


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             super().__init__()

             # make sure scheduler can always be converted to DDIM
             a_      :	Any							=						DDIMScheduler.from_config(scheduler.config  )

             self.register_modules(unet=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  )

      @torch.no_grad()
      def __call__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,							SCREAMING_SNAKE_CASE__       : float = 0.0  ,							SCREAMING_SNAKE_CASE__       : int = 5_0  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    Union[ImagePipelineOutput, Tuple]:

             # Sample gaussian noise to begin loop
             if isinstance(self.unet.config.sample_size  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Any							=						(
                        batch_size,
                        self.unet.config.in_channels,
                        self.unet.config.sample_size,
                        self.unet.config.sample_size,
                    )
             else:
                    a_      :	Optional[Any]							=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) and len(SCREAMING_SNAKE_CASE__  ) != batch_size:
                    raise ValueError(
                        F"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__  )}, but requested an effective batch"""
                        F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""  )

             a_      :	Tuple							=						randn_tensor(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=self.device  ,							dtype=self.unet.dtype  )

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

             for t in self.progress_bar(self.scheduler.timesteps  ):
                    # 1. predict noise model_output
                    a_      :	int							=						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
                    a_      :	str							=						self.scheduler.step(
                        SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  ,							use_clipped_model_output=SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ).prev_sample

             a_      :	Any							=						(image / 2 + 0.5).clamp(0  ,							1  )
             a_      :	Union[str, Any]							=						image.cpu().permute(0  ,							2  ,							3  ,							1  ).numpy()
             if output_type == "pil":
                    a_      :	Optional[int]							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__    )		,					'''Tatoeba directory does not exist.'''    )
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             a_      :	Optional[Any]							=						tempfile.mkdtemp()
             return TatoebaConverter(save_dir=SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             self.resolver.convert_models(['heb-eng']  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             a_							,	a_      :	Dict							=						self.resolver.write_model_card('opus-mt-he-en'  ,							dry_run=SCREAMING_SNAKE_CASE__  )
             assert mmeta["long_pair"] == "heb-eng"


