





'''simple docstring'''







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_xlnet import XLNetTokenizer
else:
					lowerCAmelCase_					:    Optional[Any]      	=			None


lowerCAmelCase_					:    Union[str, Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Tuple      	=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_					:    Tuple      	=			{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_					:    Union[str, Any]      	=			{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

lowerCAmelCase_					:    int      	=			'▁'

# Segments (not really needed)
lowerCAmelCase_					:    Optional[Any]      	=			0
lowerCAmelCase_					:    int      	=			1
lowerCAmelCase_					:    Optional[Any]      	=			2
lowerCAmelCase_					:    List[str]      	=			3
lowerCAmelCase_					:    List[str]      	=			4
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =VOCAB_FILES_NAMES
				__a            =PRETRAINED_VOCAB_FILES_MAP
				__a            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a            ='left'
				__a            =XLNetTokenizer




				def __init__(  self     :					Optional[int]					,				__a     :					Optional[int]=None					,				__a     :					str=None					,				__a     :					Any=False					,				__a     :					List[Any]=True					,				__a     :					List[Any]=False					,				__a     :					List[str]="<s>"					,				__a     :					Optional[Any]="</s>"					,				__a     :					str="<unk>"					,				__a     :					Union[str, Any]="<sep>"					,				__a     :					List[str]="<pad>"					,				__a     :					int="<cls>"					,				__a     :					Tuple="<mask>"					,				__a     :					Any=["<eop>", "<eod>"]					,				**__a     :					int					,				):
								# Mask token behave like a normal word, i.e. include the space before it
								_a								= AddedToken(__a					,				lstrip=__a					,				rstrip=__a					) if isinstance(__a					,				__a					) else mask_token

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

								_a								= 3
								_a								= do_lower_case
								_a								= remove_space
								_a								= keep_accents
								_a								= vocab_file
								_a								= False if not self.vocab_file else True




				def   UpperCamelCase__   (  self     :					str					,				__a     :					List[int]					,				__a     :					Optional[List[int]] = None					):
								_a								= [self.sep_token_id]
								_a								= [self.cls_token_id]
								if token_ids_a is None:
												return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					List[int]					,				__a     :					Optional[List[int]] = None					):
								_a								= [self.sep_token_id]
								_a								= [2]

								if token_ids_a is None:
												return len(token_ids_a + sep					) * [0] + cls_segment_id
								return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					Optional[str] = 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(__a					):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
												return
								_a								= os.path.join(
								    __a					,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

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

								return (out_vocab_file,)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_					:    int      	=			{'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[int]      	=			['GPTNeoXTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXForCausalLM',
					    'GPTNeoXForQuestionAnswering',
					    'GPTNeoXForSequenceClassification',
					    'GPTNeoXForTokenClassification',
					    'GPTNeoXLayer',
					    'GPTNeoXModel',
					    'GPTNeoXPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_					:    List[str]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
