



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_albert import AlbertTokenizer
else:
						UpperCAmelCase_							:      Optional[int]      =  None

UpperCAmelCase_							:      Any      =  logging.get_logger(__name__)
UpperCAmelCase_							:      Dict      =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_							:      List[Any]      =  {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase_							:      Union[str, Any]      =  {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

UpperCAmelCase_							:      Optional[int]      =  "▁"






class 					UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Any				=	VOCAB_FILES_NAMES
	lowerCAmelCase	:   int				=	PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase	:   str				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase	:   int				=	AlbertTokenizer



	def __init__(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=False			,	UpperCAmelCase__="[CLS]"			,	UpperCAmelCase__="[SEP]"			,	UpperCAmelCase__="<unk>"			,	UpperCAmelCase__="[SEP]"			,	UpperCAmelCase__="<pad>"			,	UpperCAmelCase__="[CLS]"			,	UpperCAmelCase__="[MASK]"			,	**UpperCAmelCase__			,	):
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							A__							=       (
							    AddedToken(lowerCamelCase_			,	lstrip=lowerCamelCase_			,	rstrip=lowerCamelCase_			,	normalized=lowerCamelCase_		)
							    if isinstance(lowerCamelCase_			,	lowerCamelCase_		)
							    else mask_token
							)

							super().__init__(
							    lowerCamelCase_			,	tokenizer_file=lowerCamelCase_			,	do_lower_case=lowerCamelCase_			,	remove_space=lowerCamelCase_			,	keep_accents=lowerCamelCase_			,	bos_token=lowerCamelCase_			,	eos_token=lowerCamelCase_			,	unk_token=lowerCamelCase_			,	sep_token=lowerCamelCase_			,	pad_token=lowerCamelCase_			,	cls_token=lowerCamelCase_			,	mask_token=lowerCamelCase_			,	**lowerCamelCase_			,	)

							A__							=       do_lower_case
							A__							=       remove_space
							A__							=       keep_accents
							A__							=       vocab_file
							A__							=       False if not self.vocab_file else True



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       [self.sep_token_id]
							A__							=       [self.cls_token_id]
							if token_ids_a is None:
													return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       [self.sep_token_id]
							A__							=       [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		) * [0] + len(token_ids_a + sep		) * [1]



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."		)

							if not os.path.isdir(lowerCamelCase_		):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
													return
							A__							=       os.path.join(
							    lowerCamelCase_			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase_		):
													copyfile(self.vocab_file			,	lowerCamelCase_		)

							return (out_vocab_file,)










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


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[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    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""gpt_neox"""



	def __init__(       self			,	UpperCAmelCase__=50_432			,	UpperCAmelCase__=6_144			,	UpperCAmelCase__=44			,	UpperCAmelCase__=64			,	UpperCAmelCase__=24_576			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.25			,	UpperCAmelCase__=10_000			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=2_048			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0			,	UpperCAmelCase__=2			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       vocab_size
							A__							=       max_position_embeddings
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       rotary_pct
							A__							=       rotary_emb_base
							A__							=       attention_dropout
							A__							=       hidden_dropout
							A__							=       classifier_dropout
							A__							=       initializer_range
							A__							=       layer_norm_eps
							A__							=       use_cache
							A__							=       tie_word_embeddings
							A__							=       use_parallel_residual
							A__							=       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 			__A							(       self		):
							if self.rope_scaling is None:
													return

							if not isinstance(self.rope_scaling			,	UpperCAmelCase__		) 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__							=       self.rope_scaling.get("type"			,	UpperCAmelCase__		)
							A__							=       self.rope_scaling.get("factor"			,	UpperCAmelCase__		)
							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(UpperCAmelCase__			,	UpperCAmelCase__		) or rope_scaling_factor <= 1.0:
													raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)





