





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class 				__snake_case  (     unittest.TestCase						):


    '''simple docstring'''







    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= 1
        SCREAMING_SNAKE_CASE__       							= 3
        SCREAMING_SNAKE_CASE__       							= 8
        SCREAMING_SNAKE_CASE__       							= 8

        SCREAMING_SNAKE_CASE__       							= torch.rand((batch_size, num_channels, height, width)    )

        return sample



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        if isinstance(image[0]      ,							PIL.Image.Image    ):
            return np.stack([np.array(A_    ) for i in image]      ,							axis=0    )
        elif isinstance(A_      ,							torch.Tensor    ):
            return image.cpu().numpy().transpose(0      ,							2      ,							3      ,							1    )
        return image



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= VaeImageProcessor(do_resize=A_      ,							do_normalize=A_    )

        SCREAMING_SNAKE_CASE__       							= self.dummy_sample
        SCREAMING_SNAKE_CASE__       							= self.to_np(A_    )

        for output_type in ["pt", "np", "pil"]:
            SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
                image_processor.preprocess(A_    )      ,							output_type=A_      ,							)
            SCREAMING_SNAKE_CASE__       							= self.to_np(A_    )
            SCREAMING_SNAKE_CASE__       							= (input_np * 2_55).round() if output_type == '''pil''' else input_np
            assert (
                np.abs(in_np - out_np    ).max() < 1E-6
            ), f'''decoded output does not match input for output_type {output_type}'''



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= VaeImageProcessor(do_resize=A_      ,							do_normalize=A_    )
        SCREAMING_SNAKE_CASE__       							= self.dummy_sample.cpu().numpy().transpose(0      ,							2      ,							3      ,							1    )

        for output_type in ["pt", "np", "pil"]:
            SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(image_processor.preprocess(A_    )      ,							output_type=A_    )

            SCREAMING_SNAKE_CASE__       							= self.to_np(A_    )
            SCREAMING_SNAKE_CASE__       							= (input_np * 2_55).round() if output_type == '''pil''' else input_np
            assert (
                np.abs(in_np - out_np    ).max() < 1E-6
            ), f'''decoded output does not match input for output_type {output_type}'''



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= VaeImageProcessor(do_resize=A_      ,							do_normalize=A_    )

        SCREAMING_SNAKE_CASE__       							= self.dummy_sample.cpu().numpy().transpose(0      ,							2      ,							3      ,							1    )
        SCREAMING_SNAKE_CASE__       							= image_processor.numpy_to_pil(A_    )

        for output_type in ["pt", "np", "pil"]:
            SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(image_processor.preprocess(A_    )      ,							output_type=A_    )
            for i, o in zip(A_      ,							A_    ):
                SCREAMING_SNAKE_CASE__       							= np.array(A_    )
                SCREAMING_SNAKE_CASE__       							= self.to_np(A_    ) if output_type == '''pil''' else (self.to_np(A_    ) * 2_55).round()
                assert (
                    np.abs(in_np - out_np    ).max() < 1E-6
                ), f'''decoded output does not match input for output_type {output_type}'''



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= VaeImageProcessor(do_resize=A_      ,							do_normalize=A_    )

        SCREAMING_SNAKE_CASE__       							= self.dummy_sample
        SCREAMING_SNAKE_CASE__       							= input_pt_ad.squeeze(0    )

        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)
        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)

        SCREAMING_SNAKE_CASE__       							= self.to_np(self.dummy_sample    )
        SCREAMING_SNAKE_CASE__       							= input_np_ad.squeeze(0    )

        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)
        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)

        assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
        assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6




    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= VaeImageProcessor(do_resize=A_      ,							do_normalize=A_    )

        SCREAMING_SNAKE_CASE__       							= self.dummy_sample
        SCREAMING_SNAKE_CASE__       							= list(A_    )

        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)

        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)

        SCREAMING_SNAKE_CASE__       							= self.to_np(self.dummy_sample    )
        list(A_    )

        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)

        SCREAMING_SNAKE_CASE__       							= image_processor.postprocess(
            image_processor.preprocess(A_    )      ,							output_type='''np'''      ,							)

        assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
        assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6







import io
import json
import subprocess


_A				:							str							= [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_A				:							int							= 8


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


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