

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[str]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Any						=     """▁"""

SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""vocab_file""": """sentencepiece.bpe.model"""}

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

SCREAMING_SNAKE_CASE		:Dict						=     {
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE		:Union[str, 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 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Union[str, Any]										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Tuple										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Any										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:List[str]										=      ["""input_ids""", """attention_mask"""]

     UpperCamelCase_	:List[int]										=      []
     UpperCamelCase_	:List[int]										=      []






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

         UpperCamelCase_			  =						{} if sp_model_kwargs is None else sp_model_kwargs
         UpperCamelCase_			  =						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		,						)

         UpperCamelCase_			  =						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(str(_lowercase	)	)
         UpperCamelCase_			  =						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
         UpperCamelCase_			  =						{"<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
         UpperCamelCase_			  =						1

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

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

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






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






     def __setstate__(				self		,						_lowercase	)->     Tuple:
         UpperCamelCase_			  =						d

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

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






     @property
     def 	UpperCAmelCase_				(				self	)->     str:
         return len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset + 1  # Plus 1 for the mask token






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






     @src_lang.setter
     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         UpperCamelCase_			  =						new_src_lang
         self.set_src_lang_special_tokens(self._src_lang	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = False	)->     List[int]:

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

         UpperCamelCase_			  =						[1] * len(self.prefix_tokens	)
         UpperCamelCase_			  =						[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 	UpperCAmelCase_				(				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 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:
         UpperCamelCase_			  =						[self.sep_token_id]
         UpperCamelCase_			  =						[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 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						**_lowercase	)->     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"	)
         UpperCamelCase_			  =						src_lang
         UpperCamelCase_			  =						self(_lowercase		,						add_special_tokens=_lowercase		,						return_tensors=_lowercase		,						**_lowercase	)
         UpperCamelCase_			  =						self.convert_tokens_to_ids(_lowercase	)
         UpperCamelCase_			  =						tgt_lang_id
         return inputs






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






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     List[str]:
         return self.sp_model.encode(_lowercase		,						out_type=_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     List[Any]:
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         UpperCamelCase_			  =						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 	UpperCAmelCase_				(				self		,						_lowercase	)->     Tuple:
         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 	UpperCAmelCase_				(				self		,						_lowercase	)->     Optional[int]:
         UpperCamelCase_			  =						"".join(_lowercase	).replace(_lowercase		,						" "	).strip()
         return out_string






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Tuple[str]:
         if not os.path.isdir(_lowercase	):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
             return
         UpperCamelCase_			  =						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:
                 UpperCamelCase_			  =						self.sp_model.serialized_model_proto()
                 fi.write(_lowercase	)

         return (out_vocab_file,)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = "eng_Latn"		,						_lowercase = None		,						_lowercase = "fra_Latn"		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						src_lang
         UpperCamelCase_			  =						tgt_lang
         return super().prepare_seqaseq_batch(_lowercase		,						_lowercase		,						**_lowercase	)






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






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         return self.set_tgt_lang_special_tokens(self.tgt_lang	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         UpperCamelCase_			  =						self.lang_code_to_id[src_lang]
         if self.legacy_behaviour:
             UpperCamelCase_			  =						[]
             UpperCamelCase_			  =						[self.eos_token_id, self.cur_lang_code]
         else:
             UpperCamelCase_			  =						[self.cur_lang_code]
             UpperCamelCase_			  =						[self.eos_token_id]






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         UpperCamelCase_			  =						self.lang_code_to_id[lang]
         if self.legacy_behaviour:
             UpperCamelCase_			  =						[]
             UpperCamelCase_			  =						[self.eos_token_id, self.cur_lang_code]
         else:
             UpperCamelCase_			  =						[self.cur_lang_code]
             UpperCamelCase_			  =						[self.eos_token_id]



import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     logging.get_logger(__name__)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[int]										=      """mask2former"""
     UpperCamelCase_	:Dict										=      ["""swin"""]
     UpperCamelCase_	:List[Any]										=      {"""hidden_size""": """hidden_dim"""}






     def __init__(				self		,						_lowercase = None		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 1_024		,						_lowercase = "relu"		,						_lowercase = 6		,						_lowercase = 10		,						_lowercase = 8		,						_lowercase = 0.0		,						_lowercase = 2_048		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 4		,						_lowercase = 255		,						_lowercase = 100		,						_lowercase = 0.1		,						_lowercase = 2.0		,						_lowercase = 5.0		,						_lowercase = 5.0		,						_lowercase = 12_544		,						_lowercase = 3.0		,						_lowercase = 0.75		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = True		,						_lowercase = [4, 8, 16, 32]		,						_lowercase = None		,						**_lowercase		,						)->     Union[str, Any]:
         if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
             UpperCamelCase_			  =						CONFIG_MAPPING["swin"](
                 image_size=224		,						in_channels=3		,						patch_size=4		,						embed_dim=96		,						depths=[2, 2, 18, 2]		,						num_heads=[3, 6, 12, 24]		,						window_size=7		,						drop_path_rate=0.3		,						use_absolute_embeddings=_lowercase		,						out_features=["stage1", "stage2", "stage3", "stage4"]		,						)

         if isinstance(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						config_class.from_dict(_lowercase	)

         # verify that the backbone is supported
         if backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(
                 F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
                 F"Supported model types: {','.join(self.backbones_supported	)}"	)

         UpperCamelCase_			  =						backbone_config
         UpperCamelCase_			  =						feature_size
         UpperCamelCase_			  =						mask_feature_size
         UpperCamelCase_			  =						hidden_dim
         UpperCamelCase_			  =						encoder_feedforward_dim
         UpperCamelCase_			  =						activation_function
         UpperCamelCase_			  =						encoder_layers
         UpperCamelCase_			  =						decoder_layers
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						dropout
         UpperCamelCase_			  =						dim_feedforward
         UpperCamelCase_			  =						pre_norm
         UpperCamelCase_			  =						enforce_input_projection
         UpperCamelCase_			  =						common_stride
         UpperCamelCase_			  =						ignore_value
         UpperCamelCase_			  =						num_queries
         UpperCamelCase_			  =						no_object_weight
         UpperCamelCase_			  =						class_weight
         UpperCamelCase_			  =						mask_weight
         UpperCamelCase_			  =						dice_weight
         UpperCamelCase_			  =						train_num_points
         UpperCamelCase_			  =						oversample_ratio
         UpperCamelCase_			  =						importance_sample_ratio
         UpperCamelCase_			  =						init_std
         UpperCamelCase_			  =						init_xavier_std
         UpperCamelCase_			  =						use_auxiliary_loss
         UpperCamelCase_			  =						feature_strides
         UpperCamelCase_			  =						output_auxiliary_logits
         UpperCamelCase_			  =						decoder_layers

         super().__init__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						**_lowercase	)->     Optional[int]:
         return cls(
             backbone_config=_lowercase		,						**_lowercase		,						)






     def 	UpperCAmelCase_				(				self	)->     Dict[str, any]:
         UpperCamelCase_			  =						copy.deepcopy(self.__dict__	)
         UpperCamelCase_			  =						self.backbone_config.to_dict()
         UpperCamelCase_			  =						self.__class__.model_type
         return output
