
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE    :      str          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Any          =			{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}







class  UpperCamelCase__		(    a__   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"gpt_neox"





	def __init__(			self				,       UpperCamelCase__=5_0432				,       UpperCamelCase__=6144				,       UpperCamelCase__=44				,       UpperCamelCase__=64				,       UpperCamelCase__=2_4576				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.2_5				,       UpperCamelCase__=1_0000				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.1				,       UpperCamelCase__=2048				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1e-5				,       UpperCamelCase__=True				,       UpperCamelCase__=0				,       UpperCamelCase__=2				,       UpperCamelCase__=False				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       **UpperCamelCase__				,       ):
					super().__init__(bos_token_id=_A				,       eos_token_id=_A				,       **_A		)
					A__  :	Tuple         =							vocab_size
					A__  :	str         =							max_position_embeddings
					A__  :	int         =							hidden_size
					A__  :	str         =							num_hidden_layers
					A__  :	Union[str, Any]         =							num_attention_heads
					A__  :	Dict         =							intermediate_size
					A__  :	Dict         =							hidden_act
					A__  :	Tuple         =							rotary_pct
					A__  :	Optional[Any]         =							rotary_emb_base
					A__  :	str         =							attention_dropout
					A__  :	List[str]         =							hidden_dropout
					A__  :	List[Any]         =							classifier_dropout
					A__  :	str         =							initializer_range
					A__  :	Dict         =							layer_norm_eps
					A__  :	Any         =							use_cache
					A__  :	int         =							tie_word_embeddings
					A__  :	List[Any]         =							use_parallel_residual
					A__  :	Optional[Any]         =							rope_scaling
					self._rope_scaling_validation()

					if self.hidden_size % self.num_attention_heads != 0:
									raise ValueError(
									    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''		)





	def   __snake_case				(			self		):

					if self.rope_scaling is None:
									return

					if not isinstance(self.rope_scaling				,       _A		) or len(self.rope_scaling		) != 2:
									raise ValueError(
									    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
									    F"got {self.rope_scaling}"		)
					A__  :	int         =							self.rope_scaling.get('''type'''				,       _A		)
					A__  :	Any         =							self.rope_scaling.get('''factor'''				,       _A		)
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
									raise ValueError(
									    F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"		)
					if rope_scaling_factor is None or not isinstance(_A				,       _A		) or rope_scaling_factor <= 1.0:
									raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"		)

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


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_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				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[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:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids