



'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: List[str]  =      DDIMPipeline
 __magic_name__					: Optional[int]  =      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __magic_name__					: Union[str, Any]  =      PipelineTesterMixin.required_optional_params - {
     "num_images_per_prompt",
     "latents",
     "callback",
     "callback_steps",
 }
 __magic_name__					: Dict  =      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __magic_name__					: Any  =      False




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      torch.manual_seed(0)
      a_       		=UNetaDModel(
          block_out_channels=(3_2, 6_4)							,  layers_per_block=2							,  sample_size=3_2							,  in_channels=3							,  out_channels=3							,  down_block_types=("DownBlock2D", "AttnDownBlock2D")							,  up_block_types=("AttnUpBlock2D", "UpBlock2D")							,  )
      a_       		=DDIMScheduler()
      a_       		={"unet": unet, "scheduler": scheduler}
      return components




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=0) ->      Union[str, Any]:



      """simple docstring"""







      if str(lowerCAmelCase_).startswith("mps"):
           a_       		=torch.manual_seed(lowerCAmelCase_)
      else:
           a_       		=torch.Generator(device=lowerCAmelCase_).manual_seed(lowerCAmelCase_)
      a_       		={
          "batch_size": 1,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		="cpu"

      a_       		=self.get_dummy_components()
      a_       		=self.pipeline_class(**lowerCAmelCase_)
      pipe.to(lowerCAmelCase_)
      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=self.get_dummy_inputs(lowerCAmelCase_)
      a_       		=pipe(**lowerCAmelCase_).images
      a_       		=image[0, -3:, -3:, -1]

      self.assertEqual(image.shape							,  (1, 3_2, 3_2, 3))
      a_       		=np.array(
          [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04])
      a_       		=np.abs(image_slice.flatten() - expected_slice).max()
      self.assertLessEqual(lowerCAmelCase_							,  1e-3)




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      super().test_save_load_local(expected_max_difference=3e-3)




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      super().test_save_load_optional_components(expected_max_difference=3e-3)




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      super().test_inference_batch_single_identical(expected_max_diff=3e-3)







@slow
@require_torch_gpu
class 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		="google/ddpm-cifar10-32"

      a_       		=UNetaDModel.from_pretrained(lowerCAmelCase_)
      a_       		=DDIMScheduler()

      a_       		=DDIMPipeline(unet=lowerCAmelCase_							,  scheduler=lowerCAmelCase_)
      ddim.to(lowerCAmelCase_)
      ddim.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=torch.manual_seed(0)
      a_       		=ddim(generator=lowerCAmelCase_							,  eta=0.0							,  output_type="numpy").images

      a_       		=image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      a_       		=np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		="google/ddpm-ema-bedroom-256"

      a_       		=UNetaDModel.from_pretrained(lowerCAmelCase_)
      a_       		=DDIMScheduler.from_pretrained(lowerCAmelCase_)

      a_       		=DDIMPipeline(unet=lowerCAmelCase_							,  scheduler=lowerCAmelCase_)
      ddpm.to(lowerCAmelCase_)
      ddpm.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=torch.manual_seed(0)
      a_       		=ddpm(generator=lowerCAmelCase_							,  output_type="numpy").images

      a_       		=image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_5_6, 2_5_6, 3)
      a_       		=np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[0]  # The last hidden-state is the first element of the output tuple
     a_       		=(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     a_       		=logits[0, masked_index, :]
     a_       		=logits.softmax(dim=0       )
     a_		,						a_       		=prob.topk(k=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     a_       		=tokenizer.mask_token
     a_       		=[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          a_       		=predicted_token_bpe.replace("\u2581"       ,	" "       )
          if " {0}".format(lowercase__       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(lowercase__       )       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(lowercase__       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


lowercase							       =			CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase							       =			CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase							       =			'''Le camembert est <mask> :)'''

print(fill_mask(masked_input, model, tokenizer, topk=3))


