




'''simple docstring'''

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


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

a  :				List[str]									=  {
    """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
    """funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json""",
    """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/config.json""",
    """funnel-transformer/medium-base""": """https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json""",
    """funnel-transformer/intermediate""": (
        """https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"""
    ),
    """funnel-transformer/intermediate-base""": (
        """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"""
    ),
    """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/config.json""",
    """funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json""",
    """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json""",
    """funnel-transformer/xlarge-base""": """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json""",
}






class 						a				(   UpperCAmelCase__     ):
				snake_case_      =		"""funnel"""
				snake_case_      =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """n_head""",
				}


				def __init__(		self    :					List[Any]       ,				lowercase_    :					Any=3_0522       ,				lowercase_    :					Any=[4, 4, 4]       ,				lowercase_    :					Tuple=None       ,				lowercase_    :					str=2       ,				lowercase_    :					Union[str, Any]=768       ,				lowercase_    :					Union[str, Any]=12       ,				lowercase_    :					Dict=64       ,				lowercase_    :					List[Any]=3072       ,				lowercase_    :					Optional[Any]="gelu_new"       ,				lowercase_    :					Dict=0.1       ,				lowercase_    :					int=0.1       ,				lowercase_    :					int=0.0       ,				lowercase_    :					Optional[int]=0.1       ,				lowercase_    :					Union[str, Any]=None       ,				lowercase_    :					str=1e-9       ,				lowercase_    :					List[str]="mean"       ,				lowercase_    :					int="relative_shift"       ,				lowercase_    :					List[str]=True       ,				lowercase_    :					Any=True       ,				lowercase_    :					Optional[Any]=True       ,				**lowercase_    :					Any       ,				):
									snake_case_									=				vocab_size
									snake_case_									=				block_sizes
									snake_case_									=				[1] * len(__lowercase					) if block_repeats is None else block_repeats
									assert len(__lowercase					) == len(
									    self.block_repeats					), "`block_sizes` and `block_repeats` should have the same length."
									snake_case_									=				num_decoder_layers
									snake_case_									=				d_model
									snake_case_									=				n_head
									snake_case_									=				d_head
									snake_case_									=				d_inner
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout
									snake_case_									=				attention_dropout
									snake_case_									=				activation_dropout
									snake_case_									=				initializer_range
									snake_case_									=				initializer_std
									snake_case_									=				layer_norm_eps
									assert pooling_type in [
									    "mean",
									    "max",
									], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
									snake_case_									=				pooling_type
									assert attention_type in [
									    "relative_shift",
									    "factorized",
									], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
									snake_case_									=				attention_type
									snake_case_									=				separate_cls
									snake_case_									=				truncate_seq
									snake_case_									=				pool_q_only

									super().__init__(**__lowercase					)


				@property
				def   A_ (		self    :					List[str]					):
									return sum(self.block_sizes					)


				@num_hidden_layers.setter
				def   A_ (		self    :					Any       ,				lowercase_    :					List[str]					):
									raise NotImplementedError(
									    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''					)


				@property
				def   A_ (		self    :					Tuple					):
									return len(self.block_sizes					)


				@num_blocks.setter
				def   A_ (		self    :					List[Any]       ,				lowercase_    :					Dict					):
									raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''					)





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

from tokenizers import processors

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


if is_sentencepiece_available():
						from .tokenization_nllb import NllbTokenizer
else:
						_UpperCAmelCase	:     Tuple   =None


_UpperCAmelCase	:     int   =logging.get_logger(__name__)


_UpperCAmelCase	:     Dict   ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase	:     Any   ={
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase	:     int   ={
    """facebook/nllb-large-en-ro""": 1024,
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
_UpperCAmelCase	:     Any   =["""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  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     str    =							VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE__					:     Tuple    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE__					:     Dict    =							PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE__					:     Optional[Any]    =							["""input_ids""", """attention_mask"""]
						SCREAMING_SNAKE_CASE__					:     int    =							NllbTokenizer

						SCREAMING_SNAKE_CASE__					:     List[int]    =							[]
						SCREAMING_SNAKE_CASE__					:     List[int]    =							[]




						def __init__( self      ,  __lowercase=None      ,  __lowercase=None      ,  __lowercase="<s>"      ,  __lowercase="</s>"      ,  __lowercase="</s>"      ,  __lowercase="<s>"      ,  __lowercase="<unk>"      ,  __lowercase="<pad>"      ,  __lowercase="<mask>"      ,  __lowercase=None      ,  __lowercase=None      ,  __lowercase=None      ,  __lowercase=False      ,  **__lowercase      ,  )    ->			int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowerCAmelCase_       :					int			 = AddedToken(__lowercase      ,  lstrip=__lowercase      ,  rstrip=__lowercase							) if isinstance(__lowercase      ,  __lowercase							) else mask_token
										lowerCAmelCase_       :					List[Any]			 = legacy_behaviour
										super().__init__(
										    vocab_file=__lowercase      ,  tokenizer_file=__lowercase      ,  bos_token=__lowercase      ,  eos_token=__lowercase      ,  sep_token=__lowercase      ,  cls_token=__lowercase      ,  unk_token=__lowercase      ,  pad_token=__lowercase      ,  mask_token=__lowercase      ,  src_lang=__lowercase      ,  tgt_lang=__lowercase      ,  additional_special_tokens=__lowercase      ,  legacy_behaviour=__lowercase      ,  **__lowercase      ,  )

										lowerCAmelCase_       :					Any			 = vocab_file
										lowerCAmelCase_       :					List[Any]			 = False if not self.vocab_file else True

										lowerCAmelCase_       :					Optional[Any]			 = FAIRSEQ_LANGUAGE_CODES.copy()

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

										self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}							)
										lowerCAmelCase_       :					Optional[Any]			 = {
										    lang_code: self.convert_tokens_to_ids(__lowercase							) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										lowerCAmelCase_       :					Any			 = src_lang if src_lang is not None else '''eng_Latn'''
										lowerCAmelCase_       :					str			 = self.convert_tokens_to_ids(self._src_lang							)
										lowerCAmelCase_       :					Optional[int]			 = tgt_lang
										self.set_src_lang_special_tokens(self._src_lang							)




						@property
						def   lowercase_       ( self							)    ->			str:
										return self._src_lang




						@src_lang.setter
						def   lowercase_       ( self      ,  __lowercase							)    ->			None:
										lowerCAmelCase_       :					Any			 = new_src_lang
										self.set_src_lang_special_tokens(self._src_lang							)




						def   lowercase_       ( 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   lowercase_       ( self      ,  __lowercase      ,  __lowercase = None							)    ->			List[int]:
										lowerCAmelCase_       :					Optional[Any]			 = [self.sep_token_id]
										lowerCAmelCase_       :					Any			 = [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      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  **__lowercase							)    ->			str:
										if src_lang is None or tgt_lang is None:
														raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
										lowerCAmelCase_       :					List[str]			 = src_lang
										lowerCAmelCase_       :					int			 = self(__lowercase      ,  add_special_tokens=__lowercase      ,  return_tensors=__lowercase      ,  **__lowercase							)
										lowerCAmelCase_       :					Dict			 = self.convert_tokens_to_ids(__lowercase							)
										lowerCAmelCase_       :					List[Any]			 = tgt_lang_id
										return inputs




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase = "eng_Latn"      ,  __lowercase = None      ,  __lowercase = "fra_Latn"      ,  **__lowercase      ,  )    ->			BatchEncoding:
										lowerCAmelCase_       :					List[str]			 = src_lang
										lowerCAmelCase_       :					List[str]			 = 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							)    ->			str:
										return self.set_tgt_lang_special_tokens(self.tgt_lang							)




						def   lowercase_       ( self      ,  __lowercase							)    ->			None:
										lowerCAmelCase_       :					List[str]			 = self.convert_tokens_to_ids(__lowercase							)

										if self.legacy_behaviour:
														lowerCAmelCase_       :					Any			 = []
														lowerCAmelCase_       :					List[str]			 = [self.eos_token_id, self.cur_lang_code]
										else:
														lowerCAmelCase_       :					Optional[int]			 = [self.cur_lang_code]
														lowerCAmelCase_       :					List[Any]			 = [self.eos_token_id]

										lowerCAmelCase_       :					Any			 = self.convert_ids_to_tokens(self.prefix_tokens							)
										lowerCAmelCase_       :					List[Any]			 = self.convert_ids_to_tokens(self.suffix_tokens							)

										lowerCAmelCase_       :					Any			 = processors.TemplateProcessing(
										    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str      ,  pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str      ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,  self.prefix_tokens + self.suffix_tokens							)							)      ,  )




						def   lowercase_       ( self      ,  __lowercase							)    ->			None:
										lowerCAmelCase_       :					Dict			 = self.convert_tokens_to_ids(__lowercase							)
										if self.legacy_behaviour:
														lowerCAmelCase_       :					List[Any]			 = []
														lowerCAmelCase_       :					Any			 = [self.eos_token_id, self.cur_lang_code]
										else:
														lowerCAmelCase_       :					Any			 = [self.cur_lang_code]
														lowerCAmelCase_       :					Any			 = [self.eos_token_id]

										lowerCAmelCase_       :					List[Any]			 = self.convert_ids_to_tokens(self.prefix_tokens							)
										lowerCAmelCase_       :					Tuple			 = self.convert_ids_to_tokens(self.suffix_tokens							)

										lowerCAmelCase_       :					Optional[Any]			 = processors.TemplateProcessing(
										    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str      ,  pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str      ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,  self.prefix_tokens + self.suffix_tokens							)							)      ,  )




						def   lowercase_       ( 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
										lowerCAmelCase_       :					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,)