



'''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


SCREAMING_SNAKE_CASE__     :						List[Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[str]	=  '''▁'''

SCREAMING_SNAKE_CASE__     :						List[str]	=  {'''vocab_file''': '''sentencepiece.bpe.model'''}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''facebook/nllb-200-distilled-600M''': 1_0_2_4,
}

# fmt: off
SCREAMING_SNAKE_CASE__     :						Tuple	=  ['''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 							a__( _UpperCamelCase     ):
			a_	: Union[str, Any]									=      VOCAB_FILES_NAMES
			a_	: int									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: Any									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      ['''input_ids''', '''attention_mask''']

			a_	: List[int]									=      []
			a_	: List[int]									=      []






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase = None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=False					,   **_UpperCAmelCase					,   )							->							Any:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__	     =AddedToken(lowercase__					,   lstrip=lowercase__					,   rstrip=lowercase__						) if isinstance(lowercase__					,   lowercase__						) else mask_token

					snake_case__	     ={} if sp_model_kwargs is None else sp_model_kwargs
					snake_case__	     =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__					,   )

					snake_case__	     =spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.Load(str(lowercase__						)						)
					snake_case__	     =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
					snake_case__	     ={'''<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
					snake_case__	     =1

					snake_case__	     =len(self.sp_model						)
					snake_case__	     ={
					    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__						)
					}
					snake_case__	     ={v: k for k, v in self.lang_code_to_id.items()}
					snake_case__	     =len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

					self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
					snake_case__	     ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
					snake_case__	     =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]						)

					snake_case__	     =src_lang if src_lang is not None else '''eng_Latn'''
					snake_case__	     =self.lang_code_to_id[self._src_lang]
					snake_case__	     =tgt_lang
					self.set_src_lang_special_tokens(self._src_lang						)






			def __getstate__(     self						)							->							List[Any]:
					snake_case__	     =self.__dict__.copy()
					snake_case__	     =None
					snake_case__	     =self.sp_model.serialized_model_proto()
					return state






			def __setstate__(     self					,   _UpperCAmelCase						)							->							Dict:
					snake_case__	     =d

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

					snake_case__	     =spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)






			@property
			def 						_lowercase  (     self						)							->							Optional[Any]:
					return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token






			@property
			def 						_lowercase  (     self						)							->							List[str]:
					return self._src_lang






			@src_lang.setter
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Dict:
					snake_case__	     =new_src_lang
					self.set_src_lang_special_tokens(self._src_lang						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = False						)							->							Optional[Any]:

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

					snake_case__	     =[1] * len(self.prefix_tokens						)
					snake_case__	     =[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 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Dict:
					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 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Dict:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[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 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Tuple:
					if src_lang is None or tgt_lang is None:
							raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'						)
					snake_case__	     =src_lang
					snake_case__	     =self(lowercase__					,   add_special_tokens=lowercase__					,   return_tensors=lowercase__					,   **lowercase__						)
					snake_case__	     =self.convert_tokens_to_ids(lowercase__						)
					snake_case__	     =tgt_lang_id
					return inputs






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={self.convert_ids_to_tokens(lowercase__						): i for i in range(self.vocab_size						)}
					vocab.update(self.added_tokens_encoder						)
					return vocab






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Tuple:
					return self.sp_model.encode(lowercase__					,   out_type=lowercase__						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							str:
					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					snake_case__	     =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 						_lowercase  (     self					,   _UpperCAmelCase						)							->							List[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 						_lowercase  (     self					,   _UpperCAmelCase						)							->							int:
					snake_case__	     =''''''.join(lowercase__						).replace(lowercase__					,   ' '						).strip()
					return out_string






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Any:
					if not os.path.isdir(lowercase__						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =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:
									snake_case__	     =self.sp_model.serialized_model_proto()
									fi.write(lowercase__						)

					return (out_vocab_file,)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = "eng_Latn"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fra_Latn"					,   **_UpperCAmelCase					,   )							->							Dict:
					snake_case__	     =src_lang
					snake_case__	     =tgt_lang
					return super().prepare_seqaseq_batch(lowercase__					,   lowercase__					,   **lowercase__						)






			def 						_lowercase  (     self						)							->							List[Any]:
					return self.set_src_lang_special_tokens(self.src_lang						)






			def 						_lowercase  (     self						)							->							Any:
					return self.set_tgt_lang_special_tokens(self.tgt_lang						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =self.lang_code_to_id[src_lang]
					if self.legacy_behaviour:
							snake_case__	     =[]
							snake_case__	     =[self.eos_token_id, self.cur_lang_code]
					else:
							snake_case__	     =[self.cur_lang_code]
							snake_case__	     =[self.eos_token_id]






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =self.lang_code_to_id[lang]
					if self.legacy_behaviour:
							snake_case__	     =[]
							snake_case__	     =[self.eos_token_id, self.cur_lang_code]
					else:
							snake_case__	     =[self.cur_lang_code]
							snake_case__	     =[self.eos_token_id]








from typing import List, Optional

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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[int]										=						'''autoformer'''
	_lowerCAmelCase     :		Any										=						{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}


	def __init__(    self				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = "student_t"				,     lowercase__ = "nll"				,     lowercase__ = 1				,     lowercase__ = [1, 2, 3, 4, 5, 6, 7]				,     lowercase__ = True				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = 6_4				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 3_2				,     lowercase__ = 3_2				,     lowercase__ = "gelu"				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 1_0_0				,     lowercase__ = 0.0_2				,     lowercase__ = True				,     lowercase__=True				,     lowercase__ = 1_0				,     lowercase__ = 2_5				,     lowercase__ = 3				,     **lowercase__				,     ):
							# time series specific configuration
							__UpperCAmelCase			:							Optional[int]	    =	prediction_length
							__UpperCAmelCase			:							Tuple	    =	context_length if context_length is not None else prediction_length
							__UpperCAmelCase			:							Tuple	    =	distribution_output
							__UpperCAmelCase			:							Tuple	    =	loss
							__UpperCAmelCase			:							Dict	    =	input_size
							__UpperCAmelCase			:							str	    =	num_time_features
							__UpperCAmelCase			:							str	    =	lags_sequence
							__UpperCAmelCase			:							int	    =	scaling
							__UpperCAmelCase			:							int	    =	num_dynamic_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_categorical_features
							if cardinality is not None and num_static_categorical_features > 0:
													if len(lowercase__) != num_static_categorical_features:
																			raise ValueError(
																			    '''The cardinality should be a list of the same length as `num_static_categorical_features`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	cardinality
							else:
													__UpperCAmelCase			:							List[Any]	    =	[0]
							if embedding_dimension is not None and num_static_categorical_features > 0:
													if len(lowercase__) != num_static_categorical_features:
																			raise ValueError(
																			    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	embedding_dimension
							else:
													__UpperCAmelCase			:							int	    =	[min(5_0				,     (cat + 1) // 2) for cat in self.cardinality]
							__UpperCAmelCase			:							Tuple	    =	num_parallel_samples

							# Transformer architecture configuration
							__UpperCAmelCase			:							Dict	    =	input_size * len(self.lags_sequence) + self._number_of_features
							__UpperCAmelCase			:							Optional[int]	    =	d_model
							__UpperCAmelCase			:							List[Any]	    =	encoder_attention_heads
							__UpperCAmelCase			:							int	    =	decoder_attention_heads
							__UpperCAmelCase			:							str	    =	encoder_ffn_dim
							__UpperCAmelCase			:							int	    =	decoder_ffn_dim
							__UpperCAmelCase			:							List[Any]	    =	encoder_layers
							__UpperCAmelCase			:							Tuple	    =	decoder_layers

							__UpperCAmelCase			:							Union[str, Any]	    =	dropout
							__UpperCAmelCase			:							int	    =	attention_dropout
							__UpperCAmelCase			:							int	    =	activation_dropout
							__UpperCAmelCase			:							str	    =	encoder_layerdrop
							__UpperCAmelCase			:							Dict	    =	decoder_layerdrop

							__UpperCAmelCase			:							int	    =	activation_function
							__UpperCAmelCase			:							Optional[int]	    =	init_std

							__UpperCAmelCase			:							Optional[int]	    =	use_cache

							# Autoformer
							__UpperCAmelCase			:							str	    =	label_length
							__UpperCAmelCase			:							int	    =	moving_average
							__UpperCAmelCase			:							Optional[int]	    =	autocorrelation_factor

							super().__init__(is_encoder_decoder=lowercase__				,     **lowercase__)


	@property
	def  A(    self):
							return (
							    sum(self.embedding_dimension)
							    + self.num_dynamic_real_features
							    + self.num_time_features
							    + self.num_static_real_features
							    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
							)

