






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


__UpperCAmelCase								=						logging.get_logger(__name__)

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

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

__UpperCAmelCase								=						{
    'gpt-neox-20b': 2048,
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    str       						=  VOCAB_FILES_NAMES
		_snake_case    :    Optional[Any]       						=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case    :    Optional[int]       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case    :    Union[str, Any]       						=  ['''input_ids''', '''attention_mask''']



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

							UpperCAmelCase_    :      List[str]            =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get('add_prefix_space'				,		_UpperCamelCase   ) != add_prefix_space:
												UpperCAmelCase_    :      Optional[int]            =						getattr(_UpperCamelCase				,		pre_tok_state.pop('type'   )   )
												UpperCAmelCase_    :      Optional[int]            =						add_prefix_space
												UpperCAmelCase_    :      Dict            =						pre_tok_class(**_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						add_prefix_space



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							Tuple[str]:
							UpperCAmelCase_    :      Dict            =						self._tokenizer.model.save(_UpperCamelCase				,		name=_UpperCamelCase   )
							return tuple(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[int]:
							UpperCAmelCase_    :      Tuple            =						[]
							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:
												UpperCAmelCase_    :      int            =						input_ids[-self.model_max_length :]
							return input_ids










'''simple docstring'''






from statistics import mean, stdev







def 				UpperCamelCase_			(    A__					:   list							,    A__					:   int = 3					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[str]			=   min(A__					)
  lowerCAmelCase_			:      Optional[int]			=   max(A__					)
  # normalize data
  return [round((x - x_min) / (x_max - x_min)							,    A__					) for x in data]







def 				UpperCamelCase_			(    A__					:   list							,    A__					:   int = 3					):
  '''simple docstring'''





  lowerCAmelCase_			:      str			=   mean(A__					)
  lowerCAmelCase_			:      List[Any]			=   stdev(A__					)
  # standardize data
  return [round((x - mu) / (sigma)							,    A__					) for x in data]



