





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase 				=     {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase 				=     ["""YolosFeatureExtractor"""]
						_lowerCAmelCase 				=     ["""YolosImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase 				=     [
						    """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """YolosForObjectDetection""",
						    """YolosModel""",
						    """YolosPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_yolos import YolosFeatureExtractor
												from .image_processing_yolos import YolosImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_yolos import (
												    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    YolosForObjectDetection,
												    YolosModel,
												    YolosPreTrainedModel,
												)


else:
						import sys

						_lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     """▁"""

_lowerCAmelCase 				=     {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_lowerCAmelCase 				=     {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

_lowerCAmelCase 				=     {
    """facebook/m2m100_418M""": 1_024,
}

# fmt: off
_lowerCAmelCase 				=     {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							PRETRAINED_VOCAB_FILES_MAP
      a						       =							['''input_ids''', '''attention_mask''']

      a						       =							[]
      a						       =							[]
      def __init__(		self      ,       a__      ,       a__      ,       a__=None      ,       a__=None      ,       a__="<s>"      ,       a__="</s>"      ,       a__="</s>"      ,       a__="<pad>"      ,       a__="<unk>"      ,       a__="m2m100"      ,       a__ = None      ,       a__=8      ,       **a__      ,       ):
         A_     :				Any			      =	{} if sp_model_kwargs is None else sp_model_kwargs

         A_     :				Dict			      =	language_codes
         A_     :				Union[str, Any]			      =	FAIRSEQ_LANGUAGE_CODES[language_codes]
         A_     :				int			      =	{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

         A_     :				Union[str, Any]			      =	kwargs.get("""additional_special_tokens"""      ,       []       )
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(a__       )
             for lang_code in fairseq_language_code
             if self.get_lang_token(a__       ) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=a__      ,       tgt_lang=a__      ,       bos_token=a__      ,       eos_token=a__      ,       sep_token=a__      ,       unk_token=a__      ,       pad_token=a__      ,       language_codes=a__      ,       sp_model_kwargs=self.sp_model_kwargs      ,       num_madeup_words=a__      ,       **a__      ,       )

         A_     :				List[str]			      =	vocab_file
         A_     :				Dict			      =	load_json(a__       )
         A_     :				str			      =	{v: k for k, v in self.encoder.items()}
         A_     :				Optional[Any]			      =	spm_file
         A_     :				Any			      =	load_spm(a__      ,       self.sp_model_kwargs       )

         A_     :				Union[str, Any]			      =	len(self.encoder       )

         A_     :				Dict			      =	{
             self.get_lang_token(a__       ): self.encoder_size + i for i, lang_code in enumerate(a__       )
         }
         A_     :				Optional[Any]			      =	{lang_code: self.encoder_size + i for i, lang_code in enumerate(a__       )}
         A_     :				Any			      =	{v: k for k, v in self.lang_token_to_id.items()}

         A_     :				Tuple			      =	src_lang if src_lang is not None else """en"""
         A_     :				str			      =	tgt_lang
         A_     :				Optional[int]			      =	self.get_lang_id(self._src_lang       )
         self.set_src_lang_special_tokens(self._src_lang       )

         A_     :				Optional[int]			      =	num_madeup_words
      @property
      def 		_lowerCamelCase      (		self       ):
         return len(self.encoder       ) + len(self.lang_token_to_id       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self._src_lang
      @src_lang.setter
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Any			      =	new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.sp_model.encode(a__      ,       out_type=a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
         return self.encoder.get(a__      ,       self.encoder[self.unk_token]       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
         return self.decoder.get(a__      ,       self.unk_token       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[str]			      =	[]
         A_     :				List[str]			      =	""""""
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += self.sp_model.decode(a__       ) + token
               A_     :				int			      =	[]
            else:
               current_sub_tokens.append(a__       )
         out_string += self.sp_model.decode(a__       )
         return out_string.strip()
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None      ,       a__ = False       ):

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

         A_     :				List[Any]			      =	[1] * len(self.prefix_tokens       )
         A_     :				Dict			      =	[1] * len(self.suffix_tokens       )
         if token_ids_a is None:
            return prefix_ones + ([0] * len(a__       )) + suffix_ones
         return prefix_ones + ([0] * len(a__       )) + ([0] * len(a__       )) + suffix_ones
      def 		_lowerCamelCase      (		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 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	{self.convert_ids_to_tokens(a__       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab
      def __getstate__(		self       ):
         A_     :				List[str]			      =	self.__dict__.copy()
         A_     :				str			      =	None
         return state
      def __setstate__(		self      ,       a__       ):
         A_     :				Any			      =	d

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

         A_     :				int			      =	load_spm(self.spm_file      ,       self.sp_model_kwargs       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				Optional[Any]			      =	Path(a__       )
         if not save_dir.is_dir():
            raise OSError(F"""{save_directory} should be a directory"""       )
         A_     :				List[Any]			      =	save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
         )
         A_     :				Tuple			      =	save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
         )

         save_json(self.encoder      ,       a__       )

         if os.path.abspath(self.spm_file       ) != os.path.abspath(a__       ) and os.path.isfile(self.spm_file       ):
            copyfile(self.spm_file      ,       a__       )
         elif not os.path.isfile(self.spm_file       ):
            with open(a__      ,       """wb"""       ) as fi:
               A_     :				Tuple			      =	self.sp_model.serialized_model_proto()
               fi.write(a__       )

         return (str(a__       ), str(a__       ))
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = "en"      ,       a__ = None      ,       a__ = "ro"      ,       **a__      ,       ):
         A_     :				Optional[int]			      =	src_lang
         A_     :				List[Any]			      =	tgt_lang
         self.set_src_lang_special_tokens(self.src_lang       )
         return super().prepare_seqaseq_batch(a__      ,       a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       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"""       )
         A_     :				List[str]			      =	src_lang
         A_     :				Union[str, Any]			      =	self(a__      ,       add_special_tokens=a__      ,       **a__       )
         A_     :				Any			      =	self.get_lang_id(a__       )
         A_     :				List[Any]			      =	tgt_lang_id
         return inputs
      def 		_lowerCamelCase      (		self       ):
         self.set_src_lang_special_tokens(self.src_lang       )
      def 		_lowerCamelCase      (		self       ):
         self.set_tgt_lang_special_tokens(self.tgt_lang       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Tuple			      =	self.get_lang_token(a__       )
         A_     :				Optional[Any]			      =	self.lang_token_to_id[lang_token]
         A_     :				Optional[Any]			      =	[self.cur_lang_id]
         A_     :				Union[str, Any]			      =	[self.eos_token_id]
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				str			      =	self.get_lang_token(a__       )
         A_     :				Optional[int]			      =	self.lang_token_to_id[lang_token]
         A_     :				str			      =	[self.cur_lang_id]
         A_     :				Optional[int]			      =	[self.eos_token_id]
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.lang_code_to_token[lang]






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Union[str, Any]			      =	self.get_lang_token(a__       )
         return self.lang_token_to_id[lang_token]






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	sentencepiece.SentencePieceProcessor(**_lowerCAmelCase     )
   spm.Load(str(_lowerCAmelCase     )     )
   return spm



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   with open(_lowerCAmelCase  ,"""r"""     ) as f:
      return json.load(_lowerCAmelCase     )







def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   with open(_lowerCAmelCase  ,"""w"""     ) as f:
      json.dump(_lowerCAmelCase  ,_lowerCAmelCase  ,indent=2     )






