


'''simple docstring'''



from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin



@dataclass
class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			torch.FloatTensor





class      a    (		_a     ,  _a	):


     """simple docstring"""






     @register_to_config
     def __init__(    self					:		str							,			snake_case					:		int = 32							,			snake_case					:		int = 64							,			snake_case					:		int = 20							,			snake_case					:		int = 768							,			snake_case					:		Tuple=77							,			snake_case					:		List[Any]=4							,			snake_case					:		float = 0.0							,			snake_case					:		str = "silu"							,			snake_case					:		Optional[str] = None							,			snake_case					:		Optional[str] = None							,			snake_case					:		Optional[str] = "linear"							,			snake_case					:		Optional[str] = "prd"							,			snake_case					:		Optional[int] = None							,			snake_case					:		Optional[int] = None							,			snake_case					:		Optional[int] = None							,			)						->							List[str]:
          super().__init__()
          __UpperCAmelCase     :			List[Any]  							=      num_attention_heads
          __UpperCAmelCase     :			List[str]  							=      attention_head_dim
          __UpperCAmelCase     :			int  							=      num_attention_heads * attention_head_dim
          __UpperCAmelCase     :			List[Any]  							=      additional_embeddings

          __UpperCAmelCase     :			Any  							=      time_embed_dim or inner_dim
          __UpperCAmelCase     :			Any  							=      embedding_proj_dim or embedding_dim
          __UpperCAmelCase     :			Union[str, Any]  							=      clip_embed_dim or embedding_dim

          __UpperCAmelCase     :			List[Any]  							=      Timesteps(snake_case							,			snake_case							,			0      )
          __UpperCAmelCase     :			Optional[Any]  							=      TimestepEmbedding(snake_case							,			snake_case							,			out_dim=snake_case							,			act_fn=snake_case      )

          __UpperCAmelCase     :			str  							=      nn.Linear(snake_case							,			snake_case      )

          if embedding_proj_norm_type is None:
               __UpperCAmelCase     :			str  							=      None
          elif embedding_proj_norm_type == "layer":
               __UpperCAmelCase     :			str  							=      nn.LayerNorm(snake_case      )
          else:
               raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'      )

          __UpperCAmelCase     :			List[Any]  							=      nn.Linear(snake_case							,			snake_case      )

          if encoder_hid_proj_type is None:
               __UpperCAmelCase     :			Union[str, Any]  							=      None
          elif encoder_hid_proj_type == "linear":
               __UpperCAmelCase     :			Any  							=      nn.Linear(snake_case							,			snake_case      )
          else:
               raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'      )

          __UpperCAmelCase     :			Dict  							=      nn.Parameter(torch.zeros(1							,			num_embeddings + additional_embeddings							,			snake_case      )      )

          if added_emb_type == "prd":
               __UpperCAmelCase     :			Any  							=      nn.Parameter(torch.zeros(1							,			1							,			snake_case      )      )
          elif added_emb_type is None:
               __UpperCAmelCase     :			List[Any]  							=      None
          else:
               raise ValueError(
                   f'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'      )

          __UpperCAmelCase     :			Optional[int]  							=      nn.ModuleList(
              [
                  BasicTransformerBlock(
                      snake_case							,			snake_case							,			snake_case							,			dropout=snake_case							,			activation_fn='''gelu'''							,			attention_bias=snake_case							,			)
                  for d in range(snake_case      )
              ]      )

          if norm_in_type == "layer":
               __UpperCAmelCase     :			Tuple  							=      nn.LayerNorm(snake_case      )
          elif norm_in_type is None:
               __UpperCAmelCase     :			List[Any]  							=      None
          else:
               raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.'      )

          __UpperCAmelCase     :			Dict  							=      nn.LayerNorm(snake_case      )

          __UpperCAmelCase     :			Any  							=      nn.Linear(snake_case							,			snake_case      )

          __UpperCAmelCase     :			Any  							=      torch.full(
              [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]							,			-1_0000.0      )
          causal_attention_mask.triu_(1      )
          __UpperCAmelCase     :			str  							=      causal_attention_mask[None, ...]
          self.register_buffer('''causal_attention_mask'''							,			snake_case							,			persistent=snake_case      )

          __UpperCAmelCase     :			Tuple  							=      nn.Parameter(torch.zeros(1							,			snake_case      )      )
          __UpperCAmelCase     :			List[str]  							=      nn.Parameter(torch.zeros(1							,			snake_case      )      )

     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 			lowerCamelCase__			(    self					:		int      )						->							Dict[str, AttentionProcessor]:
          __UpperCAmelCase     :			Optional[Any]  							=      {}

          def fn_recursive_add_processors(snake_case					:		str							,			snake_case					:		torch.nn.Module							,			snake_case					:		Dict[str, AttentionProcessor]      ):
               if hasattr(snake_case							,			'''set_processor'''      ):
                    __UpperCAmelCase     :			Union[str, Any]  							=      module.processor

               for sub_name, child in module.named_children():
                    fn_recursive_add_processors(f'{name}.{sub_name}'							,			snake_case							,			snake_case      )

               return processors

          for name, module in self.named_children():
               fn_recursive_add_processors(snake_case							,			snake_case							,			snake_case      )

          return processors

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Union[AttentionProcessor, Dict[str, AttentionProcessor]]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      len(self.attn_processors.keys()      )

          if isinstance(snake_case							,			snake_case      ) and len(snake_case      ) != count:
               raise ValueError(
                   f'A dict of processors was passed, but the number of processors {len(snake_case      )} does not match the'
                   f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'      )

          def fn_recursive_attn_processor(snake_case					:		str							,			snake_case					:		torch.nn.Module							,			snake_case					:		int      ):
               if hasattr(snake_case							,			'''set_processor'''      ):
                    if not isinstance(snake_case							,			snake_case      ):
                         module.set_processor(snake_case      )
                    else:
                         module.set_processor(processor.pop(f'{name}.processor'      )      )

               for sub_name, child in module.named_children():
                    fn_recursive_attn_processor(f'{name}.{sub_name}'							,			snake_case							,			snake_case      )

          for name, module in self.named_children():
               fn_recursive_attn_processor(snake_case							,			snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		str      )						->							Tuple:
          self.set_attn_processor(AttnProcessor()      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		List[Any]							,			snake_case					:		Union[torch.Tensor, float, int]							,			snake_case					:		torch.FloatTensor							,			snake_case					:		Optional[torch.FloatTensor] = None							,			snake_case					:		Optional[torch.BoolTensor] = None							,			snake_case					:		bool = True							,			)						->							List[Any]:
          __UpperCAmelCase     :			Any  							=      hidden_states.shape[0]

          __UpperCAmelCase     :			Optional[int]  							=      timestep
          if not torch.is_tensor(snake_case      ):
               __UpperCAmelCase     :			str  							=      torch.tensor([timesteps]							,			dtype=torch.long							,			device=hidden_states.device      )
          elif torch.is_tensor(snake_case      ) and len(timesteps.shape      ) == 0:
               __UpperCAmelCase     :			Any  							=      timesteps[None].to(hidden_states.device      )

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          __UpperCAmelCase     :			Optional[int]  							=      timesteps * torch.ones(snake_case							,			dtype=timesteps.dtype							,			device=timesteps.device      )

          __UpperCAmelCase     :			Tuple  							=      self.time_proj(snake_case      )

          # timesteps does not contain any weights and will always return f32 tensors
          # but time_embedding might be fp16, so we need to cast here.
          __UpperCAmelCase     :			int  							=      timesteps_projected.to(dtype=self.dtype      )
          __UpperCAmelCase     :			Optional[int]  							=      self.time_embedding(snake_case      )

          if self.embedding_proj_norm is not None:
               __UpperCAmelCase     :			Optional[Any]  							=      self.embedding_proj_norm(snake_case      )

          __UpperCAmelCase     :			str  							=      self.embedding_proj(snake_case      )
          if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
               __UpperCAmelCase     :			Dict  							=      self.encoder_hidden_states_proj(snake_case      )
          elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
               raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''      )

          __UpperCAmelCase     :			Optional[int]  							=      self.proj_in(snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.positional_embedding.to(hidden_states.dtype      )

          __UpperCAmelCase     :			Union[str, Any]  							=      []
          __UpperCAmelCase     :			Optional[Any]  							=      0

          if encoder_hidden_states is not None:
               additional_embeds.append(snake_case      )
               additional_embeddings_len += encoder_hidden_states.shape[1]

          if len(proj_embeddings.shape      ) == 2:
               __UpperCAmelCase     :			Optional[int]  							=      proj_embeddings[:, None, :]

          if len(hidden_states.shape      ) == 2:
               __UpperCAmelCase     :			Union[str, Any]  							=      hidden_states[:, None, :]

          __UpperCAmelCase     :			Union[str, Any]  							=      additional_embeds + [
              proj_embeddings,
              time_embeddings[:, None, :],
              hidden_states,
          ]

          if self.prd_embedding is not None:
               __UpperCAmelCase     :			Any  							=      self.prd_embedding.to(hidden_states.dtype      ).expand(snake_case							,			-1							,			-1      )
               additional_embeds.append(snake_case      )

          __UpperCAmelCase     :			Dict  							=      torch.cat(
              snake_case							,			dim=1							,			)

          # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
          __UpperCAmelCase     :			str  							=      additional_embeddings_len + proj_embeddings.shape[1] + 1
          if positional_embeddings.shape[1] < hidden_states.shape[1]:
               __UpperCAmelCase     :			Union[str, Any]  							=      F.pad(
                   snake_case							,			(
                       0,
                       0,
                       additional_embeddings_len,
                       self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                   )							,			value=0.0							,			)

          __UpperCAmelCase     :			Optional[int]  							=      hidden_states + positional_embeddings

          if attention_mask is not None:
               __UpperCAmelCase     :			List[Any]  							=      (1 - attention_mask.to(hidden_states.dtype      )) * -1_0000.0
               __UpperCAmelCase     :			str  							=      F.pad(snake_case							,			(0, self.additional_embeddings)							,			value=0.0      )
               __UpperCAmelCase     :			Optional[int]  							=      (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
               __UpperCAmelCase     :			Optional[int]  							=      attention_mask.repeat_interleave(self.config.num_attention_heads							,			dim=0      )

          if self.norm_in is not None:
               __UpperCAmelCase     :			str  							=      self.norm_in(snake_case      )

          for block in self.transformer_blocks:
               __UpperCAmelCase     :			Optional[int]  							=      block(snake_case							,			attention_mask=snake_case      )

          __UpperCAmelCase     :			int  							=      self.norm_out(snake_case      )

          if self.prd_embedding is not None:
               __UpperCAmelCase     :			Optional[int]  							=      hidden_states[:, -1]
          else:
               __UpperCAmelCase     :			List[Any]  							=      hidden_states[:, additional_embeddings_len:]

          __UpperCAmelCase     :			Dict  							=      self.proj_to_clip_embeddings(snake_case      )

          if not return_dict:
               return (predicted_image_embedding,)

          return PriorTransformerOutput(predicted_image_embedding=snake_case      )






     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		List[str]      )						->							str:
          __UpperCAmelCase     :			Dict  							=      (prior_latents * self.clip_std) + self.clip_mean
          return prior_latents



'''simple docstring'''



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

__UpperCAmelCase      :str			    =       logging.get_logger(__name__)


__UpperCAmelCase      :int			    =       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__UpperCAmelCase      :List[str]			    =       {
    "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"
    },
}

__UpperCAmelCase      :Optional[Any]			    =       {"facebook/blenderbot-3B": 1_2_8}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      _a     (						):





     '''simple docstring'''






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




def      _a     (						_lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, Any]  							=      set()
     __UpperCAmelCase     :			List[str]  							=      word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)  )
          __UpperCAmelCase     :			Tuple  							=      char
     return pairs



class      a    (		_a	):


     """simple docstring"""






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

     def __init__(    self					:		str							,			snake_case					:		Dict							,			snake_case					:		str							,			snake_case					:		Optional[int]="replace"							,			snake_case					:		Tuple="<s>"							,			snake_case					:		Dict="</s>"							,			snake_case					:		Any="</s>"							,			snake_case					:		Any="<s>"							,			snake_case					:		List[Any]="<unk>"							,			snake_case					:		int="<pad>"							,			snake_case					:		List[Any]="<mask>"							,			snake_case					:		List[Any]=False							,			**snake_case					:		Any							,			)						->							Union[str, Any]:
          __UpperCAmelCase     :			List[str]  							=      AddedToken(snake_case							,			lstrip=snake_case							,			rstrip=snake_case      ) if isinstance(snake_case							,			snake_case      ) else bos_token
          __UpperCAmelCase     :			Union[str, Any]  							=      AddedToken(snake_case							,			lstrip=snake_case							,			rstrip=snake_case      ) if isinstance(snake_case							,			snake_case      ) else eos_token
          __UpperCAmelCase     :			str  							=      AddedToken(snake_case							,			lstrip=snake_case							,			rstrip=snake_case      ) if isinstance(snake_case							,			snake_case      ) else sep_token
          __UpperCAmelCase     :			Union[str, Any]  							=      AddedToken(snake_case							,			lstrip=snake_case							,			rstrip=snake_case      ) if isinstance(snake_case							,			snake_case      ) else cls_token
          __UpperCAmelCase     :			Tuple  							=      AddedToken(snake_case							,			lstrip=snake_case							,			rstrip=snake_case      ) if isinstance(snake_case							,			snake_case      ) else unk_token
          __UpperCAmelCase     :			Optional[int]  							=      AddedToken(snake_case							,			lstrip=snake_case							,			rstrip=snake_case      ) if isinstance(snake_case							,			snake_case      ) else pad_token

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

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

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

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          __UpperCAmelCase     :			Optional[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 			lowerCamelCase__			(    self					:		Tuple      )						->							List[str]:
          return len(self.encoder      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							int:
          return dict(self.encoder							,			**self.added_tokens_encoder      )

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Optional[Any]      )						->							int:
          if token in self.cache:
               return self.cache[token]
          __UpperCAmelCase     :			Optional[Any]  							=      tuple(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      get_pairs(snake_case      )

          if not pairs:
               return token

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

                    if word[i] == first and i < len(snake_case      ) - 1 and word[i + 1] == second:
                         new_word.append(first + second      )
                         i += 2
                    else:
                         new_word.append(word[i]      )
                         i += 1
               __UpperCAmelCase     :			Union[str, Any]  							=      tuple(snake_case      )
               __UpperCAmelCase     :			Optional[Any]  							=      new_word
               if len(snake_case      ) == 1:
                    break
               else:
                    __UpperCAmelCase     :			Optional[int]  							=      get_pairs(snake_case      )
          __UpperCAmelCase     :			str  							=      ''' '''.join(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      word
          return word

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		Optional[Any]      )						->							List[str]:
          __UpperCAmelCase     :			List[Any]  							=      []
          for token in re.findall(self.pat							,			snake_case      ):
               __UpperCAmelCase     :			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(snake_case      ).split(''' '''      )      )
          return bpe_tokens

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[str]      )						->							Optional[int]:
          return self.encoder.get(snake_case							,			self.encoder.get(self.unk_token      )      )

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Optional[int]:
          return self.decoder.get(snake_case      )

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		Tuple      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Tuple  							=      ''''''.join(snake_case      )
          __UpperCAmelCase     :			Dict  							=      bytearray([self.byte_decoder[c] for c in text]      ).decode('''utf-8'''							,			errors=self.errors      )
          return text

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		str							,			snake_case					:		Optional[str] = None      )						->							Tuple[str]:
          if not os.path.isdir(snake_case      ):
               logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
               return
          __UpperCAmelCase     :			str  							=      os.path.join(
              snake_case							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
          __UpperCAmelCase     :			int  							=      os.path.join(
              snake_case							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

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

          __UpperCAmelCase     :			Optional[int]  							=      0
          with open(snake_case							,			'''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 snake_case      : 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!'''      )
                         __UpperCAmelCase     :			Union[str, Any]  							=      token_index
                    writer.write(''' '''.join(snake_case      ) + '''\n'''      )
                    index += 1

          return vocab_file, merge_file

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None							,			snake_case					:		bool = False      )						->							List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=snake_case							,			token_ids_a=snake_case							,			already_has_special_tokens=snake_case      )

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

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None      )						->							List[int]:
          __UpperCAmelCase     :			List[str]  							=      [self.sep_token_id]
          __UpperCAmelCase     :			Optional[int]  							=      [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 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Dict=False							,			**snake_case					:		Union[str, Any]      )						->							List[str]:
          __UpperCAmelCase     :			Optional[int]  							=      kwargs.pop('''add_prefix_space'''							,			self.add_prefix_space      )
          if (is_split_into_words or add_prefix_space) and (len(snake_case      ) > 0 and not text[0].isspace()):
               __UpperCAmelCase     :			Tuple  							=      ''' ''' + text
          return (text, kwargs)

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None      )						->							int:
          return token_ids_a + [self.eos_token_id]






     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		"Conversation"      )						->							List[int]:
          __UpperCAmelCase     :			List[str]  							=      []
          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(snake_case      )

          __UpperCAmelCase     :			List[str]  							=      '''  '''.join(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      self.encode(snake_case      )
          if len(snake_case      ) > self.model_max_length:
               __UpperCAmelCase     :			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