




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								=       logging.get_logger(__name__)

__A								=       "▁"

__A								=       {"vocab_file": "sentencepiece.bpe.model"}

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

__A								=       {
    "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					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			VOCAB_FILES_NAMES
			snake_case_  						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_  						=			PRETRAINED_VOCAB_FILES_MAP
			snake_case_  						=			['''input_ids''', '''attention_mask''']

			snake_case_  						=			[]
			snake_case_  						=			[]


			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__="<s>"     ,		lowerCamelCase__="</s>"     ,		lowerCamelCase__="</s>"     ,		lowerCamelCase__="<s>"     ,		lowerCamelCase__="<unk>"     ,		lowerCamelCase__="<pad>"     ,		lowerCamelCase__="<mask>"     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__=None     ,		lowerCamelCase__=False     ,		**lowerCamelCase__     ,		)       -> List[Any]:



							'''simple docstring'''
							# Mask token behave like a normal word, i.e. include the space before it
							__lowerCamelCase									=       AddedToken(lowerCamelCase__     ,		lstrip=lowerCamelCase__     ,		rstrip=lowerCamelCase__				) if isinstance(lowerCamelCase__     ,		lowerCamelCase__				) else mask_token

							__lowerCamelCase									=       {} if sp_model_kwargs is None else sp_model_kwargs
							__lowerCamelCase									=       legacy_behaviour

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

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

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

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

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


			def __getstate__( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       self.__dict__.copy()
							__lowerCamelCase									=       None
							__lowerCamelCase									=       self.sp_model.serialized_model_proto()
							return state


			def __setstate__( self     ,		lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       d

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

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


			@property
			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							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				)       -> str:



							'''simple docstring'''
							return self._src_lang


			@src_lang.setter
			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> None:



							'''simple docstring'''
							__lowerCamelCase									=       new_src_lang
							self.set_src_lang_special_tokens(self._src_lang				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = False				)       -> List[int]:



							'''simple docstring'''

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

							__lowerCamelCase									=       [1] * len(self.prefix_tokens				)
							__lowerCamelCase									=       [1] * len(self.suffix_tokens				)
							if token_ids_a is None:
											return prefix_ones + ([0] * len(lowerCamelCase__				)) + suffix_ones
							return prefix_ones + ([0] * len(lowerCamelCase__				)) + ([0] * len(lowerCamelCase__				)) + suffix_ones


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> List[int]:



							'''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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> List[int]:



							'''simple docstring'''
							__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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''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'				)
							__lowerCamelCase									=       src_lang
							__lowerCamelCase									=       self(lowerCamelCase__     ,		add_special_tokens=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)
							__lowerCamelCase									=       self.convert_tokens_to_ids(lowerCamelCase__				)
							__lowerCamelCase									=       tgt_lang_id
							return inputs


			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       {self.convert_ids_to_tokens(lowerCamelCase__				): i for i in range(self.vocab_size				)}
							vocab.update(self.added_tokens_encoder				)
							return vocab


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							return self.sp_model.encode(lowerCamelCase__     ,		out_type=lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> str:



							'''simple docstring'''
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							__lowerCamelCase									=       self.sp_model.PieceToId(lowerCamelCase__				)

							# 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     ,		lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							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     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       ''.join(lowerCamelCase__				).replace(lowerCamelCase__     ,		' '				).strip()
							return out_string


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Tuple[str]:



							'''simple docstring'''
							if not os.path.isdir(lowerCamelCase__				):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
											return
							__lowerCamelCase									=       os.path.join(
							    lowerCamelCase__     ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				) and os.path.isfile(self.vocab_file				):
											copyfile(self.vocab_file     ,		lowerCamelCase__				)
							elif not os.path.isfile(self.vocab_file				):
											with open(lowerCamelCase__     ,		'wb'				) as fi:
															__lowerCamelCase									=       self.sp_model.serialized_model_proto()
															fi.write(lowerCamelCase__				)

							return (out_vocab_file,)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = "eng_Latn"     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = "fra_Latn"     ,		**lowerCamelCase__     ,		)       -> BatchEncoding:



							'''simple docstring'''
							__lowerCamelCase									=       src_lang
							__lowerCamelCase									=       tgt_lang
							return super().prepare_seqaseq_batch(lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							return self.set_src_lang_special_tokens(self.src_lang				)


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							return self.set_tgt_lang_special_tokens(self.tgt_lang				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> None:



							'''simple docstring'''
							__lowerCamelCase									=       self.lang_code_to_id[src_lang]
							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]






			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> None:



							'''simple docstring'''
							__lowerCamelCase									=       self.lang_code_to_id[lang]
							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]








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


__A								=       logging.get_logger(__name__)

__A								=       {
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''lxmert'''
			snake_case_  						=			{}


			def __init__( self     ,		lowerCamelCase__=30_522     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=9_500     ,		lowerCamelCase__=1_600     ,		lowerCamelCase__=400     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=512     ,		lowerCamelCase__=2     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=9     ,		lowerCamelCase__=5     ,		lowerCamelCase__=5     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=4     ,		lowerCamelCase__=6.67     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       num_qa_labels
							__lowerCamelCase									=       num_object_labels
							__lowerCamelCase									=       num_attr_labels
							__lowerCamelCase									=       l_layers
							__lowerCamelCase									=       x_layers
							__lowerCamelCase									=       r_layers
							__lowerCamelCase									=       visual_feat_dim
							__lowerCamelCase									=       visual_pos_dim
							__lowerCamelCase									=       visual_loss_normalizer
							__lowerCamelCase									=       task_matched
							__lowerCamelCase									=       task_mask_lm
							__lowerCamelCase									=       task_obj_predict
							__lowerCamelCase									=       task_qa
							__lowerCamelCase									=       visual_obj_loss
							__lowerCamelCase									=       visual_attr_loss
							__lowerCamelCase									=       visual_feat_loss
							__lowerCamelCase									=       {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
							super().__init__(**lowerCamelCase__				)


