


"""simple docstring"""


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     :   Dict			    =			None


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


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

A     :   Union[str, 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"
        ),
    },
}

A     :   List[str]			    =			{
    "facebook/nllb-large-en-ro": 1_0_2_4,
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
A     :   Optional[int]			    =			["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   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[Any]				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    Optional[int]				      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    Optional[Any]				      =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    Any				      =["""input_ids""", """attention_mask"""]
							__UpperCAmelCase						:    str				      =NllbTokenizer

							__UpperCAmelCase						:    List[int]				      =[]
							__UpperCAmelCase						:    List[int]				      =[]

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

										__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(__a	) 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 			snake_case				(     self	):
										return self._src_lang

							@src_lang.setter
							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			new_src_lang
										self.set_src_lang_special_tokens(self._src_lang	)

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										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 			snake_case				(     self    ,   __a    ,   __a = None	):
										__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 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   **__a	):
										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(__a    ,   add_special_tokens=__a    ,   return_tensors=__a    ,   **__a	)
										__lowerCAmelCase				    =			self.convert_tokens_to_ids(__a	)
										__lowerCAmelCase				    =			tgt_lang_id
										return inputs

							def 			snake_case				(     self    ,   __a    ,   __a = "eng_Latn"    ,   __a = None    ,   __a = "fra_Latn"    ,   **__a    ,   ):
										__lowerCAmelCase				    =			src_lang
										__lowerCAmelCase				    =			tgt_lang
										return super().prepare_seqaseq_batch(__a    ,   __a    ,   **__a	)

							def 			snake_case				(     self	):
										return self.set_src_lang_special_tokens(self.src_lang	)

							def 			snake_case				(     self	):
										return self.set_tgt_lang_special_tokens(self.tgt_lang	)

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			self.convert_tokens_to_ids(__a	)

										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 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			self.convert_tokens_to_ids(__a	)
										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 			snake_case				(     self    ,   __a    ,   __a = None	):
										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(__a	):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory."	)
													return
										__lowerCAmelCase				    =			os.path.join(
										    __a    ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

										return (out_vocab_file,)








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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




