


"""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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





