a_		    =  neww * scale

         a_    , a_		    =  int(newh + 0.5       ), int(neww + 0.5       )
         a_    , a_		    =  (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

       else:
         a_		    =  []
         for image in image_inputs:
           a_    , a_		    =  self.get_expected_values([image]       )
           expected_values.append((expected_height, expected_width)       )
         a_		    =  max(lowercase__      ,		key=lambda lowercase__       : item[0]       )[0]
         a_		    =  max(lowercase__      ,		key=lambda lowercase__       : item[1]       )[1]

       return expected_height, expected_width



@require_torch
@require_vision
class 							__lowercase   (			a__	,		unittest.TestCase    ):
     _lowerCAmelCase		       =						ViltImageProcessor if is_vision_available() else None



     def 				__magic_name__		(  self   :							int       ):
       a_		    =  ViltImageProcessingTester(self       )



     @property
     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       return self.image_processor_tester.prepare_image_processor_dict()



     def 				__magic_name__		(  self   :							int       ):
       a_		    =  self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(lowercase__      ,		'''image_mean'''       )       )
       self.assertTrue(hasattr(lowercase__      ,		'''image_std'''       )       )
       self.assertTrue(hasattr(lowercase__      ,		'''do_normalize'''       )       )
       self.assertTrue(hasattr(lowercase__      ,		'''do_resize'''       )       )
       self.assertTrue(hasattr(lowercase__      ,		'''size'''       )       )
       self.assertTrue(hasattr(lowercase__      ,		'''size_divisor'''       )       )



     def 				__magic_name__		(  self   :							Optional[int]       ):
       a_		    =  self.image_processing_class.from_dict(self.image_processor_dict       )
       self.assertEqual(image_processor.size      ,		{'''shortest_edge''': 3_0}       )

       a_		    =  self.image_processing_class.from_dict(self.image_processor_dict      ,		size=4_2       )
       self.assertEqual(image_processor.size      ,		{'''shortest_edge''': 4_2}       )



     def 				__magic_name__		(  self   :							int       ):
       pass



     def 				__magic_name__		(  self   :							int       ):
       # Initialize image_processing
       a_		    =  self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       a_		    =  prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase__       )
       for image in image_inputs:
         self.assertIsInstance(lowercase__      ,		Image.Image       )

       # Test not batched input
       a_		    =  image_processing(image_inputs[0]      ,		return_tensors='''pt'''       ).pixel_values

       a_    , a_		    =  self.image_processor_tester.get_expected_values(lowercase__       )
       self.assertEqual(
           encoded_images.shape      ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,		)

       # Test batched
       a_		    =  image_processing(lowercase__      ,		return_tensors='''pt'''       ).pixel_values

       a_    , a_		    =  self.image_processor_tester.get_expected_values(lowercase__      ,		batched=lowercase__       )
       self.assertEqual(
           encoded_images.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,		)



     def 				__magic_name__		(  self   :							List[str]       ):
       # Initialize image_processing
       a_		    =  self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       a_		    =  prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase__      ,		numpify=lowercase__       )
       for image in image_inputs:
         self.assertIsInstance(lowercase__      ,		np.ndarray       )

       # Test not batched input
       a_		    =  image_processing(image_inputs[0]      ,		return_tensors='''pt'''       ).pixel_values

       a_    , a_		    =  self.image_processor_tester.get_expected_values(lowercase__       )
       self.assertEqual(
           encoded_images.shape      ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,		)

       # Test batched
       a_		    =  image_processing(lowercase__      ,		return_tensors='''pt'''       ).pixel_values

       a_    , a_		    =  self.image_processor_tester.get_expected_values(lowercase__      ,		batched=lowercase__       )
       self.assertEqual(
           encoded_images.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,		)



     def 				__magic_name__		(  self   :							List[Any]       ):
       # Initialize image_processing
       a_		    =  self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       a_		    =  prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase__      ,		torchify=lowercase__       )
       for image in image_inputs:
         self.assertIsInstance(lowercase__      ,		torch.Tensor       )

       # Test not batched input
       a_		    =  image_processing(image_inputs[0]      ,		return_tensors='''pt'''       ).pixel_values

       a_    , a_		    =  self.image_processor_tester.get_expected_values(lowercase__       )
       self.assertEqual(
           encoded_images.shape      ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,		)

       # Test batched
       a_		    =  image_processing(lowercase__      ,		return_tensors='''pt'''       ).pixel_values

       a_    , a_		    =  self.image_processor_tester.get_expected_values(lowercase__      ,		batched=lowercase__       )
       self.assertEqual(
           encoded_images.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,		)






import io
import json
import subprocess


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

UpperCamelCase__       				=	8






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  {}
  for src, tgt in pairs:
    a_		    =  f"{src}-{tgt}"
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo src".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  {'''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)
