
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:
			_A   =     None


_A   =     logging.get_logger(__name__)


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

_A   =     {
    "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"
        ),
    },
}

_A   =     {
    "facebook/nllb-large-en-ro": 10_24,
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
_A   =     ["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 	_lowerCAmelCase					(	__a       ):
				_lowercase					=VOCAB_FILES_NAMES
				_lowercase					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase					=PRETRAINED_VOCAB_FILES_MAP
				_lowercase					=['''input_ids''', '''attention_mask''']
				_lowercase					=NllbTokenizer

				_lowercase					=[]
				_lowercase					=[]




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

							lowerCAmelCase_						=	vocab_file
							lowerCAmelCase_						=	False if not self.vocab_file else True

							lowerCAmelCase_						=	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_						=	{
							    lang_code: self.convert_tokens_to_ids(_UpperCamelCase     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							lowerCAmelCase_						=	src_lang if src_lang is not None else "eng_Latn"
							lowerCAmelCase_						=	self.convert_tokens_to_ids(self._src_lang     )
							lowerCAmelCase_						=	tgt_lang
							self.set_src_lang_special_tokens(self._src_lang     )




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




				@src_lang.setter
				def      __a						(					self				,				_UpperCamelCase     )				->      None:
							lowerCAmelCase_						=	new_src_lang
							self.set_src_lang_special_tokens(self._src_lang     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = 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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None     )				->      List[int]:
							lowerCAmelCase_						=	[self.sep_token_id]
							lowerCAmelCase_						=	[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				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      Optional[int]:
							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_						=	src_lang
							lowerCAmelCase_						=	self(_UpperCamelCase				,				add_special_tokens=_UpperCamelCase				,				return_tensors=_UpperCamelCase				,				**_UpperCamelCase     )
							lowerCAmelCase_						=	self.convert_tokens_to_ids(_UpperCamelCase     )
							lowerCAmelCase_						=	tgt_lang_id
							return inputs




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = "eng_Latn"				,				_UpperCamelCase = None				,				_UpperCamelCase = "fra_Latn"				,				**_UpperCamelCase				,				)				->      BatchEncoding:
							lowerCAmelCase_						=	src_lang
							lowerCAmelCase_						=	tgt_lang
							return super().prepare_seqaseq_batch(_UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							return self.set_src_lang_special_tokens(self.src_lang     )




				def      __a						(					self     )				->      str:
							return self.set_tgt_lang_special_tokens(self.tgt_lang     )




				def      __a						(					self				,				_UpperCamelCase     )				->      None:
							lowerCAmelCase_						=	self.convert_tokens_to_ids(_UpperCamelCase     )

							if self.legacy_behaviour:
										lowerCAmelCase_						=	[]
										lowerCAmelCase_						=	[self.eos_token_id, self.cur_lang_code]
							else:
										lowerCAmelCase_						=	[self.cur_lang_code]
										lowerCAmelCase_						=	[self.eos_token_id]

							lowerCAmelCase_						=	self.convert_ids_to_tokens(self.prefix_tokens     )
							lowerCAmelCase_						=	self.convert_ids_to_tokens(self.suffix_tokens     )

							lowerCAmelCase_						=	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				,				_UpperCamelCase     )				->      None:
							lowerCAmelCase_						=	self.convert_tokens_to_ids(_UpperCamelCase     )
							if self.legacy_behaviour:
										lowerCAmelCase_						=	[]
										lowerCAmelCase_						=	[self.eos_token_id, self.cur_lang_code]
							else:
										lowerCAmelCase_						=	[self.cur_lang_code]
										lowerCAmelCase_						=	[self.eos_token_id]

							lowerCAmelCase_						=	self.convert_ids_to_tokens(self.prefix_tokens     )
							lowerCAmelCase_						=	self.convert_ids_to_tokens(self.suffix_tokens     )

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

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

							return (out_vocab_file,)





import functools
import operator

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


_A   =     logging.get_logger(__name__)

_A   =     {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''sew'''




				def __init__(					self				,				_UpperCamelCase=32				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase=2				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-5				,				_UpperCamelCase="group"				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_UpperCamelCase=False				,				_UpperCamelCase=128				,				_UpperCamelCase=16				,				_UpperCamelCase=True				,				_UpperCamelCase=0.05				,				_UpperCamelCase=10				,				_UpperCamelCase=2				,				_UpperCamelCase=0.0				,				_UpperCamelCase=10				,				_UpperCamelCase=0				,				_UpperCamelCase="mean"				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=256				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=2				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							super().__init__(**_UpperCamelCase				,				pad_token_id=_UpperCamelCase				,				bos_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	feat_extract_norm
							lowerCAmelCase_						=	feat_extract_activation
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	conv_bias
							lowerCAmelCase_						=	num_conv_pos_embeddings
							lowerCAmelCase_						=	num_conv_pos_embedding_groups
							lowerCAmelCase_						=	len(self.conv_dim     )
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	squeeze_factor
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	feat_proj_dropout
							lowerCAmelCase_						=	final_dropout
							lowerCAmelCase_						=	layerdrop
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	vocab_size

							if (
							    (len(self.conv_stride     ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCAmelCase_						=	apply_spec_augment
							lowerCAmelCase_						=	mask_time_prob
							lowerCAmelCase_						=	mask_time_length
							lowerCAmelCase_						=	mask_time_min_masks
							lowerCAmelCase_						=	mask_feature_prob
							lowerCAmelCase_						=	mask_feature_length
							lowerCAmelCase_						=	mask_feature_min_masks

							# ctc loss
							lowerCAmelCase_						=	ctc_loss_reduction
							lowerCAmelCase_						=	ctc_zero_infinity

							# sequence classification
							lowerCAmelCase_						=	use_weighted_layer_sum
							lowerCAmelCase_						=	classifier_proj_size




				@property
				def      __a						(					self     )				->      Optional[Any]:
							return functools.reduce(operator.mul				,				self.conv_stride				,				1     )



