

import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


__A			:					Dict						=			get_logger(__name__)


__A			:					Tuple						=			r'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   @add_start_docstrings(__lowerCamelCase			)
   def __call__(       self					:			Union[str, Any]     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray			):
       raise NotImplementedError(
           f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   @add_start_docstrings(__lowerCamelCase			)
   def __call__(       self					:			List[Any]     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray			):
       raise NotImplementedError(
           f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."			)





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   @add_start_docstrings(__lowerCamelCase			)
   def __call__(       self					:			Optional[Any]     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int     , **__lowerCamelCase					:			Optional[Any]			):
       for processor in self:
           SCREAMING_SNAKE_CASE										=   inspect.signature(processor.__call__			).parameters
           if len(__lowerCamelCase			) > 3:
               if not all(arg in kwargs for arg in list(function_args.keys()			)[2:]			):
                   raise ValueError(
                       f"Make sure that all the required parameters: {list(function_args.keys()			)} for "
                       f"{processor.__class__} are passed to the logits processor."			)
               SCREAMING_SNAKE_CASE										=   processor(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			)
           else:
               SCREAMING_SNAKE_CASE										=   processor(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Tuple     , __lowerCamelCase					:			float			):
       if not isinstance(__lowerCamelCase     , __lowerCamelCase			) or not (temperature > 0):
           raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}"			)

       SCREAMING_SNAKE_CASE										=   temperature





   def __call__(       self					:			Any     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   scores / self.temperature
       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			float     , __lowerCamelCase					:			float = -float("Inf"			)     , __lowerCamelCase					:			int = 1			):
       if not isinstance(__lowerCamelCase     , __lowerCamelCase			) or (top_p < 0 or top_p > 1.0):
           raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}"			)
       if not isinstance(__lowerCamelCase     , __lowerCamelCase			) or (min_tokens_to_keep < 1):
           raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"			)

       SCREAMING_SNAKE_CASE										=   top_p
       SCREAMING_SNAKE_CASE										=   filter_value
       SCREAMING_SNAKE_CASE										=   min_tokens_to_keep





   def __call__(       self					:			Tuple     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   lax.top_k(__lowerCamelCase     , scores.shape[-1]			)

       SCREAMING_SNAKE_CASE										=   jnp.full_like(__lowerCamelCase     , self.filter_value			)
       SCREAMING_SNAKE_CASE										=   jax.nn.softmax(__lowerCamelCase     , axis=-1			).cumsum(axis=-1			)
       SCREAMING_SNAKE_CASE										=   cumulative_probs < self.top_p

       # include the token that is higher than top_p as well
       SCREAMING_SNAKE_CASE										=   jnp.roll(__lowerCamelCase     , 1			)
       score_mask |= score_mask.at[:, 0].set(__lowerCamelCase			)

       # min tokens to keep
       SCREAMING_SNAKE_CASE										=   score_mask.at[:, : self.min_tokens_to_keep].set(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   jnp.where(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   jax.lax.sort_key_val(__lowerCamelCase     , __lowerCamelCase			)[-1]

       return next_scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float = -float("Inf"			)     , __lowerCamelCase					:			int = 1			):
       if not isinstance(__lowerCamelCase     , __lowerCamelCase			) or top_k <= 0:
           raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}"			)

       SCREAMING_SNAKE_CASE										=   max(__lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   filter_value





   def __call__(       self					:			Union[str, Any]     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   scores.shape
       SCREAMING_SNAKE_CASE										=   jnp.full(batch_size * vocab_size     , self.filter_value			)

       SCREAMING_SNAKE_CASE										=   min(self.top_k     , scores.shape[-1]			)  # Safety check
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   lax.top_k(__lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   jnp.broadcast_to((jnp.arange(__lowerCamelCase			) * vocab_size)[:, None]     , (batch_size, topk)			).flatten()
       SCREAMING_SNAKE_CASE										=   topk_scores.flatten()
       SCREAMING_SNAKE_CASE										=   topk_indices.flatten() + shift

       SCREAMING_SNAKE_CASE										=   next_scores_flat.at[topk_indices_flat].set(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   next_scores_flat.reshape(__lowerCamelCase     , __lowerCamelCase			)
       return next_scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Optional[Any]     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   bos_token_id





   def __call__(       self					:			str     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   jnp.full(scores.shape     , -float("inf"			)			)

       SCREAMING_SNAKE_CASE										=   1 - jnp.bool_(cur_len - 1			)

       SCREAMING_SNAKE_CASE										=   jnp.where(__lowerCamelCase     , new_scores.at[:, self.bos_token_id].set(0			)     , __lowerCamelCase			)

       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   eos_token_id





   def __call__(       self					:			List[str]     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   jnp.full(scores.shape     , -float("inf"			)			)

       SCREAMING_SNAKE_CASE										=   1 - jnp.bool_(cur_len - self.max_length + 1			)

       SCREAMING_SNAKE_CASE										=   jnp.where(__lowerCamelCase     , new_scores.at[:, self.eos_token_id].set(0			)     , __lowerCamelCase			)

       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int			):
       if not isinstance(__lowerCamelCase     , __lowerCamelCase			) or min_length < 0:
           raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}"			)

       if not isinstance(__lowerCamelCase     , __lowerCamelCase			) or eos_token_id < 0:
           raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}"			)

       SCREAMING_SNAKE_CASE										=   min_length
       SCREAMING_SNAKE_CASE										=   eos_token_id





   def __call__(       self					:			List[str]     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       # create boolean flag to decide if min length penalty should be applied
       SCREAMING_SNAKE_CASE										=   1 - jnp.clip(cur_len - self.min_length     , 0     , 1			)

       SCREAMING_SNAKE_CASE										=   jnp.where(__lowerCamelCase     , scores.at[:, self.eos_token_id].set(-float("inf"			)			)     , __lowerCamelCase			)

       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   list(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   begin_index





   def __call__(       self					:			Any     , __lowerCamelCase					:			int     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   1 - jnp.bool_(cur_len - self.begin_index			)

       SCREAMING_SNAKE_CASE										=   jnp.where(__lowerCamelCase     , scores.at[:, self.begin_suppress_tokens].set(-float("inf"			)			)     , __lowerCamelCase			)

       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Optional[Any]     , __lowerCamelCase					:			list			):
       SCREAMING_SNAKE_CASE										=   list(__lowerCamelCase			)





   def __call__(       self					:			List[str]     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   scores.at[..., self.suppress_tokens].set(-float("inf"			)			)

       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			int     , __lowerCamelCase					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   dict(__lowerCamelCase			)
       # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
       # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
       # Indexes without forced tokens will have a negative value.
       SCREAMING_SNAKE_CASE										=   jnp.ones((max(force_token_map.keys()			) + 1)     , dtype=jnp.intaa			) * -1
       for index, token in force_token_map.items():
           if token is not None:
               SCREAMING_SNAKE_CASE										=   force_token_array.at[index].set(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   jnp.intaa(__lowerCamelCase			)





   def __call__(       self					:			str     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			jnp.ndarray     , __lowerCamelCase					:			int			):
       def _force_token(__lowerCamelCase					:			str			):
           SCREAMING_SNAKE_CASE										=   scores.shape[0]
           SCREAMING_SNAKE_CASE										=   self.force_token_array[generation_idx]

           SCREAMING_SNAKE_CASE										=   jnp.ones_like(__lowerCamelCase     , dtype=scores.dtype			) * -float("inf"			)
           SCREAMING_SNAKE_CASE										=   jnp.zeros((batch_size, 1)     , dtype=scores.dtype			)
           SCREAMING_SNAKE_CASE										=   lax.dynamic_update_slice(__lowerCamelCase     , __lowerCamelCase     , (0, current_token)			)
           return new_scores

       SCREAMING_SNAKE_CASE										=   lax.cond(
           cur_len >= self.force_token_array.shape[0]     , lambda: scores     , lambda: lax.cond(
               self.force_token_array[cur_len] >= 0     , lambda: _force_token(__lowerCamelCase			)     , lambda: scores     , )     , )
       return scores





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   generate_config.eos_token_id
       SCREAMING_SNAKE_CASE										=   generate_config.no_timestamps_token_id
       SCREAMING_SNAKE_CASE										=   generate_config.no_timestamps_token_id + 1

       SCREAMING_SNAKE_CASE										=   decoder_input_length + 1

       if generate_config.is_multilingual:
           # room for language token and task token
           self.begin_index += 2
       if hasattr(__lowerCamelCase     , "max_initial_timestamp_index"			):
           SCREAMING_SNAKE_CASE										=   generate_config.max_initial_timestamp_index
       else:
           SCREAMING_SNAKE_CASE										=   model_config.vocab_size
       if self.max_initial_timestamp_index is None:
           SCREAMING_SNAKE_CASE										=   model_config.vocab_size





   def __call__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			int			):
       # suppress <|notimestamps|> which is handled by without_timestamps
       SCREAMING_SNAKE_CASE										=   scores.at[:, self.no_timestamps_token_id].set(-float("inf"			)			)

       def handle_pairs(__lowerCamelCase					:			Tuple     , __lowerCamelCase					:			List[str]			):
           SCREAMING_SNAKE_CASE										=   jnp.where((cur_len - self.begin_index) >= 1     , __lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   jnp.where(
               input_ids_k[cur_len - 1] >= self.timestamp_begin     , True and last_was_timestamp     , __lowerCamelCase     , )

           SCREAMING_SNAKE_CASE										=   jnp.where((cur_len - self.begin_index) < 2     , __lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   jnp.where(
               input_ids_k[cur_len - 2] >= self.timestamp_begin     , __lowerCamelCase     , __lowerCamelCase     , )

           return jnp.where(
               __lowerCamelCase     , jnp.where(
                   penultimate_was_timestamp > 0     , scores_k.at[self.timestamp_begin :].set(-float("inf"			)			)     , scores_k.at[: self.eos_token_id].set(-float("inf"			)			)     , )     , __lowerCamelCase     , )

       SCREAMING_SNAKE_CASE										=   jax.vmap(__lowerCamelCase			)(__lowerCamelCase     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   jnp.where(cur_len == self.begin_index     , __lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   jnp.where(
           self.max_initial_timestamp_index is not None     , True and apply_max_initial_timestamp     , __lowerCamelCase     , )

       SCREAMING_SNAKE_CASE										=   self.timestamp_begin + self.max_initial_timestamp_index

       SCREAMING_SNAKE_CASE										=   jnp.where(
           __lowerCamelCase     , scores.at[:, last_allowed + 1 :].set(-float("inf"			)			)     , __lowerCamelCase     , )

       # if sum of probability over timestamps is above any other token, sample timestamp
       SCREAMING_SNAKE_CASE										=   jax.nn.log_softmax(__lowerCamelCase     , axis=-1			)

       def handle_cumulative_probs(__lowerCamelCase					:			Tuple     , __lowerCamelCase					:			List[str]			):
           SCREAMING_SNAKE_CASE										=   jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]     , axis=-1			)
           SCREAMING_SNAKE_CASE										=   jnp.max(logprobs_k[: self.timestamp_begin]			)
           return jnp.where(
               timestamp_logprob > max_text_token_logprob     , scores_k.at[: self.timestamp_begin].set(-float("inf"			)			)     , __lowerCamelCase     , )

       SCREAMING_SNAKE_CASE										=   jax.vmap(__lowerCamelCase			)(__lowerCamelCase     , __lowerCamelCase			)

       return scores


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

__A			:					Any						=			logging.get_logger(__name__)


__A			:					Any						=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__A			:					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'
    },
}

__A			:					Union[str, Any]						=			{'facebook/blenderbot-3B': 1_2_8}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   (
        list(range(ord("!"       )			,  ord("~"       ) + 1       )       ) + list(range(ord("¡"       )			,  ord("¬"       ) + 1       )       ) + list(range(ord("®"       )			,  ord("ÿ"       ) + 1       )       )
    )
    SCREAMING_SNAKE_CASE										=   bs[:]
    SCREAMING_SNAKE_CASE										=   0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(A__       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE										=   [chr(A__       ) for n in cs]
    return dict(zip(A__			,  A__       )       )





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   set()
    SCREAMING_SNAKE_CASE										=   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE										=   char
    return pairs





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      ["input_ids", "attention_mask"]





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

       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else mask_token

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

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

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			str			):
       return len(self.encoder			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       return dict(self.encoder     , **self.added_tokens_encoder			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[Any]			):
       if token in self.cache:
           return self.cache[token]
       SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)

       if not pairs:
           return token

       while True:
           SCREAMING_SNAKE_CASE										=   min(__lowerCamelCase     , key=lambda __lowerCamelCase			: self.bpe_ranks.get(__lowerCamelCase     , float("inf"			)			)			)
           if bigram not in self.bpe_ranks:
               break
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   bigram
           SCREAMING_SNAKE_CASE										=   []
           SCREAMING_SNAKE_CASE										=   0
           while i < len(__lowerCamelCase			):
               try:
                   SCREAMING_SNAKE_CASE										=   word.index(__lowerCamelCase     , __lowerCamelCase			)
               except ValueError:
                   new_word.extend(word[i:]			)
                   break
               else:
                   new_word.extend(word[i:j]			)
                   SCREAMING_SNAKE_CASE										=   j

               if word[i] == first and i < len(__lowerCamelCase			) - 1 and word[i + 1] == second:
                   new_word.append(first + second			)
                   i += 2
               else:
                   new_word.append(word[i]			)
                   i += 1
           SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   new_word
           if len(__lowerCamelCase			) == 1:
               break
           else:
               SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   " ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   word
       return word





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   []
       for token in re.findall(self.pat     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   "".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(__lowerCamelCase			).split(" "			)			)
       return bpe_tokens





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Dict			):
       return self.encoder.get(__lowerCamelCase     , self.encoder.get(self.unk_token			)			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Optional[int]			):
       return self.decoder.get(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   bytearray([self.byte_decoder[c] for c in text]			).decode("utf-8"     , errors=self.errors			)
       return text





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       if not os.path.isdir(__lowerCamelCase			):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
           return
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]			)

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

       SCREAMING_SNAKE_CASE										=   0
       with open(__lowerCamelCase     , "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 __lowerCamelCase			: 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!"			)
                   SCREAMING_SNAKE_CASE										=   token_index
               writer.write(" ".join(__lowerCamelCase			) + "\n"			)
               index += 1

       return vocab_file, merge_file





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = False			):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__lowerCamelCase     , token_ids_a=__lowerCamelCase     , already_has_special_tokens=__lowerCamelCase			)

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





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       SCREAMING_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]





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   kwargs.pop("add_prefix_space"     , self.add_prefix_space			)
       if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase			) > 0 and not text[0].isspace()):
           SCREAMING_SNAKE_CASE										=   " " + text
       return (text, kwargs)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       return token_ids_a + [self.eos_token_id]





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			"Conversation"			):
       SCREAMING_SNAKE_CASE										=   []
       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(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "  ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.encode(__lowerCamelCase			)
       if len(__lowerCamelCase			) > self.model_max_length:
           SCREAMING_SNAKE_CASE										=   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