






'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A:      Dict  				=				logging.get_logger(__name__)

A:      Union[str, Any]  				=				"▁"

A:      Optional[Any]  				=				{"vocab_file": "sentencepiece.bpe.model"}

A:      str  				=				{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

A:      Optional[Any]  				=				{
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
A:      Dict  				=				["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






class 							__magic_name__     (	UpperCAmelCase_				):
	SCREAMING_SNAKE_CASE_					:      str        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      str        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  ['input_ids', 'attention_mask']

	SCREAMING_SNAKE_CASE_					:      List[int]        =  []
	SCREAMING_SNAKE_CASE_					:      List[int]        =  []
	def __init__(							self  ,      _lowercase  ,      _lowercase="<s>"  ,      _lowercase="</s>"  ,      _lowercase="</s>"  ,      _lowercase="<s>"  ,      _lowercase="<unk>"  ,      _lowercase="<pad>"  ,      _lowercase="<mask>"  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase = None  ,      _lowercase=None  ,      _lowercase=False  ,      **_lowercase  ,      )						->  Optional[Any]:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_      :   Tuple        =			AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase       ) if isinstance(_lowercase  ,      _lowercase       ) else mask_token

							lowercase_      :   Dict        =			{} if sp_model_kwargs is None else sp_model_kwargs
							lowercase_      :   Union[str, Any]        =			legacy_behaviour

							super().__init__(
							    bos_token=_lowercase  ,      eos_token=_lowercase  ,      unk_token=_lowercase  ,      sep_token=_lowercase  ,      cls_token=_lowercase  ,      pad_token=_lowercase  ,      mask_token=_lowercase  ,      tokenizer_file=_lowercase  ,      src_lang=_lowercase  ,      tgt_lang=_lowercase  ,      additional_special_tokens=_lowercase  ,      sp_model_kwargs=self.sp_model_kwargs  ,      legacy_behaviour=_lowercase  ,      **_lowercase  ,      )

							lowercase_      :   Any        =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(str(_lowercase       )       )
							lowercase_      :   Tuple        =			vocab_file
							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
							# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
							# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

							# Mimic fairseq token-to-id alignment for the first 4 token
							lowercase_      :   List[str]        =			{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							lowercase_      :   List[str]        =			1

							lowercase_      :   Optional[int]        =			len(self.sp_model       )
							lowercase_      :   int        =			{
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowercase       )
							}
							lowercase_      :   Tuple        =			{v: k for k, v in self.lang_code_to_id.items()}
							lowercase_      :   Optional[int]        =			len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

							self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
							lowercase_      :   Tuple        =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
							lowercase_      :   Optional[Any]        =			list(self.lang_code_to_id.keys()       )

							if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													self._additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in self._additional_special_tokens]       )

							lowercase_      :   List[str]        =			src_lang if src_lang is not None else 'eng_Latn'
							lowercase_      :   Union[str, Any]        =			self.lang_code_to_id[self._src_lang]
							lowercase_      :   str        =			tgt_lang
							self.set_src_lang_special_tokens(self._src_lang       )
	def __getstate__(							self       )						->  Tuple:
							lowercase_      :   Dict        =			self.__dict__.copy()
							lowercase_      :   Tuple        =			None
							lowercase_      :   List[Any]        =			self.sp_model.serialized_model_proto()
							return state
	def __setstate__(							self  ,      _lowercase       )						->  int:
							lowercase_      :   List[Any]        =			d

							# for backward compatibility
							if not hasattr(self  ,      'sp_model_kwargs'       ):
													lowercase_      :   str        =			{}

							lowercase_      :   Any        =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )
	@property
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token
	@property
	def        lowerCamelCase__		(							self       )						->  str:
							return self._src_lang
	@src_lang.setter
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  None:
							lowercase_      :   Union[str, Any]        =			new_src_lang
							self.set_src_lang_special_tokens(self._src_lang       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = False       )						->  List[int]:

							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowercase  ,      token_ids_a=_lowercase  ,      already_has_special_tokens=_lowercase       )

							lowercase_      :   Union[str, Any]        =			[1] * len(self.prefix_tokens       )
							lowercase_      :   Union[str, Any]        =			[1] * len(self.suffix_tokens       )
							if token_ids_a is None:
													return prefix_ones + ([0] * len(_lowercase       )) + suffix_ones
							return prefix_ones + ([0] * len(_lowercase       )) + ([0] * len(_lowercase       )) + suffix_ones
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							lowercase_      :   Any        =			[self.sep_token_id]
							lowercase_      :   str        =			[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 + sep + token_ids_a + sep       ) * [0]
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       )						->  List[Any]:
							if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'       )
							lowercase_      :   Dict        =			src_lang
							lowercase_      :   str        =			self(_lowercase  ,      add_special_tokens=_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase       )
							lowercase_      :   str        =			self.convert_tokens_to_ids(_lowercase       )
							lowercase_      :   Any        =			tgt_lang_id
							return inputs
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   Dict        =			{self.convert_ids_to_tokens(_lowercase       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  List[str]:
							return self.sp_model.encode(_lowercase  ,      out_type=_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Any:
							if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
							lowercase_      :   str        =			self.sp_model.PieceToId(_lowercase       )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  str:
							if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  List[str]:
							lowercase_      :   Union[str, Any]        =			''.join(_lowercase       ).replace(_lowercase  ,      ' '       ).strip()
							return out_string
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Tuple[str]:
							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       ) and os.path.isfile(self.vocab_file       ):
													copyfile(self.vocab_file  ,      _lowercase       )
							elif not os.path.isfile(self.vocab_file       ):
													with open(_lowercase  ,      'wb'       ) as fi:
																			lowercase_      :   Union[str, Any]        =			self.sp_model.serialized_model_proto()
																			fi.write(_lowercase       )

							return (out_vocab_file,)
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = "eng_Latn"  ,      _lowercase = None  ,      _lowercase = "fra_Latn"  ,      **_lowercase  ,      )						->  BatchEncoding:
							lowercase_      :   Optional[int]        =			src_lang
							lowercase_      :   int        =			tgt_lang
							return super().prepare_seqaseq_batch(_lowercase  ,      _lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							return self.set_src_lang_special_tokens(self.src_lang       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							return self.set_tgt_lang_special_tokens(self.tgt_lang       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  None:
							lowercase_      :   Optional[Any]        =			self.lang_code_to_id[src_lang]
							if self.legacy_behaviour:
													lowercase_      :   Any        =			[]
													lowercase_      :   Tuple        =			[self.eos_token_id, self.cur_lang_code]
							else:
													lowercase_      :   str        =			[self.cur_lang_code]
													lowercase_      :   Union[str, Any]        =			[self.eos_token_id]







	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  None:
							lowercase_      :   str        =			self.lang_code_to_id[lang]
							if self.legacy_behaviour:
													lowercase_      :   List[Any]        =			[]
													lowercase_      :   List[str]        =			[self.eos_token_id, self.cur_lang_code]
							else:
													lowercase_      :   int        =			[self.cur_lang_code]
													lowercase_      :   Union[str, Any]        =			[self.eos_token_id]








'''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__)
