





'''simple docstring'''

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=5      )						->					int:

   '''simple docstring'''







   assert masked_input.count("""<mask>"""      ) == 1
   __SCREAMING_SNAKE_CASE      							=       torch.tensor(tokenizer.encode(__UpperCAmelCase							,	add_special_tokens=__UpperCAmelCase      )      ).unsqueeze(0      )  # Batch size 1
   __SCREAMING_SNAKE_CASE      							=       model(__UpperCAmelCase      )[0]  # The last hidden-state is the first element of the output tuple
   __SCREAMING_SNAKE_CASE      							=       (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
   __SCREAMING_SNAKE_CASE      							=       logits[0, masked_index, :]
   __SCREAMING_SNAKE_CASE      							=       logits.softmax(dim=0      )
   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       prob.topk(k=__UpperCAmelCase							,	dim=0      )
   __SCREAMING_SNAKE_CASE      							=       """ """.join(
       [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(__UpperCAmelCase      )      )]      )
   __SCREAMING_SNAKE_CASE      							=       tokenizer.mask_token
   __SCREAMING_SNAKE_CASE      							=       []
   for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """      )      ):
      __SCREAMING_SNAKE_CASE      							=       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


a        =						CamembertTokenizer.from_pretrained("camembert-base")
a        =						CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

a        =						"Le camembert est <mask> :)"







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












'''simple docstring'''

import flax.linen as nn
import jax
import jax.numpy as jnp







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       hidden_states.shape
      __SCREAMING_SNAKE_CASE      							=       jax.image.resize(
          lowerCamelCase				,shape=(batch, height * 2, width * 2, channels)				,method="""nearest"""				,)
      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(2, 2)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : int	      =	None
   __UpperCamelCase   : float	      =	0.0
   __UpperCamelCase   : bool	      =	None
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.in_channels if self.out_channels is None else self.out_channels

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       nn.Dense(lowerCamelCase				,dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Dropout(self.dropout_prob    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      __SCREAMING_SNAKE_CASE      							=       None
      if use_nin_shortcut:
         __SCREAMING_SNAKE_CASE      							=       nn.Conv(
             lowerCamelCase				,kernel_size=(1, 1)				,strides=(1, 1)				,padding="""VALID"""				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       hidden_states
      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.time_emb_proj(nn.swish(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       jnp.expand_dims(jnp.expand_dims(lowerCamelCase				,1    )				,1    )
      __SCREAMING_SNAKE_CASE      							=       hidden_states + temb

      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dropout(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      if self.conv_shortcut is not None:
         __SCREAMING_SNAKE_CASE      							=       self.conv_shortcut(lowerCamelCase    )

      return hidden_states + residual





