



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        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:torch.FloatTensor




class        snake_case__    (nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase=3 ,       __lowercase=3 ,       __lowercase=("DownEncoderBlock2D",) ,       __lowercase=(6_4,) ,       __lowercase=2 ,       __lowercase=3_2 ,       __lowercase="silu" ,       __lowercase=True ,       ) ->				Union[str, Any]:
									"""simple docstring"""




									super().__init__()
									a__       :						Any							=  layers_per_block

									a__       :						Optional[Any]							=  torch.nn.Convad(
									    __lowercase ,       block_out_channels[0] ,       kernel_size=3 ,       stride=1 ,       padding=1 ,       )

									a__       :						Optional[Any]							=  None
									a__       :						List[Any]							=  nn.ModuleList([]  )

									# down
									a__       :						Optional[Any]							=  block_out_channels[0]
									for i, down_block_type in enumerate(__lowercase  ):
												a__       :						Optional[Any]							=  output_channel
												a__       :						Optional[int]							=  block_out_channels[i]
												a__       :						Union[str, Any]							=  i == len(__lowercase  ) - 1

												a__       :						List[str]							=  get_down_block(
												    __lowercase ,       num_layers=self.layers_per_block ,       in_channels=__lowercase ,       out_channels=__lowercase ,       add_downsample=not is_final_block ,       resnet_eps=1E-6 ,       downsample_padding=0 ,       resnet_act_fn=__lowercase ,       resnet_groups=__lowercase ,       attention_head_dim=__lowercase ,       temb_channels=__lowercase ,       )
												self.down_blocks.append(__lowercase  )

									# mid
									a__       :						Dict							=  UNetMidBlockaD(
									    in_channels=block_out_channels[-1] ,       resnet_eps=1E-6 ,       resnet_act_fn=__lowercase ,       output_scale_factor=1 ,       resnet_time_scale_shift="""default""" ,       attention_head_dim=block_out_channels[-1] ,       resnet_groups=__lowercase ,       temb_channels=__lowercase ,       )

									# out
									a__       :						Tuple							=  nn.GroupNorm(num_channels=block_out_channels[-1] ,       num_groups=__lowercase ,       eps=1E-6  )
									a__       :						List[Any]							=  nn.SiLU()

									a__       :						Union[str, Any]							=  2 * out_channels if double_z else out_channels
									a__       :						Tuple							=  nn.Convad(block_out_channels[-1] ,       __lowercase ,       3 ,       padding=1  )

									a__       :						List[str]							=  False

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						str							=  x
									a__       :						str							=  self.conv_in(__lowercase  )

									if self.training and self.gradient_checkpointing:

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

															return custom_forward

												# down
												if is_torch_version(""">=""" ,       """1.11.0"""  ):
															for down_block in self.down_blocks:
																		a__       :						Tuple							=  torch.utils.checkpoint.checkpoint(
																		    create_custom_forward(__lowercase  ) ,       __lowercase ,       use_reentrant=__lowercase  )
															# middle
															a__       :						int							=  torch.utils.checkpoint.checkpoint(
															    create_custom_forward(self.mid_block  ) ,       __lowercase ,       use_reentrant=__lowercase  )
												else:
															for down_block in self.down_blocks:
																		a__       :						List[str]							=  torch.utils.checkpoint.checkpoint(create_custom_forward(__lowercase  ) ,       __lowercase  )
															# middle
															a__       :						str							=  torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block  ) ,       __lowercase  )

									else:
												# down
												for down_block in self.down_blocks:
															a__       :						str							=  down_block(__lowercase  )

												# middle
												a__       :						Tuple							=  self.mid_block(__lowercase  )

									# post-process
									a__       :						Optional[int]							=  self.conv_norm_out(__lowercase  )
									a__       :						List[str]							=  self.conv_act(__lowercase  )
									a__       :						List[Any]							=  self.conv_out(__lowercase  )

									return sample




class        snake_case__    (nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase=3 ,       __lowercase=3 ,       __lowercase=("UpDecoderBlock2D",) ,       __lowercase=(6_4,) ,       __lowercase=2 ,       __lowercase=3_2 ,       __lowercase="silu" ,       __lowercase="group" ,       ) ->				str:
									"""simple docstring"""




									super().__init__()
									a__       :						Optional[Any]							=  layers_per_block

									a__       :						Any							=  nn.Convad(
									    __lowercase ,       block_out_channels[-1] ,       kernel_size=3 ,       stride=1 ,       padding=1 ,       )

									a__       :						Optional[Any]							=  None
									a__       :						Dict							=  nn.ModuleList([]  )

									a__       :						List[str]							=  in_channels if norm_type == """spatial""" else None

									# mid
									a__       :						Optional[Any]							=  UNetMidBlockaD(
									    in_channels=block_out_channels[-1] ,       resnet_eps=1E-6 ,       resnet_act_fn=__lowercase ,       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=__lowercase ,       temb_channels=__lowercase ,       )

									# up
									a__       :						str							=  list(reversed(__lowercase  )  )
									a__       :						Dict							=  reversed_block_out_channels[0]
									for i, up_block_type in enumerate(__lowercase  ):
												a__       :						Any							=  output_channel
												a__       :						Any							=  reversed_block_out_channels[i]

												a__       :						List[Any]							=  i == len(__lowercase  ) - 1

												a__       :						Any							=  get_up_block(
												    __lowercase ,       num_layers=self.layers_per_block + 1 ,       in_channels=__lowercase ,       out_channels=__lowercase ,       prev_output_channel=__lowercase ,       add_upsample=not is_final_block ,       resnet_eps=1E-6 ,       resnet_act_fn=__lowercase ,       resnet_groups=__lowercase ,       attention_head_dim=__lowercase ,       temb_channels=__lowercase ,       resnet_time_scale_shift=__lowercase ,       )
												self.up_blocks.append(__lowercase  )
												a__       :						List[str]							=  output_channel

									# out
									if norm_type == "spatial":
												a__       :						Union[str, Any]							=  SpatialNorm(block_out_channels[0] ,       __lowercase  )
									else:
												a__       :						Tuple							=  nn.GroupNorm(num_channels=block_out_channels[0] ,       num_groups=__lowercase ,       eps=1E-6  )
									a__       :						Any							=  nn.SiLU()
									a__       :						Dict							=  nn.Convad(block_out_channels[0] ,       __lowercase ,       3 ,       padding=1  )

									a__       :						int							=  False

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=None  ) ->				Any:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  z
									a__       :						Dict							=  self.conv_in(__lowercase  )

									a__       :						List[Any]							=  next(iter(self.up_blocks.parameters()  )  ).dtype
									if self.training and self.gradient_checkpointing:

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

															return custom_forward

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

															# up
															for up_block in self.up_blocks:
																		a__       :						Tuple							=  torch.utils.checkpoint.checkpoint(
																		    create_custom_forward(__lowercase  ) ,       __lowercase ,       __lowercase ,       use_reentrant=__lowercase  )
												else:
															# middle
															a__       :						Optional[int]							=  torch.utils.checkpoint.checkpoint(
															    create_custom_forward(self.mid_block  ) ,       __lowercase ,       __lowercase  )
															a__       :						Dict							=  sample.to(__lowercase  )

															# up
															for up_block in self.up_blocks:
																		a__       :						Union[str, Any]							=  torch.utils.checkpoint.checkpoint(create_custom_forward(__lowercase  ) ,       __lowercase ,       __lowercase  )
									else:
												# middle
												a__       :						str							=  self.mid_block(__lowercase ,       __lowercase  )
												a__       :						int							=  sample.to(__lowercase  )

												# up
												for up_block in self.up_blocks:
															a__       :						List[Any]							=  up_block(__lowercase ,       __lowercase  )

        # post-process
									if latent_embeds is None:
												a__       :						int							=  self.conv_norm_out(__lowercase  )
									else:
												a__       :						Dict							=  self.conv_norm_out(__lowercase ,       __lowercase  )
									a__       :						Dict							=  self.conv_act(__lowercase  )
									a__       :						List[Any]							=  self.conv_out(__lowercase  )

									return sample




class        snake_case__    (nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase=None ,       __lowercase="random" ,       __lowercase=False ,       __lowercase=True  ) ->				Tuple:
									"""simple docstring"""




									super().__init__()
									a__       :						Any							=  n_e
									a__       :						Optional[Any]							=  vq_embed_dim
									a__       :						Tuple							=  beta
									a__       :						List[str]							=  legacy

									a__       :						List[str]							=  nn.Embedding(self.n_e ,       self.vq_embed_dim  )
									self.embedding.weight.data.uniform_(-1.0 / self.n_e ,       1.0 / self.n_e  )

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

									a__       :						Optional[Any]							=  sane_index_shape

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						List[Any]							=  inds.shape
									assert len(__lowercase  ) > 1
									a__       :						Dict							=  inds.reshape(ishape[0] ,       -1  )
									a__       :						List[str]							=  self.used.to(__lowercase  )
									if self.re_embed > self.used.shape[0]:  # extra token
												a__       :						Tuple							=  0  # simply set to zero
									a__       :						Union[str, Any]							=  torch.gather(used[None, :][inds.shape[0] * [0], :] ,       1 ,       __lowercase  )
									return back.reshape(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[int]							=  z.permute(0 ,       2 ,       3 ,       1  ).contiguous()
									a__       :						Optional[Any]							=  z.view(-1 ,       self.vq_embed_dim  )

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

									a__       :						Optional[int]							=  self.embedding(__lowercase  ).view(z.shape  )
									a__       :						int							=  None
									a__       :						List[str]							=  None

									# compute loss for embedding
									if not self.legacy:
												a__       :						int							=  self.beta * torch.mean((z_q.detach() - z) ** 2  ) + torch.mean((z_q - z.detach()) ** 2  )
									else:
												a__       :						List[Any]							=  torch.mean((z_q.detach() - z) ** 2  ) + self.beta * torch.mean((z_q - z.detach()) ** 2  )

									# preserve gradients
									a__       :						str							=  z + (z_q - z).detach()

									# reshape back to match original input shape
									a__       :						List[str]							=  z_q.permute(0 ,       3 ,       1 ,       2  ).contiguous()

									if self.remap is not None:
												a__       :						Tuple							=  min_encoding_indices.reshape(z.shape[0] ,       -1  )  # add batch axis
												a__       :						List[str]							=  self.remap_to_used(__lowercase  )
												a__       :						str							=  min_encoding_indices.reshape(-1 ,       1  )  # flatten

									if self.sane_index_shape:
												a__       :						Any							=  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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									if self.remap is not None:
												a__       :						Union[str, Any]							=  indices.reshape(shape[0] ,       -1  )  # add batch axis
												a__       :						List[Any]							=  self.unmap_to_all(__lowercase  )
												a__       :						str							=  indices.reshape(-1  )  # flatten again

									# get quantized latent vectors
									a__       :						str							=  self.embedding(__lowercase  )

									if shape is not None:
												a__       :						Union[str, Any]							=  z_q.view(__lowercase  )
												# reshape back to match original input shape
												a__       :						str							=  z_q.permute(0 ,       3 ,       1 ,       2  ).contiguous()

									return z_q




class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase=False  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Optional[Any]							=  parameters
									a__	,						a__       :						List[Any]							=  torch.chunk(__lowercase ,       2 ,       dim=1  )
									a__       :						Any							=  torch.clamp(self.logvar ,       -3_0.0 ,       2_0.0  )
									a__       :						Optional[int]							=  deterministic
									a__       :						int							=  torch.exp(0.5 * self.logvar  )
									a__       :						Any							=  torch.exp(self.logvar  )
									if self.deterministic:
												a__       :						List[str]							=  torch.zeros_like(
												    self.mean ,       device=self.parameters.device ,       dtype=self.parameters.dtype  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase = None  ) ->				torch.FloatTensor:
									"""simple docstring"""




									a__       :						Tuple							=  randn_tensor(
									    self.mean.shape ,       generator=__lowercase ,       device=self.parameters.device ,       dtype=self.parameters.dtype  )
									a__       :						Tuple							=  self.mean + self.std * sample
									return x

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=None  ) ->				Optional[int]:
									"""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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=[1, 2, 3]  ) ->				Union[str, Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									return self.mean









import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

_lowercase		:				List[str]      		=logging.get_logger(__name__)


_lowercase		:				List[str]      		={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase		:				Optional[Any]      		={
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

_lowercase		:				int      		={"facebook/blenderbot-3B": 128}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowerCAmelCase_					(  )							->	Optional[int]:







			"""simple docstring"""
			a__       :						List[str]							=  (
			    list(range(ord("""!""")						,       ord("""~""") + 1)) + list(range(ord("""¡""")						,       ord("""¬""") + 1)) + list(range(ord("""®""")						,       ord("""ÿ""") + 1))
			)
			a__       :						Optional[Any]							=  bs[:]
			a__       :						List[Any]							=  0
			for b in range(2**8):
						if b not in bs:
									bs.append(_lowercase)
									cs.append(2**8 + n)
									n += 1
			a__       :						Tuple							=  [chr(_lowercase) for n in cs]
			return dict(zip(_lowercase						,       _lowercase))

def      lowerCAmelCase_					(  _lowercase    :       Tuple)							->	List[str]:







			"""simple docstring"""
			a__       :						int							=  set()
			a__       :						Tuple							=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char))
						a__       :						Any							=  char
			return pairs





class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					VOCAB_FILES_NAMES
						__lowerCAmelCase					:int      =					PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase					:int      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase					:Optional[Any]      =					["input_ids", "attention_mask"]

						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase="replace" ,       __lowercase="<s>" ,       __lowercase="</s>" ,       __lowercase="</s>" ,       __lowercase="<s>" ,       __lowercase="<unk>" ,       __lowercase="<pad>" ,       __lowercase="<mask>" ,       __lowercase=False ,       **__lowercase ,       ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Any							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else bos_token
									a__       :						Optional[Any]							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else eos_token
									a__       :						Tuple							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else sep_token
									a__       :						Any							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else cls_token
									a__       :						List[Any]							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else unk_token
									a__       :						str							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									a__       :						List[str]							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else mask_token

									super().__init__(
									    errors=__lowercase ,       bos_token=__lowercase ,       eos_token=__lowercase ,       unk_token=__lowercase ,       sep_token=__lowercase ,       cls_token=__lowercase ,       pad_token=__lowercase ,       mask_token=__lowercase ,       add_prefix_space=__lowercase ,       **__lowercase ,       )

									with open(__lowercase ,       encoding="""utf-8"""  ) as vocab_handle:
												a__       :						str							=  json.load(__lowercase  )
									a__       :						Dict							=  {v: k for k, v in self.encoder.items()}
									a__       :						Any							=  errors  # how to handle errors in decoding
									a__       :						Union[str, Any]							=  bytes_to_unicode()
									a__       :						Optional[Any]							=  {v: k for k, v in self.byte_encoder.items()}
									with open(__lowercase ,       encoding="""utf-8"""  ) as merges_handle:
												a__       :						List[Any]							=  merges_handle.read().split("""\n"""  )[1:-1]
									a__       :						Union[str, Any]							=  [tuple(merge.split()  ) for merge in bpe_merges]
									a__       :						str							=  dict(zip(__lowercase ,       range(len(__lowercase  )  )  )  )
									a__       :						Optional[Any]							=  {}
									a__       :						Optional[Any]							=  add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									a__       :						Union[str, Any]							=  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""  )

						@property
						# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									return len(self.encoder  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									return dict(self.encoder ,       **self.added_tokens_encoder  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									if token in self.cache:
												return self.cache[token]
									a__       :						List[Any]							=  tuple(__lowercase  )
									a__       :						Optional[int]							=  get_pairs(__lowercase  )

									if not pairs:
												return token

									while True:
												a__       :						List[Any]							=  min(__lowercase ,       key=lambda __lowercase  : self.bpe_ranks.get(__lowercase ,       float("""inf"""  )  )  )
												if bigram not in self.bpe_ranks:
															break
												a__	,						a__       :						Dict							=  bigram
												a__       :						List[Any]							=  []
												a__       :						int							=  0
												while i < len(__lowercase  ):
															try:
																		a__       :						str							=  word.index(__lowercase ,       __lowercase  )
															except ValueError:
																		new_word.extend(word[i:]  )
																		break
															else:
																		new_word.extend(word[i:j]  )
																		a__       :						Optional[int]							=  j

															if word[i] == first and i < len(__lowercase  ) - 1 and word[i + 1] == second:
																		new_word.append(first + second  )
																		i += 2
															else:
																		new_word.append(word[i]  )
																		i += 1
												a__       :						List[Any]							=  tuple(__lowercase  )
												a__       :						Any							=  new_word
												if len(__lowercase  ) == 1:
															break
												else:
															a__       :						List[Any]							=  get_pairs(__lowercase  )
									a__       :						Optional[Any]							=  """ """.join(__lowercase  )
									a__       :						Tuple							=  word
									return word

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						int							=  []
									for token in re.findall(self.pat ,       __lowercase  ):
												a__       :						List[Any]							=  """""".join(
												    self.byte_encoder[b] for b in token.encode("""utf-8"""  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase  ).split(""" """  )  )
									return bpe_tokens

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									return self.encoder.get(__lowercase ,       self.encoder.get(self.unk_token  )  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									return self.decoder.get(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  """""".join(__lowercase  )
									a__       :						Tuple							=  bytearray([self.byte_decoder[c] for c in text]  ).decode("""utf-8""" ,       errors=self.errors  )
									return text

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				Tuple[str]:
									"""simple docstring"""




									if not os.path.isdir(__lowercase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
									a__       :						int							=  os.path.join(
									    __lowercase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
									a__       :						Optional[Any]							=  os.path.join(
									    __lowercase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

									with open(__lowercase ,       """w""" ,       encoding="""utf-8"""  ) as f:
												f.write(json.dumps(self.encoder ,       indent=2 ,       sort_keys=__lowercase ,       ensure_ascii=__lowercase  ) + """\n"""  )

									a__       :						str							=  0
									with open(__lowercase ,       """w""" ,       encoding="""utf-8"""  ) as writer:
												writer.write("""#version: 0.2\n"""  )
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,       key=lambda __lowercase  : kv[1]  ):
															if index != token_index:
																		logger.warning(
																		    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																		    """ Please check that the tokenizer is not corrupted!"""  )
																		a__       :						Tuple							=  token_index
															writer.write(""" """.join(__lowercase  ) + """\n"""  )
															index += 1

									return vocab_file, merge_file

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None ,       __lowercase = False  ) ->				List[int]:
									"""simple docstring"""




									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__lowercase ,       token_ids_a=__lowercase ,       already_has_special_tokens=__lowercase  )

									if token_ids_a is None:
												return [1] + ([0] * len(__lowercase  )) + [1]
									return [1] + ([0] * len(__lowercase  )) + [1, 1] + ([0] * len(__lowercase  )) + [1]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[int]:
									"""simple docstring"""




									a__       :						Any							=  [self.sep_token_id]
									a__       :						List[Any]							=  [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]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=False ,       **__lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Tuple							=  kwargs.pop("""add_prefix_space""" ,       self.add_prefix_space  )
									if (is_split_into_words or add_prefix_space) and (len(__lowercase  ) > 0 and not text[0].isspace()):
												a__       :						Union[str, Any]							=  """ """ + text
									return (text, kwargs)

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[str]:
									"""simple docstring"""




									return token_ids_a + [self.eos_token_id]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[int]:
									"""simple docstring"""




									a__       :						List[Any]							=  []
									for is_user, text in conversation.iter_texts():
												if is_user:
															# We need to space prefix as it's being done within blenderbot
															inputs.append(""" """ + text  )
												else:
															# Generated responses should contain them already.
															inputs.append(__lowercase  )

									a__       :						Optional[int]							=  """  """.join(__lowercase  )
									a__       :						Any							=  self.encode(__lowercase  )
									if len(__lowercase  ) > self.model_max_length:
												a__       :						List[str]							=  input_ids[-self.model_max_length :]
												logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''  )
									return input_ids




