





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class   A							(						__lowercase    ):
      _snake_case				  =42

class   A							(						nn.Module    ):



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    Optional[Any]=3		,	_lowerCAmelCase:    str=3		,	_lowerCAmelCase:    Tuple=("DownEncoderBlock2D",)		,	_lowerCAmelCase:    Optional[int]=(64,)		,	_lowerCAmelCase:    List[Any]=2		,	_lowerCAmelCase:    Optional[Any]=32		,	_lowerCAmelCase:    List[Any]="silu"		,	_lowerCAmelCase:    str=True		,	)    ->     Tuple:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =layers_per_block

         UpperCAmelCase_           =torch.nn.Convad(
             _lowerCAmelCase		,	block_out_channels[0]		,	kernel_size=3		,	stride=1		,	padding=1		,	)

         UpperCAmelCase_           =None
         UpperCAmelCase_           =nn.ModuleList([]							)

         # down
         UpperCAmelCase_           =block_out_channels[0]
         for i, down_block_type in enumerate(_lowerCAmelCase							):
            UpperCAmelCase_           =output_channel
            UpperCAmelCase_           =block_out_channels[i]
            UpperCAmelCase_           =i == len(_lowerCAmelCase							) - 1

            UpperCAmelCase_           =get_down_block(
                _lowerCAmelCase		,	num_layers=self.layers_per_block		,	in_channels=_lowerCAmelCase		,	out_channels=_lowerCAmelCase		,	add_downsample=not is_final_block		,	resnet_eps=1e-6		,	downsample_padding=0		,	resnet_act_fn=_lowerCAmelCase		,	resnet_groups=_lowerCAmelCase		,	attention_head_dim=_lowerCAmelCase		,	temb_channels=_lowerCAmelCase		,	)
            self.down_blocks.append(_lowerCAmelCase							)

         # mid
         UpperCAmelCase_           =UNetMidBlockaD(
             in_channels=block_out_channels[-1]		,	resnet_eps=1e-6		,	resnet_act_fn=_lowerCAmelCase		,	output_scale_factor=1		,	resnet_time_scale_shift="default"		,	attention_head_dim=block_out_channels[-1]		,	resnet_groups=_lowerCAmelCase		,	temb_channels=_lowerCAmelCase		,	)

         # out
         UpperCAmelCase_           =nn.GroupNorm(num_channels=block_out_channels[-1]		,	num_groups=_lowerCAmelCase		,	eps=1e-6							)
         UpperCAmelCase_           =nn.SiLU()

         UpperCAmelCase_           =2 * out_channels if double_z else out_channels
         UpperCAmelCase_           =nn.Convad(block_out_channels[-1]		,	_lowerCAmelCase		,	3		,	padding=1							)

         UpperCAmelCase_           =False






      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    int							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =x
         UpperCAmelCase_           =self.conv_in(_lowerCAmelCase							)

         if self.training and self.gradient_checkpointing:

            def create_custom_forward(_lowerCAmelCase:    Optional[Any]							):
               def custom_forward(*_lowerCAmelCase:    Union[str, Any]							):
                  return module(*_lowerCAmelCase							)

               return custom_forward

            # down
            if is_torch_version(">="		,	"1.11.0"							):
               for down_block in self.down_blocks:
                  UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(
                      create_custom_forward(_lowerCAmelCase							)		,	_lowerCAmelCase		,	use_reentrant=_lowerCAmelCase							)
               # middle
               UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(
                   create_custom_forward(self.mid_block							)		,	_lowerCAmelCase		,	use_reentrant=_lowerCAmelCase							)
            else:
               for down_block in self.down_blocks:
                  UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(create_custom_forward(_lowerCAmelCase							)		,	_lowerCAmelCase							)
               # middle
               UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)		,	_lowerCAmelCase							)

         else:
            # down
            for down_block in self.down_blocks:
               UpperCAmelCase_           =down_block(_lowerCAmelCase							)

            # middle
            UpperCAmelCase_           =self.mid_block(_lowerCAmelCase							)

         # post-process
         UpperCAmelCase_           =self.conv_norm_out(_lowerCAmelCase							)
         UpperCAmelCase_           =self.conv_act(_lowerCAmelCase							)
         UpperCAmelCase_           =self.conv_out(_lowerCAmelCase							)

         return sample

class   A							(						nn.Module    ):



      def __init__(						self:    Union[str, Any]		,	_lowerCAmelCase:    Dict=3		,	_lowerCAmelCase:    List[str]=3		,	_lowerCAmelCase:    int=("UpDecoderBlock2D",)		,	_lowerCAmelCase:    Optional[int]=(64,)		,	_lowerCAmelCase:    Tuple=2		,	_lowerCAmelCase:    Dict=32		,	_lowerCAmelCase:    Tuple="silu"		,	_lowerCAmelCase:    str="group"		,	)    ->     Union[str, Any]:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =layers_per_block

         UpperCAmelCase_           =nn.Convad(
             _lowerCAmelCase		,	block_out_channels[-1]		,	kernel_size=3		,	stride=1		,	padding=1		,	)

         UpperCAmelCase_           =None
         UpperCAmelCase_           =nn.ModuleList([]							)

         UpperCAmelCase_           =in_channels if norm_type == "spatial" else None

         # mid
         UpperCAmelCase_           =UNetMidBlockaD(
             in_channels=block_out_channels[-1]		,	resnet_eps=1e-6		,	resnet_act_fn=_lowerCAmelCase		,	output_scale_factor=1		,	resnet_time_scale_shift="default" if norm_type == "group" else norm_type		,	attention_head_dim=block_out_channels[-1]		,	resnet_groups=_lowerCAmelCase		,	temb_channels=_lowerCAmelCase		,	)

         # up
         UpperCAmelCase_           =list(reversed(_lowerCAmelCase							)							)
         UpperCAmelCase_           =reversed_block_out_channels[0]
         for i, up_block_type in enumerate(_lowerCAmelCase							):
            UpperCAmelCase_           =output_channel
            UpperCAmelCase_           =reversed_block_out_channels[i]

            UpperCAmelCase_           =i == len(_lowerCAmelCase							) - 1

            UpperCAmelCase_           =get_up_block(
                _lowerCAmelCase		,	num_layers=self.layers_per_block + 1		,	in_channels=_lowerCAmelCase		,	out_channels=_lowerCAmelCase		,	prev_output_channel=_lowerCAmelCase		,	add_upsample=not is_final_block		,	resnet_eps=1e-6		,	resnet_act_fn=_lowerCAmelCase		,	resnet_groups=_lowerCAmelCase		,	attention_head_dim=_lowerCAmelCase		,	temb_channels=_lowerCAmelCase		,	resnet_time_scale_shift=_lowerCAmelCase		,	)
            self.up_blocks.append(_lowerCAmelCase							)
            UpperCAmelCase_           =output_channel

         # out
         if norm_type == "spatial":
            UpperCAmelCase_           =SpatialNorm(block_out_channels[0]		,	_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =nn.GroupNorm(num_channels=block_out_channels[0]		,	num_groups=_lowerCAmelCase		,	eps=1e-6							)
         UpperCAmelCase_           =nn.SiLU()
         UpperCAmelCase_           =nn.Convad(block_out_channels[0]		,	_lowerCAmelCase		,	3		,	padding=1							)

         UpperCAmelCase_           =False






      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Optional[int]=None							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =z
         UpperCAmelCase_           =self.conv_in(_lowerCAmelCase							)

         UpperCAmelCase_           =next(iter(self.up_blocks.parameters()							)							).dtype
         if self.training and self.gradient_checkpointing:

            def create_custom_forward(_lowerCAmelCase:    Dict							):
               def custom_forward(*_lowerCAmelCase:    Union[str, Any]							):
                  return module(*_lowerCAmelCase							)

               return custom_forward

            if is_torch_version(">="		,	"1.11.0"							):
               # middle
               UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(
                   create_custom_forward(self.mid_block							)		,	_lowerCAmelCase		,	_lowerCAmelCase		,	use_reentrant=_lowerCAmelCase							)
               UpperCAmelCase_           =sample.to(_lowerCAmelCase							)

               # up
               for up_block in self.up_blocks:
                  UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(
                      create_custom_forward(_lowerCAmelCase							)		,	_lowerCAmelCase		,	_lowerCAmelCase		,	use_reentrant=_lowerCAmelCase							)
            else:
               # middle
               UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(
                   create_custom_forward(self.mid_block							)		,	_lowerCAmelCase		,	_lowerCAmelCase							)
               UpperCAmelCase_           =sample.to(_lowerCAmelCase							)

               # up
               for up_block in self.up_blocks:
                  UpperCAmelCase_           =torch.utils.checkpoint.checkpoint(create_custom_forward(_lowerCAmelCase							)		,	_lowerCAmelCase		,	_lowerCAmelCase							)
         else:
            # middle
            UpperCAmelCase_           =self.mid_block(_lowerCAmelCase		,	_lowerCAmelCase							)
            UpperCAmelCase_           =sample.to(_lowerCAmelCase							)

            # up
            for up_block in self.up_blocks:
               UpperCAmelCase_           =up_block(_lowerCAmelCase		,	_lowerCAmelCase							)

        # post-process
         if latent_embeds is None:
            UpperCAmelCase_           =self.conv_norm_out(_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =self.conv_norm_out(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =self.conv_act(_lowerCAmelCase							)
         UpperCAmelCase_           =self.conv_out(_lowerCAmelCase							)

         return sample

class   A							(						nn.Module    ):



      def __init__(						self:    Tuple		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    Tuple		,	_lowerCAmelCase:    int=None		,	_lowerCAmelCase:    Union[str, Any]="random"		,	_lowerCAmelCase:    Optional[int]=False		,	_lowerCAmelCase:    Union[str, Any]=True							)    ->     str:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =n_e
         UpperCAmelCase_           =vq_embed_dim
         UpperCAmelCase_           =beta
         UpperCAmelCase_           =legacy

         UpperCAmelCase_           =nn.Embedding(self.n_e		,	self.vq_embed_dim							)
         self.embedding.weight.data.uniform_(-1.0 / self.n_e		,	1.0 / self.n_e							)

         UpperCAmelCase_           =remap
         if self.remap is not None:
            self.register_buffer("used"		,	torch.tensor(np.load(self.remap							)							)							)
            UpperCAmelCase_           =self.used.shape[0]
            UpperCAmelCase_           =unknown_index  # "random" or "extra" or integer
            if self.unknown_index == "extra":
               UpperCAmelCase_           =self.re_embed
               UpperCAmelCase_           =self.re_embed + 1
            print(
                F'Remapping {self.n_e} indices to {self.re_embed} indices. '
                F'Using {self.unknown_index} for unknown indices.'							)
         else:
            UpperCAmelCase_           =n_e

         UpperCAmelCase_           =sane_index_shape



      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    Any							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =inds.shape
         assert len(_lowerCAmelCase							) > 1
         UpperCAmelCase_           =inds.reshape(ishape[0]		,	-1							)
         UpperCAmelCase_           =self.used.to(_lowerCAmelCase							)
         UpperCAmelCase_           =(inds[:, :, None] == used[None, None, ...]).long()
         UpperCAmelCase_           =match.argmax(-1							)
         UpperCAmelCase_           =match.sum(2							) < 1
         if self.unknown_index == "random":
            UpperCAmelCase_           =torch.randint(0		,	self.re_embed		,	size=new[unknown].shape							).to(device=new.device							)
         else:
            UpperCAmelCase_           =self.unknown_index
         return new.reshape(_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    Tuple							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =inds.shape
         assert len(_lowerCAmelCase							) > 1
         UpperCAmelCase_           =inds.reshape(ishape[0]		,	-1							)
         UpperCAmelCase_           =self.used.to(_lowerCAmelCase							)
         if self.re_embed > self.used.shape[0]:  # extra token
            UpperCAmelCase_           =0  # simply set to zero
         UpperCAmelCase_           =torch.gather(used[None, :][inds.shape[0] * [0], :]		,	1		,	_lowerCAmelCase							)
         return back.reshape(_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    Tuple							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =z.permute(0		,	2		,	3		,	1							).contiguous()
         UpperCAmelCase_           =z.view(-1		,	self.vq_embed_dim							)

         # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
         UpperCAmelCase_           =torch.argmin(torch.cdist(_lowerCAmelCase		,	self.embedding.weight							)		,	dim=1							)

         UpperCAmelCase_           =self.embedding(_lowerCAmelCase							).view(z.shape							)
         UpperCAmelCase_           =None
         UpperCAmelCase_           =None

         # compute loss for embedding
         if not self.legacy:
            UpperCAmelCase_           =self.beta * torch.mean((z_q.detach() - z) ** 2							) + torch.mean((z_q - z.detach()) ** 2							)
         else:
            UpperCAmelCase_           =torch.mean((z_q.detach() - z) ** 2							) + self.beta * torch.mean((z_q - z.detach()) ** 2							)

         # preserve gradients
         UpperCAmelCase_           =z + (z_q - z).detach()

         # reshape back to match original input shape
         UpperCAmelCase_           =z_q.permute(0		,	3		,	1		,	2							).contiguous()

         if self.remap is not None:
            UpperCAmelCase_           =min_encoding_indices.reshape(z.shape[0]		,	-1							)  # add batch axis
            UpperCAmelCase_           =self.remap_to_used(_lowerCAmelCase							)
            UpperCAmelCase_           =min_encoding_indices.reshape(-1		,	1							)  # flatten

         if self.sane_index_shape:
            UpperCAmelCase_           =min_encoding_indices.reshape(z_q.shape[0]		,	z_q.shape[2]		,	z_q.shape[3]							)

         return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






      def 						lowerCAmelCase__     (						self:    List[str]		,	_lowerCAmelCase:    Dict		,	_lowerCAmelCase:    Dict							)    ->     Union[str, Any]:
         '''simple docstring'''

         if self.remap is not None:
            UpperCAmelCase_           =indices.reshape(shape[0]		,	-1							)  # add batch axis
            UpperCAmelCase_           =self.unmap_to_all(_lowerCAmelCase							)
            UpperCAmelCase_           =indices.reshape(-1							)  # flatten again

         # get quantized latent vectors
         UpperCAmelCase_           =self.embedding(_lowerCAmelCase							)

         if shape is not None:
            UpperCAmelCase_           =z_q.view(_lowerCAmelCase							)
            # reshape back to match original input shape
            UpperCAmelCase_           =z_q.permute(0		,	3		,	1		,	2							).contiguous()

         return z_q

class   A							(						__lowercase    ):



      def __init__(						self:    Any		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    Union[str, Any]=False							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =parameters
         UpperCAmelCase_    ,							UpperCAmelCase_           =torch.chunk(_lowerCAmelCase		,	2		,	dim=1							)
         UpperCAmelCase_           =torch.clamp(self.logvar		,	-30.0		,	20.0							)
         UpperCAmelCase_           =deterministic
         UpperCAmelCase_           =torch.exp(0.5 * self.logvar							)
         UpperCAmelCase_           =torch.exp(self.logvar							)
         if self.deterministic:
            UpperCAmelCase_           =UpperCAmelCase_           =torch.zeros_like(
                self.mean		,	device=self.parameters.device		,	dtype=self.parameters.dtype							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    Optional[torch.Generator] = None							)    ->     torch.FloatTensor:
         '''simple docstring'''

         UpperCAmelCase_           =randn_tensor(
             self.mean.shape		,	generator=_lowerCAmelCase		,	device=self.parameters.device		,	dtype=self.parameters.dtype							)
         UpperCAmelCase_           =self.mean + self.std * sample
         return x



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    str=None							)    ->     Tuple:
         '''simple docstring'''

         if self.deterministic:
            return torch.Tensor([0.0]							)
         else:
            if other is None:
               return 0.5 * torch.sum(torch.pow(self.mean		,	2							) + self.var - 1.0 - self.logvar		,	dim=[1, 2, 3]							)
            else:
               return 0.5 * torch.sum(
                   torch.pow(self.mean - other.mean		,	2							) / other.var
                   + self.var / other.var
                   - 1.0
                   - self.logvar
                   + other.logvar		,	dim=[1, 2, 3]		,	)



      def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    List[Any]=[1, 2, 3]							)    ->     Union[str, Any]:
         '''simple docstring'''

         if self.deterministic:
            return torch.Tensor([0.0]							)
         UpperCAmelCase_           =np.log(2.0 * np.pi							)
         return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean		,	2							) / self.var		,	dim=_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     str:
         '''simple docstring'''

         return self.mean











"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

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


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))
