




'''simple docstring'''





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							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       torch.FloatTensor







class   _a							(		nn.Module				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",)   ,_SCREAMING_SNAKE_CASE=(64,)   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE="silu"   ,_SCREAMING_SNAKE_CASE=True   ,)				->     Tuple:
          super().__init__()
          _snake_case				= layers_per_block

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

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

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

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

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

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

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

          _snake_case				= False






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          _snake_case				= x
          _snake_case				= self.conv_in(_SCREAMING_SNAKE_CASE       )

          if self.training and self.gradient_checkpointing:

               def create_custom_forward(_SCREAMING_SNAKE_CASE       ):
                    def custom_forward(*_SCREAMING_SNAKE_CASE       ):
                         return module(*_SCREAMING_SNAKE_CASE       )

                    return custom_forward

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

          else:
               # down
               for down_block in self.down_blocks:
                    _snake_case				= down_block(_SCREAMING_SNAKE_CASE       )

               # middle
               _snake_case				= self.mid_block(_SCREAMING_SNAKE_CASE       )

          # post-process
          _snake_case				= self.conv_norm_out(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.conv_act(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.conv_out(_SCREAMING_SNAKE_CASE       )

          return sample







class   _a							(		nn.Module				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",)   ,_SCREAMING_SNAKE_CASE=(64,)   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE="silu"   ,_SCREAMING_SNAKE_CASE="group"   ,)				->     Any:
          super().__init__()
          _snake_case				= layers_per_block

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

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

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

          # mid
          _snake_case				= UNetMidBlockaD(
              in_channels=block_out_channels[-1]   ,resnet_eps=1e-6   ,resnet_act_fn=_SCREAMING_SNAKE_CASE   ,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=_SCREAMING_SNAKE_CASE   ,temb_channels=_SCREAMING_SNAKE_CASE   ,)

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

               _snake_case				= i == len(_SCREAMING_SNAKE_CASE       ) - 1

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

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

          _snake_case				= False






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     int:
          _snake_case				= z
          _snake_case				= self.conv_in(_SCREAMING_SNAKE_CASE       )

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

               def create_custom_forward(_SCREAMING_SNAKE_CASE       ):
                    def custom_forward(*_SCREAMING_SNAKE_CASE       ):
                         return module(*_SCREAMING_SNAKE_CASE       )

                    return custom_forward

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

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

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

               # up
               for up_block in self.up_blocks:
                    _snake_case				= up_block(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

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

          return sample







class   _a							(		nn.Module				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE="random"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True       )				->     List[str]:
          super().__init__()
          _snake_case				= n_e
          _snake_case				= vq_embed_dim
          _snake_case				= beta
          _snake_case				= legacy

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

          _snake_case				= remap
          if self.remap is not None:
               self.register_buffer("used"   ,torch.tensor(np.load(self.remap       )       )       )
               _snake_case				= self.used.shape[0]
               _snake_case				= unknown_index  # "random" or "extra" or integer
               if self.unknown_index == "extra":
                    _snake_case				= self.re_embed
                    _snake_case				= 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:
               _snake_case				= n_e

          _snake_case				= sane_index_shape






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






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






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Union[str, Any]:
          # reshape z -> (batch, height, width, channel) and flatten
          _snake_case				= z.permute(0   ,2   ,3   ,1       ).contiguous()
          _snake_case				= z.view(-1   ,self.vq_embed_dim       )

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

          _snake_case				= self.embedding(_SCREAMING_SNAKE_CASE       ).view(z.shape       )
          _snake_case				= None
          _snake_case				= None

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

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

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

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

          if self.sane_index_shape:
               _snake_case				= 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 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          # shape specifying (batch, height, width, channel)
          if self.remap is not None:
               _snake_case				= indices.reshape(shape[0]   ,-1       )  # add batch axis
               _snake_case				= self.unmap_to_all(_SCREAMING_SNAKE_CASE       )
               _snake_case				= indices.reshape(-1       )  # flatten again

          # get quantized latent vectors
          _snake_case				= self.embedding(_SCREAMING_SNAKE_CASE       )

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

          return z_q







class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=False       )				->     Any:
          _snake_case				= parameters
          _snake_case				,   _snake_case				= torch.chunk(_SCREAMING_SNAKE_CASE   ,2   ,dim=1       )
          _snake_case				= torch.clamp(self.logvar   ,-3_0.0   ,2_0.0       )
          _snake_case				= deterministic
          _snake_case				= torch.exp(0.5 * self.logvar       )
          _snake_case				= torch.exp(self.logvar       )
          if self.deterministic:
               _snake_case				= _snake_case				= torch.zeros_like(
                   self.mean   ,device=self.parameters.device   ,dtype=self.parameters.dtype       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE = None       )				->     torch.FloatTensor:
          # make sure sample is on the same device as the parameters and has same dtype
          _snake_case				= randn_tensor(
              self.mean.shape   ,generator=_SCREAMING_SNAKE_CASE   ,device=self.parameters.device   ,dtype=self.parameters.dtype       )
          _snake_case				= self.mean + self.std * sample
          return x






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE=None       )				->     Optional[Any]:
          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 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=[1, 2, 3]       )				->     List[Any]:
          if self.deterministic:
               return torch.Tensor([0.0]       )
          _snake_case				= np.log(2.0 * np.pi       )
          return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean   ,2       ) / self.var   ,dim=_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     int:
          return self.mean








'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase_		:     Union[str, Any]          =		logging.get_logger(__name__)


UpperCamelCase_		:     Optional[Any]          =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase_		:     Dict          =		{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase_		:     Tuple          =		{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_				:       int       							=		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_				:       Any       							=		["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_				:       str       							=		BartTokenizer






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE="replace"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="<unk>"   ,_SCREAMING_SNAKE_CASE="<pad>"   ,_SCREAMING_SNAKE_CASE="<mask>"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,tokenizer_file=_SCREAMING_SNAKE_CASE   ,errors=_SCREAMING_SNAKE_CASE   ,bos_token=_SCREAMING_SNAKE_CASE   ,eos_token=_SCREAMING_SNAKE_CASE   ,sep_token=_SCREAMING_SNAKE_CASE   ,cls_token=_SCREAMING_SNAKE_CASE   ,unk_token=_SCREAMING_SNAKE_CASE   ,pad_token=_SCREAMING_SNAKE_CASE   ,mask_token=_SCREAMING_SNAKE_CASE   ,add_prefix_space=_SCREAMING_SNAKE_CASE   ,trim_offsets=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
          if pre_tok_state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
               _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,pre_tok_state.pop("type"       )       )
               _snake_case				= add_prefix_space
               _snake_case				= pre_tok_class(**_SCREAMING_SNAKE_CASE       )

          _snake_case				= add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _snake_case				= "post_processor"
          _snake_case				= getattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          if tokenizer_component_instance:
               _snake_case				= json.loads(tokenizer_component_instance.__getstate__()       )

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                    _snake_case				= tuple(state["sep"]       )
               if "cls" in state:
                    _snake_case				= tuple(state["cls"]       )

               _snake_case				= False

               if state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
                    _snake_case				= add_prefix_space
                    _snake_case				= True

               if state.get("trim_offsets"   ,_SCREAMING_SNAKE_CASE       ) != trim_offsets:
                    _snake_case				= trim_offsets
                    _snake_case				= True

               if changes_to_apply:
                    _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,state.pop("type"       )       )
                    _snake_case				= component_class(**_SCREAMING_SNAKE_CASE       )
                    setattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     @property
     def 							_lowercase      (					self       )				->     str:
          if self._mask_token is None:
               if self.verbose:
                    logger.error("Using mask_token, but it is not set yet."       )
               return None
          return str(self._mask_token       )






     @mask_token.setter
     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          _snake_case				= AddedToken(_SCREAMING_SNAKE_CASE   ,lstrip=_SCREAMING_SNAKE_CASE   ,rstrip=_SCREAMING_SNAKE_CASE       ) if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) else value
          _snake_case				= value






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Tuple[str]:
          _snake_case				= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,name=_SCREAMING_SNAKE_CASE       )
          return tuple(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     Tuple:
          _snake_case				= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
               return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     List[int]:
          _snake_case				= [self.sep_token_id]
          _snake_case				= [self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep       ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]


