


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase			=							{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

__lowerCAmelCase			=							{
    '''gpt-neox-20b''': 20_48,
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Optional[int]       					=			VOCAB_FILES_NAMES
 __lowercase			:							Tuple       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							Tuple       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase			:							Any       					=			['input_ids', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__="<|endoftext|>"    ,			lowerCAmelCase__="<|endoftext|>"    ,			lowerCAmelCase__="<|endoftext|>"    ,			lowerCAmelCase__=False    ,			**lowerCAmelCase__    ,			) ->    Optional[int]:




        '''simple docstring'''
        super().__init__(
            lowerCAmelCase__    ,			lowerCAmelCase__    ,			tokenizer_file=lowerCAmelCase__    ,			unk_token=lowerCAmelCase__    ,			bos_token=lowerCAmelCase__    ,			eos_token=lowerCAmelCase__    ,			add_prefix_space=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)

        lowercase__:       Any        =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
        if pre_tok_state.get('add_prefix_space'    ,			lowerCAmelCase__			) != add_prefix_space:
               lowercase__:       int        =       getattr(lowerCAmelCase__    ,			pre_tok_state.pop('type'			)			)
               lowercase__:       Any        =       add_prefix_space
               lowercase__:       int        =       pre_tok_class(**lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       add_prefix_space
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Tuple[str]:




        '''simple docstring'''
        lowercase__:       int        =       self._tokenizer.model.save(lowerCAmelCase__    ,			name=lowerCAmelCase__			)
        return tuple(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[int]:




        '''simple docstring'''
        lowercase__:       Any        =       []
        for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			) + [self.eos_token_id]			)

        if len(lowerCAmelCase__			) > self.model_max_length:
               lowercase__:       int        =       input_ids[-self.model_max_length :]
        return input_ids






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class   __a      (   tf.keras.layers.Layer				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None			) ->    int:




        '''simple docstring'''
        super().__init__()
        lowercase__:       Union[str, Any]        =       pad_token_id
        lowercase__:       List[str]        =       max_length
        lowercase__:       int        =       vocab
        lowercase__:       List[Any]        =       merges
        lowercase__:       str        =       BytePairTokenizer(lowerCAmelCase__    ,			lowerCAmelCase__    ,			sequence_length=lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Tuple        =       [' '.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
        lowercase__:       List[Any]        =       tokenizer.get_vocab()
        return cls(lowerCAmelCase__    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       GPTaTokenizer.from_pretrained(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
        return cls.from_tokenizer(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        return cls(**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return {
            "vocab": self.vocab,
            "merges": self.merges,
            "max_length": self.max_length,
            "pad_token_id": self.pad_token_id,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.tf_tokenizer(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       tf.ones_like(lowerCAmelCase__			)

        if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__:       int        =       max_length if max_length is not None else self.max_length

               if max_length is not None:
                      lowercase__      ,						lowercase__:       List[Any]        =       pad_model_inputs(
                          lowerCAmelCase__    ,			max_seq_length=lowerCAmelCase__    ,			pad_value=self.pad_token_id			)

        return {"attention_mask": attention_mask, "input_ids": input_ids}


