






"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class 						snake_case  (  UpperCAmelCase	):





    def __init__(							self     : List[Any]    ,		A     : Optional[int]    ,		A     : str      ):


      '''simple docstring'''




      super().__init__()
      self.register_modules(unet=A    ,		scheduler=A      )





    @torch.no_grad()
    def __call__(							self     : Optional[int]    ,		A     : int = 1    ,		A     : Optional[torch.Generator] = None    ,		A     : int = 5_0    ,		A     : Optional[str] = "pil"    ,		A     : bool = True    ,		**A     : Dict    ,		):


      '''simple docstring'''




      a					:      Dict    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,		generator=A    ,		)
      a					:      Optional[Any]    =       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
        a					:      Dict    =       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
        a					:      List[Any]    =       self.scheduler.step(A    ,		A    ,		A      ).prev_sample

      a					:      Optional[int]    =       (image / 2 + 0.5).clamp(0    ,		1      )
      a					:      Union[str, Any]    =       image.cpu().permute(0    ,		2    ,		3    ,		1      ).numpy()
      if output_type == "pil":
        a					:      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 AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       AutoTokenizer.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       'The dog is cute and lives in the garden house'
      a					:      List[Any]    =       jnp.array([tokenizer.encode(A      )]      )

      a					:      int    =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
      a					:      Dict    =       jnp.array(
          [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )

      a					:      Any    =       model(A      )['last_hidden_state']
      self.assertEqual(output.shape    ,		A      )
      # compare the actual values for a slice of last dim
      self.assertTrue(jnp.allclose(output[:, :, -1]    ,		A    ,		atol=1E-3      )      )




