






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_A			:    Dict					  ='''scheduler_config.json'''

class _lowercase			(       _lowercase	):
							a          = 1
							a          = 2
							a          = 3
							a          = 4
							a          = 5
							a          = 6
							a          = 7
							a          = 8
							a          = 9
							a          = 10
							a          = 11
							a          = 12
							a          = 13
							a          = 14



@dataclass
class _lowercase			(       _lowercase	):
							a          = 42



class _lowercase			:
							a          = SCHEDULER_CONFIG_NAME
							a          = []
							a          = True




							@classmethod
							def 	lowerCamelCase_       (							cls:       int    ,							UpperCamelCase__:       Dict[str, Any] = None    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[Any]=False    ,							**UpperCamelCase__:       Union[str, Any]    ,							):
														lowerCamelCase__      :			Dict 						= cls.load_config(
														    pretrained_model_name_or_path=UpperCamelCase__    ,							subfolder=UpperCamelCase__    ,							return_unused_kwargs=UpperCamelCase__    ,							return_commit_hash=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														return cls.from_config(UpperCamelCase__    ,							return_unused_kwargs=UpperCamelCase__    ,							**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Union[str, os.PathLike]    ,							UpperCamelCase__:       bool = False    ,							**UpperCamelCase__:       Optional[int]  ):
														self.save_config(save_directory=UpperCamelCase__    ,							push_to_hub=UpperCamelCase__    ,							**UpperCamelCase__  )




							@property
							def 	lowerCamelCase_       (							self:       str  ):
														return self._get_compatibles()




							@classmethod
							def 	lowerCamelCase_       (							cls:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= list(set([cls.__name__] + cls._compatibles  )  )
														lowerCamelCase__      :			Optional[int] 						= importlib.import_module(__name__.split("""."""  )[0]  )
														lowerCamelCase__      :			List[str] 						= [
														    getattr(UpperCamelCase__    ,							UpperCamelCase__  ) for c in compatible_classes_str if hasattr(UpperCamelCase__    ,							UpperCamelCase__  )
														]
														return compatible_classes









'''simple docstring'''







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


_A			:    Optional[Any]					  =logging.get_logger(__name__)

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

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

_A			:    List[Any]					  ={
    '''gpt-neox-20b''': 2_048,
}

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """attention_mask"""]




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Union[str, Any]="<|endoftext|>"    ,							UpperCamelCase__:       Tuple=False    ,							**UpperCamelCase__:       str    ,							):
														super().__init__(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							bos_token=UpperCamelCase__    ,							eos_token=UpperCamelCase__    ,							add_prefix_space=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

														lowerCamelCase__      :			Dict 						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""    ,							UpperCamelCase__  ) != add_prefix_space:
																					lowerCamelCase__      :			Any 						= getattr(UpperCamelCase__    ,							pre_tok_state.pop("""type"""  )  )
																					lowerCamelCase__      :			Dict 						= add_prefix_space
																					lowerCamelCase__      :			Optional[int] 						= pre_tok_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= add_prefix_space




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= self._tokenizer.model.save(UpperCamelCase__    ,							name=UpperCamelCase__  )
														return tuple(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       "Conversation"  ):
														lowerCamelCase__      :			str 						= []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  ) + [self.eos_token_id]  )

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

