
'''simple docstring'''




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_A:					List[str]							=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def _lowerCAmelCase     (      _lowerCAmelCase    )->     str:
 warnings.warn(
     'The preprocess method is deprecated and will be removed in a future version. Please'
     ' use VaeImageProcessor.preprocess instead'       ,    _lowerCAmelCase       ,    )
 if isinstance(_lowerCAmelCase       ,    torch.Tensor    ):
  return image
 elif isinstance(_lowerCAmelCase       ,    PIL.Image.Image    ):
  __UpperCAmelCase					=   [image]

 if isinstance(image[0]       ,    PIL.Image.Image    ):
  __UpperCAmelCase					,    __UpperCAmelCase					=   image[0].size
  __UpperCAmelCase					,    __UpperCAmelCase					=   (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

  __UpperCAmelCase					=   [np.array(i.resize((w, h)       ,    resample=PIL_INTERPOLATION['lanczos']    )    )[None, :] for i in image]
  __UpperCAmelCase					=   np.concatenate(_lowerCAmelCase       ,    axis=0    )
  __UpperCAmelCase					=   np.array(_lowerCAmelCase    ).astype(np.floataa    ) / 255.0
  __UpperCAmelCase					=   image.transpose(0       ,    3       ,    1       ,    2    )
  __UpperCAmelCase					=   2.0 * image - 1.0
  __UpperCAmelCase					=   torch.from_numpy(_lowerCAmelCase    )
 elif isinstance(image[0]       ,    torch.Tensor    ):
  __UpperCAmelCase					=   torch.cat(_lowerCAmelCase       ,    dim=0    )
 return image







def _lowerCAmelCase     (      _lowerCAmelCase    )->     int:
 if isinstance(_lowerCAmelCase       ,    torch.Tensor    ):
  return mask
 elif isinstance(_lowerCAmelCase       ,    PIL.Image.Image    ):
  __UpperCAmelCase					=   [mask]

 if isinstance(mask[0]       ,    PIL.Image.Image    ):
  __UpperCAmelCase					,    __UpperCAmelCase					=   mask[0].size
  __UpperCAmelCase					,    __UpperCAmelCase					=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __UpperCAmelCase					=   [np.array(m.convert('L'    ).resize((w, h)       ,    resample=PIL_INTERPOLATION['nearest']    )    )[None, :] for m in mask]
  __UpperCAmelCase					=   np.concatenate(_lowerCAmelCase       ,    axis=0    )
  __UpperCAmelCase					=   mask.astype(np.floataa    ) / 255.0
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   1
  __UpperCAmelCase					=   torch.from_numpy(_lowerCAmelCase    )
 elif isinstance(mask[0]       ,    torch.Tensor    ):
  __UpperCAmelCase					=   torch.cat(_lowerCAmelCase       ,    dim=0    )
 return mask






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			UNetaDModel
    _A :			RePaintScheduler



    def __init__(  self     ,			__A     ,			__A					):
     super().__init__()
     self.register_modules(unet=__A     ,			scheduler=__A					)




    @torch.no_grad()
    def __call__(  self     ,			__A     ,			__A     ,			__A = 250     ,			__A = 0.0     ,			__A = 10     ,			__A = 10     ,			__A = None     ,			__A = "pil"     ,			__A = True     ,			):
     __UpperCAmelCase					=   image

     __UpperCAmelCase					=   _preprocess_image(__A					)
     __UpperCAmelCase					=   original_image.to(device=self.device     ,			dtype=self.unet.dtype					)
     __UpperCAmelCase					=   _preprocess_mask(__A					)
     __UpperCAmelCase					=   mask_image.to(device=self.device     ,			dtype=self.unet.dtype					)

     __UpperCAmelCase					=   original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(__A     ,			__A					) and len(__A					) != batch_size:
      raise ValueError(
          f'You have passed a list of generators of length {len(__A					)}, but requested an effective batch'
          f' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

     __UpperCAmelCase					=   original_image.shape
     __UpperCAmelCase					=   randn_tensor(__A     ,			generator=__A     ,			device=self.device     ,			dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(__A     ,			__A     ,			__A     ,			self.device					)
     __UpperCAmelCase					=   eta

     __UpperCAmelCase					=   self.scheduler.timesteps[0] + 1
     __UpperCAmelCase					=   generator[0] if isinstance(__A     ,			__A					) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
      if t < t_last:
       # predict the noise residual
       __UpperCAmelCase					=   self.unet(__A     ,			__A					).sample
       # compute previous image: x_t -> x_t-1
       __UpperCAmelCase					=   self.scheduler.step(__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					).prev_sample

      else:
       # compute the reverse: x_t-1 -> x_t
       __UpperCAmelCase					=   self.scheduler.undo_step(__A     ,			__A     ,			__A					)
      __UpperCAmelCase					=   t

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

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=__A					)












'''simple docstring'''




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_A:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





