




'''simple docstring'''





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      _a       (		__lowerCAmelCase							):






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    str:
          super().__init__()
          self.register_modules(unet=_SCREAMING_SNAKE_CASE      ,scheduler=_SCREAMING_SNAKE_CASE					)






     @torch.no_grad()
     def __call__(				self      ,_SCREAMING_SNAKE_CASE = 1      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = 50      ,_SCREAMING_SNAKE_CASE = "pil"      ,_SCREAMING_SNAKE_CASE = True      ,**_SCREAMING_SNAKE_CASE      ,)							->    Union[ImagePipelineOutput, Tuple]:
          _snake_case							=   torch.randn(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,generator=_SCREAMING_SNAKE_CASE      ,)
          _snake_case							=   image.to(self.device					)

          # set step values
          self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

          for t in self.progress_bar(self.scheduler.timesteps					):
               # 1. predict noise model_output
               _snake_case							=   self.unet(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               _snake_case							=   self.scheduler.step(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					).prev_sample

          _snake_case							=   (image / 2 + 0.5).clamp(0      ,1					)
          _snake_case							=   image.cpu().permute(0      ,2      ,3      ,1					).numpy()
          if output_type == "pil":
               _snake_case							=   self.numpy_to_pil(_SCREAMING_SNAKE_CASE					)

          if not return_dict:
               return (image,), "This is a local test"

          return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE					), "This is a local test"








'''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)


