




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class       a					(       lowerCamelCase__      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[str]       		=  (DDPMScheduler,)
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   **__snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					{
             '''num_train_timesteps''': 10_00,
             '''beta_start''': 0.0_001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**snake_case__    )
         return config
     def   lowerCamelCase_       (     self       :      Tuple    ):





         for timesteps in [1, 5, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):





         for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] ,   [0.002, 0.02, 0.2, 2]    ):
             self.check_over_configs(beta_start=snake_case__ ,   beta_end=snake_case__    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):





         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=snake_case__    )
     def   lowerCamelCase_       (     self       :      Any    ):





         for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=snake_case__    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):





         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=snake_case__    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         self.check_over_configs(thresholding=snake_case__    )
         for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=snake_case__ ,   prediction_type=snake_case__ ,   sample_max_value=snake_case__ ,   )
     def   lowerCamelCase_       (     self       :      Tuple    ):





         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=snake_case__    )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):





         for t in [0, 5_00, 9_99]:
             self.check_over_forward(time_step=snake_case__    )
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**snake_case__    )

         assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87    ) - 0.00_979    )    ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99    ) - 0.02    )    ) < 1E-5
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**snake_case__    )

         UpperCAmelCase_										=					len(snake_case__    )

         UpperCAmelCase_										=					self.dummy_model()
         UpperCAmelCase_										=					self.dummy_sample_deter
         UpperCAmelCase_										=					torch.manual_seed(0    )

         for t in reversed(range(snake_case__    )    ):
             # 1. predict noise residual
             UpperCAmelCase_										=					model(snake_case__ ,   snake_case__    )

             # 2. predict previous mean of sample x_t-1
             UpperCAmelCase_										=					scheduler.step(snake_case__ ,   snake_case__ ,   snake_case__ ,   generator=snake_case__    ).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             UpperCAmelCase_										=					pred_prev_sample

         UpperCAmelCase_										=					torch.sum(torch.abs(snake_case__    )    )
         UpperCAmelCase_										=					torch.mean(torch.abs(snake_case__    )    )

         assert abs(result_sum.item() - 258.9_606    ) < 1E-2
         assert abs(result_mean.item() - 0.3_372    ) < 1E-3
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config(prediction_type='''v_prediction'''    )
         UpperCAmelCase_										=					scheduler_class(**snake_case__    )

         UpperCAmelCase_										=					len(snake_case__    )

         UpperCAmelCase_										=					self.dummy_model()
         UpperCAmelCase_										=					self.dummy_sample_deter
         UpperCAmelCase_										=					torch.manual_seed(0    )

         for t in reversed(range(snake_case__    )    ):
             # 1. predict noise residual
             UpperCAmelCase_										=					model(snake_case__ ,   snake_case__    )

             # 2. predict previous mean of sample x_t-1
             UpperCAmelCase_										=					scheduler.step(snake_case__ ,   snake_case__ ,   snake_case__ ,   generator=snake_case__    ).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             UpperCAmelCase_										=					pred_prev_sample

         UpperCAmelCase_										=					torch.sum(torch.abs(snake_case__    )    )
         UpperCAmelCase_										=					torch.mean(torch.abs(snake_case__    )    )

         assert abs(result_sum.item() - 202.0_296    ) < 1E-2
         assert abs(result_mean.item() - 0.2_631    ) < 1E-3
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**snake_case__    )

         UpperCAmelCase_										=					[1_00, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=snake_case__    )

         UpperCAmelCase_										=					scheduler.timesteps

         for i, timestep in enumerate(snake_case__    ):
             if i == len(snake_case__    ) - 1:
                 UpperCAmelCase_										=					-1
             else:
                 UpperCAmelCase_										=					timesteps[i + 1]

             UpperCAmelCase_										=					scheduler.previous_timestep(snake_case__    )
             UpperCAmelCase_										=					prev_t.item()

             self.assertEqual(snake_case__ ,   snake_case__    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**snake_case__    )

         UpperCAmelCase_										=					[1_00, 87, 50, 51, 0]

         with self.assertRaises(snake_case__ ,   msg='''`custom_timesteps` must be in descending order.'''    ):
             scheduler.set_timesteps(timesteps=snake_case__    )
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**snake_case__    )

         UpperCAmelCase_										=					[1_00, 87, 50, 1, 0]
         UpperCAmelCase_										=					len(snake_case__    )

         with self.assertRaises(snake_case__ ,   msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''    ):
             scheduler.set_timesteps(num_inference_steps=snake_case__ ,   timesteps=snake_case__    )




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**snake_case__    )

         UpperCAmelCase_										=					[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             snake_case__ ,   msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,   ):
             scheduler.set_timesteps(timesteps=snake_case__    )











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[str]=5				)  ->							Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''				) == 1
    UpperCAmelCase_										=					torch.tensor(tokenizer.encode(__UpperCamelCase	,					add_special_tokens=__UpperCamelCase				)				).unsqueeze(0				)  # Batch size 1
    UpperCAmelCase_										=					model(__UpperCamelCase				)[0]  # The last hidden-state is the first element of the output tuple
    UpperCAmelCase_										=					(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    UpperCAmelCase_										=					logits[0, masked_index, :]
    UpperCAmelCase_										=					logits.softmax(dim=0				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					prob.topk(k=__UpperCamelCase	,					dim=0				)
    UpperCAmelCase_										=					''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCamelCase				)				)]				)
    UpperCAmelCase_										=					tokenizer.mask_token
    UpperCAmelCase_										=					[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''				)				):
        UpperCAmelCase_										=					predicted_token_bpe.replace('''\u2581'''	,					''' '''				)
        if " {0}".format(__UpperCamelCase				) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(__UpperCamelCase				)	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(__UpperCamelCase	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
    return topk_filled_outputs


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

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


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





