


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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  def __init__(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							VQModel				,							SCREAMING_SNAKE_CASE__     :							UNetaDModel				,							SCREAMING_SNAKE_CASE__     :							DDIMScheduler						):





       '''simple docstring'''

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


  @torch.no_grad()
  def __call__(					self     :							Optional[Any]				,							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[str] = "pil"				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							**SCREAMING_SNAKE_CASE__     :							int				,							):





       '''simple docstring'''

       __a       =   randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,							generator=__A				,							)
       __a       =   latents.to(self.device						)

       # scale the initial noise by the standard deviation required by the scheduler
       __a       =   latents * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(__A						)

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       __a       =   """eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

       __a       =   {}
       if accepts_eta:
            __a       =   eta

       for t in self.progress_bar(self.scheduler.timesteps						):
            __a       =   self.scheduler.scale_model_input(__A				,							__A						)
            # predict the noise residual
            __a       =   self.unet(__A				,							__A						).sample
            # compute the previous noisy sample x_t -> x_t-1
            __a       =   self.scheduler.step(__A				,							__A				,							__A				,							**__A						).prev_sample

       # decode the image latents with the VAE
       __a       =   self.vqvae.decode(__A						).sample

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=__A						)










'''simple docstring'''







import unittest

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


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)






