






'''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:
						A:      List[Any]  				=				None


A:      Dict  				=				logging.get_logger(__name__)

A:      Dict  				=				{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A:      List[str]  				=				{
    "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",
    },
}

A:      Optional[Any]  				=				{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

A:      str  				=				"▁"

# Segments (not really needed)
A:      str  				=				0
A:      Any  				=				1
A:      Union[str, Any]  				=				2
A:      str  				=				3
A:      List[str]  				=				4






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      Any        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  'left'
	SCREAMING_SNAKE_CASE_					:      Dict        =  XLNetTokenizer
	def __init__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase="<s>"  ,      _lowercase="</s>"  ,      _lowercase="<unk>"  ,      _lowercase="<sep>"  ,      _lowercase="<pad>"  ,      _lowercase="<cls>"  ,      _lowercase="<mask>"  ,      _lowercase=["<eop>", "<eod>"]  ,      **_lowercase  ,      )						->  int:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_      :   Dict        =			AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase       ) if isinstance(_lowercase  ,      _lowercase       ) else mask_token

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

							lowercase_      :   Optional[int]        =			3
							lowercase_      :   List[str]        =			do_lower_case
							lowercase_      :   Tuple        =			remove_space
							lowercase_      :   List[Any]        =			keep_accents
							lowercase_      :   int        =			vocab_file
							lowercase_      :   List[Any]        =			False if not self.vocab_file else True
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							lowercase_      :   str        =			[self.sep_token_id]
							lowercase_      :   Optional[Any]        =			[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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							lowercase_      :   int        =			[self.sep_token_id]
							lowercase_      :   str        =			[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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Tuple[str]:
							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(_lowercase       ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
													return
							lowercase_      :   Any        =			os.path.join(
							    _lowercase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

							return (out_vocab_file,)








'''simple docstring'''

from typing import TYPE_CHECKING

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


A:      int  				=				{
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Union[str, Any]  				=				[
						    "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TrajectoryTransformerModel",
						    "TrajectoryTransformerPreTrainedModel",
						    "load_tf_weights_in_trajectory_transformer",
						]


if TYPE_CHECKING:
						from .configuration_trajectory_transformer import (
						    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TrajectoryTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trajectory_transformer import (
												    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TrajectoryTransformerModel,
												    TrajectoryTransformerPreTrainedModel,
												    load_tf_weights_in_trajectory_transformer,
												)


else:
						import sys

						A:      int  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
