




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_		:					str          =  None


UpperCAmelCase_		:					List[str]          =  logging.get_logger(__name__)


UpperCAmelCase_		:					List[str]          =  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_		:					List[str]          =  {
    "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_		:					Dict          =  {
    "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 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       VOCAB_FILES_NAMES
						UpperCamelCase      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      =       PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      =       ["""input_ids""", """attention_mask"""]
						UpperCamelCase      =       NllbTokenizer

						UpperCamelCase      =       []
						UpperCamelCase      =       []

						def __init__(		self :Any     ,			__snake_case :Union[str, Any]=None     ,			__snake_case :Optional[int]=None     ,			__snake_case :str="<s>"     ,			__snake_case :Union[str, Any]="</s>"     ,			__snake_case :Optional[int]="</s>"     ,			__snake_case :Optional[Any]="<s>"     ,			__snake_case :List[str]="<unk>"     ,			__snake_case :List[str]="<pad>"     ,			__snake_case :List[Any]="<mask>"     ,			__snake_case :List[Any]=None     ,			__snake_case :Any=None     ,			__snake_case :Optional[Any]=None     ,			__snake_case :Optional[Any]=False     ,			**__snake_case :Optional[Any]     ,			):
										'''simple docstring'''

										__magic_name__						:						Any										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else mask_token
										__magic_name__						:						Any										=legacy_behaviour
										super().__init__(
										    vocab_file=__snake_case     ,			tokenizer_file=__snake_case     ,			bos_token=__snake_case     ,			eos_token=__snake_case     ,			sep_token=__snake_case     ,			cls_token=__snake_case     ,			unk_token=__snake_case     ,			pad_token=__snake_case     ,			mask_token=__snake_case     ,			src_lang=__snake_case     ,			tgt_lang=__snake_case     ,			additional_special_tokens=__snake_case     ,			legacy_behaviour=__snake_case     ,			**__snake_case     ,			)

										__magic_name__						:						Optional[int]										=vocab_file
										__magic_name__						:						List[str]										=False if not self.vocab_file else True

										__magic_name__						:						str										=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}			)
										__magic_name__						:						Union[str, Any]										={
										    lang_code: self.convert_tokens_to_ids(__snake_case			) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										__magic_name__						:						Optional[Any]										=src_lang if src_lang is not None else """eng_Latn"""
										__magic_name__						:						Union[str, Any]										=self.convert_tokens_to_ids(self._src_lang			)
										__magic_name__						:						Any										=tgt_lang
										self.set_src_lang_special_tokens(self._src_lang			)

						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										return self._src_lang

						@src_lang.setter
						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						str										=new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)

						def      A__    (		self :List[Any]     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										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      A__    (		self :str     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=[self.sep_token_id]
										__magic_name__						:						List[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      A__    (		self :List[Any]     ,			__snake_case :int     ,			__snake_case :str     ,			__snake_case :Optional[str]     ,			__snake_case :Optional[str]     ,			**__snake_case :Any			):
										'''simple docstring'''

										if src_lang is None or tgt_lang is None:
														raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
										__magic_name__						:						str										=src_lang
										__magic_name__						:						Any										=self(__snake_case     ,			add_special_tokens=__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case			)
										__magic_name__						:						Union[str, Any]										=self.convert_tokens_to_ids(__snake_case			)
										__magic_name__						:						Tuple										=tgt_lang_id
										return inputs

						def      A__    (		self :Optional[Any]     ,			__snake_case :List[str]     ,			__snake_case :str = "eng_Latn"     ,			__snake_case :Optional[List[str]] = None     ,			__snake_case :str = "fra_Latn"     ,			**__snake_case :List[Any]     ,			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=src_lang
										__magic_name__						:						List[Any]										=tgt_lang
										return super().prepare_seqaseq_batch(__snake_case     ,			__snake_case     ,			**__snake_case			)

						def      A__    (		self :str			):
										'''simple docstring'''

										return self.set_src_lang_special_tokens(self.src_lang			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return self.set_tgt_lang_special_tokens(self.tgt_lang			)

						def      A__    (		self :Tuple     ,			__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.convert_tokens_to_ids(__snake_case			)

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

										__magic_name__						:						int										=self.convert_ids_to_tokens(self.prefix_tokens			)
										__magic_name__						:						Dict										=self.convert_ids_to_tokens(self.suffix_tokens			)

										__magic_name__						:						Union[str, 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      A__    (		self :Optional[int]     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.convert_tokens_to_ids(__snake_case			)
										if self.legacy_behaviour:
														__magic_name__						:						Any										=[]
														__magic_name__						:						Optional[Any]										=[self.eos_token_id, self.cur_lang_code]
										else:
														__magic_name__						:						List[Any]										=[self.cur_lang_code]
														__magic_name__						:						Dict										=[self.eos_token_id]

										__magic_name__						:						Dict										=self.convert_ids_to_tokens(self.prefix_tokens			)
										__magic_name__						:						List[str]										=self.convert_ids_to_tokens(self.suffix_tokens			)

										__magic_name__						:						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      A__    (		self :Union[str, Any]     ,			__snake_case :str     ,			__snake_case :Optional[str] = None			):
										'''simple docstring'''

										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(__snake_case			):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory."			)
														return
										__magic_name__						:						str										=os.path.join(
										    __snake_case     ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

										return (out_vocab_file,)










from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase_		:					Dict          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):

						def __init__(		self :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :float     ,			**__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=feature_size
										__magic_name__						:						Union[str, Any]										=sampling_rate
										__magic_name__						:						List[Any]										=padding_value

										__magic_name__						:						List[str]										=kwargs.pop("""padding_side"""     ,			"""right"""			)
										__magic_name__						:						Tuple										=kwargs.pop("""return_attention_mask"""     ,			__snake_case			)

										super().__init__(**__snake_case			)

						def      A__    (		self :Any     ,			__snake_case :Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]     ,			__snake_case :Union[bool, str, PaddingStrategy] = True     ,			__snake_case :Optional[int] = None     ,			__snake_case :bool = False     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			):
										'''simple docstring'''

										if isinstance(__snake_case     ,			(list, tuple)			) and isinstance(processed_features[0]     ,			(dict, BatchFeature)			):
														__magic_name__						:						Union[str, Any]										={
														    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
														}

										# The model's main input name, usually `input_values`, has be passed for padding
										if self.model_input_names[0] not in processed_features:
														raise ValueError(
														    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
														    f" to this method that includes {self.model_input_names[0]}, but you provided"
														    f" {list(processed_features.keys()			)}"			)

										__magic_name__						:						int										=processed_features[self.model_input_names[0]]
										__magic_name__						:						Union[str, Any]										=(
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(__snake_case			) == 0:
														if return_attention_mask:
																		__magic_name__						:						List[str]										=[]
														return processed_features

										# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
										# and rebuild them afterwards if no return_tensors is specified
										# Note that we lose the specific device the tensor may be on for PyTorch

										__magic_name__						:						Optional[int]										=required_input[0]
										if isinstance(__snake_case     ,			(list, tuple)			):
														# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
														__magic_name__						:						Optional[Any]										=0
														while len(required_input[index]			) == 0:
																		index += 1
														if index < len(__snake_case			):
																		__magic_name__						:						List[str]										=required_input[index][0]

										if return_tensors is None:
														if is_tf_tensor(__snake_case			):
																		__magic_name__						:						int										="""tf"""
														elif is_torch_tensor(__snake_case			):
																		__magic_name__						:						str										="""pt"""
														elif isinstance(__snake_case     ,			(int, float, list, tuple, np.ndarray)			):
																		__magic_name__						:						List[Any]										="""np"""
														else:
																		raise ValueError(
																		    f"type of {first_element} unknown: {type(__snake_case			)}. "
																		    """Should be one of a python, numpy, pytorch or tensorflow object."""			)

										for key, value in processed_features.items():
														if isinstance(value[0]     ,			(int, float)			):
																		__magic_name__						:						List[str]										=to_numpy(__snake_case			)
														else:
																		__magic_name__						:						str										=[to_numpy(__snake_case			) for v in value]

        # Convert padding_strategy in PaddingStrategy
										__magic_name__						:						Dict										=self._get_padding_strategies(padding=__snake_case     ,			max_length=__snake_case			)

										__magic_name__						:						Optional[Any]										=processed_features[self.model_input_names[0]]

										__magic_name__						:						Dict										=len(__snake_case			)
										if not all(len(__snake_case			) == batch_size for v in processed_features.values()			):
														raise ValueError("""Some items in the output dictionary have a different batch size than others."""			)

										__magic_name__						:						Optional[int]										=[]
										for i in range(__snake_case			):
														__magic_name__						:						Any										={k: v[i] for k, v in processed_features.items()}
														# truncation
														__magic_name__						:						List[str]										=self._truncate(
														    __snake_case     ,			max_length=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			truncation=__snake_case     ,			)
														truncated_inputs.append(__snake_case			)

										if padding_strategy == PaddingStrategy.LONGEST:
														# make sure that `max_length` cannot be longer than the longest truncated length
														__magic_name__						:						Optional[int]										=max(len(input_slice[self.model_input_names[0]]			) for input_slice in truncated_inputs			)
														__magic_name__						:						Tuple										=PaddingStrategy.MAX_LENGTH

										__magic_name__						:						str										={}
										for i in range(__snake_case			):
														# padding
														__magic_name__						:						List[str]										=self._pad(
														    truncated_inputs[i]     ,			max_length=__snake_case     ,			padding_strategy=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			return_attention_mask=__snake_case     ,			)

														for key, value in outputs.items():
																		if key not in batch_outputs:
																						__magic_name__						:						Dict										=[]
																		if value.dtype is np.dtype(np.floataa			):
																						__magic_name__						:						Optional[int]										=value.astype(np.floataa			)
																		batch_outputs[key].append(__snake_case			)

										return BatchFeature(__snake_case     ,			tensor_type=__snake_case			)

						def      A__    (		self :Any     ,			__snake_case :Union[Dict[str, np.ndarray], BatchFeature]     ,			__snake_case :Optional[int] = None     ,			__snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Dict										=processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
														__magic_name__						:						Any										=len(__snake_case			)

										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__						:						Dict										=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__						:						List[Any]										=padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case			) < max_length

										if return_attention_mask and "attention_mask" not in processed_features:
														__magic_name__						:						int										=np.ones(len(__snake_case			)     ,			dtype=np.intaa			)

										if needs_to_be_padded:
														__magic_name__						:						List[Any]										=max_length - len(__snake_case			)
														if self.padding_side == "right":
																		if return_attention_mask:
																						__magic_name__						:						str										=np.pad(
																						    processed_features["""attention_mask"""]     ,			(0, difference)			)
																		__magic_name__						:						Tuple										=((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																		__magic_name__						:						str										=np.pad(
																		    __snake_case     ,			__snake_case     ,			"""constant"""     ,			constant_values=self.padding_value			)
														elif self.padding_side == "left":
																		if return_attention_mask:
																						__magic_name__						:						str										=np.pad(
																						    processed_features["""attention_mask"""]     ,			(difference, 0)			)
																		__magic_name__						:						Optional[int]										=((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																		__magic_name__						:						List[Any]										=np.pad(
																		    __snake_case     ,			__snake_case     ,			"""constant"""     ,			constant_values=self.padding_value			)
														else:
																		raise ValueError("""Invalid padding strategy:""" + str(self.padding_side			)			)

										return processed_features

						def      A__    (		self :Optional[Any]     ,			__snake_case :Union[Dict[str, np.ndarray], BatchFeature]     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			):
										'''simple docstring'''

										if not truncation:
														return processed_features
										elif truncation and max_length is None:
														raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""			)

										__magic_name__						:						Union[str, Any]										=processed_features[self.model_input_names[0]]

										# find `max_length` that fits `pad_to_multiple_of`
										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__						:						List[str]										=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__						:						Any										=len(__snake_case			) > max_length

										if needs_to_be_truncated:
														__magic_name__						:						List[Any]										=processed_features[self.model_input_names[0]][:max_length]
														if "attention_mask" in processed_features:
																		__magic_name__						:						List[str]										=processed_features["""attention_mask"""][:max_length]

										return processed_features


						def      A__    (		self :List[Any]     ,			__snake_case :str=False     ,			__snake_case :Optional[int]=None			):
										'''simple docstring'''

										if padding is not False:
														if padding is True:
																		__magic_name__						:						Union[str, Any]										=PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
														elif not isinstance(__snake_case     ,			__snake_case			):
																		__magic_name__						:						Optional[int]										=PaddingStrategy(__snake_case			)
														elif isinstance(__snake_case     ,			__snake_case			):
																		__magic_name__						:						Any										=padding
										else:
														__magic_name__						:						Any										=PaddingStrategy.DO_NOT_PAD

										# Set max length if needed
										if max_length is None:
														if padding_strategy == PaddingStrategy.MAX_LENGTH:
																		raise ValueError(
																		    f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"			)

        # Test if we have a padding value
										if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
														raise ValueError(
														    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
														    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""			)

										return padding_strategy




