
"""simple docstring"""




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          super().__init__()
          self.register_modules(unet=_a      ,       scheduler=_a							)

   @torch.no_grad()
   def __call__(     self      ,       _a = 1      ,       _a = None      ,       _a = 50      ,       _a = "pil"      ,       _a = True      ,       **_a      ,       ):
          lowercase       :							Any									=				torch.randn(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,       generator=_a      ,       )
          lowercase       :							List[str]									=				image.to(self.device							)

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

          for t in self.progress_bar(self.scheduler.timesteps							):
                 # 1. predict noise model_output
                 lowercase       :							Optional[int]									=				self.unet(_a      ,       _a							).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
                 lowercase       :							Tuple									=				self.scheduler.step(_a      ,       _a      ,       _a							).prev_sample

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

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

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




"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


