





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


UpperCAmelCase	:	Optional[int]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	List[str]		     =       """▁"""

UpperCAmelCase	:	Union[str, Any]		     =       {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase	:	Tuple		     =       {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

UpperCAmelCase	:	str		     =       {
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
UpperCAmelCase	:	Tuple		     =       ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Union[str, Any]						=			VOCAB_FILES_NAMES
      _lowercase				:					Dict						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					List[str]						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					int						=			["""input_ids""", """attention_mask"""]

      _lowercase				:					List[int]						=			[]
      _lowercase				:					List[int]						=			[]







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="<unk>"		,      lowerCAmelCase__="<pad>"		,      lowerCAmelCase__="<mask>"		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						None:



        '''simple docstring'''




        a__	:					Any										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else mask_token

        a__	:					str										={} if sp_model_kwargs is None else sp_model_kwargs

        a__	:					Dict										=kwargs.get("additional_special_tokens"		,      []							)
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=lowerCAmelCase__		,      tgt_lang=lowerCAmelCase__		,      eos_token=lowerCAmelCase__		,      unk_token=lowerCAmelCase__		,      sep_token=lowerCAmelCase__		,      cls_token=lowerCAmelCase__		,      pad_token=lowerCAmelCase__		,      mask_token=lowerCAmelCase__		,      sp_model_kwargs=self.sp_model_kwargs		,      **lowerCAmelCase__		,      )

        a__	:					Union[str, Any]										=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(str(lowerCAmelCase__							)							)
        a__	:					str										=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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        a__	:					Optional[int]										={"<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
        a__	:					Dict										=1

        a__	:					List[Any]										=len(self.sp_model							)
        a__	:					List[str]										={
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase__							)
        }
        a__	:					Optional[int]										={v: k for k, v in self.lang_code_to_id.items()}
        a__	:					Any										=len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

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

        a__	:					Optional[int]										=src_lang if src_lang is not None else "en_XX"
        a__	:					int										=self.lang_code_to_id[self._src_lang]
        a__	:					Dict										=tgt_lang
        self.set_src_lang_special_tokens(self._src_lang							)







      @property
      def    _lowercase  (    self							)   ->						int:



        '''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'''




        a__	:					Tuple										=new_src_lang
        self.set_src_lang_special_tokens(self._src_lang							)







      def __getstate__(    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Tuple										=self.__dict__.copy()
        a__	:					List[Any]										=None
        return state







      def __setstate__(    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Union[str, Any]										=d

        # for backward compatibility
        if not hasattr(self		,      "sp_model_kwargs"							):
          a__	:					Optional[int]										={}

        a__	:					str										=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(self.vocab_file							)







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										={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__							)   ->						int:



        '''simple docstring'''




        if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
        a__	:					Union[str, Any]										=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__							)   ->						str:



        '''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__							)   ->						Dict:



        '''simple docstring'''




        a__	:					str										=[]
        a__	:					Tuple										=""
        a__	:					str										=False
        for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
            if not prev_is_special:
              out_string += " "
            out_string += self.sp_model.decode(lowerCAmelCase__							) + token
            a__	:					Optional[int]										=True
            a__	:					Dict										=[]
          else:
            current_sub_tokens.append(lowerCAmelCase__							)
            a__	:					Optional[Any]										=False
        out_string += self.sp_model.decode(lowerCAmelCase__							)
        return out_string.strip()







      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
        a__	:					List[str]										=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:
            a__	:					Optional[int]										=self.sp_model.serialized_model_proto()
            fi.write(lowerCAmelCase__							)

        return (out_vocab_file,)







      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__							)

        a__	:					List[str]										=[1] * len(self.prefix_tokens							)
        a__	:					List[str]										=[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__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''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"							)
        a__	:					Any										=src_lang
        a__	:					Optional[int]										=self(lowerCAmelCase__		,      add_special_tokens=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Any										=self.convert_tokens_to_ids(lowerCAmelCase__							)
        a__	:					int										=tgt_lang_id
        return inputs







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = "en_XX"		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = "ro_RO"		,      **lowerCAmelCase__		,      )   ->						BatchEncoding:



        '''simple docstring'''




        a__	:					Optional[Any]										=src_lang
        a__	:					Optional[int]										=tgt_lang
        return super().prepare_seqaseq_batch(lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        return self.set_src_lang_special_tokens(self.src_lang							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return self.set_tgt_lang_special_tokens(self.tgt_lang							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Tuple										=self.lang_code_to_id[src_lang]
        a__	:					Optional[Any]										=[self.cur_lang_code_id]
        a__	:					Optional[int]										=[self.eos_token_id]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Tuple										=self.lang_code_to_id[tgt_lang]
        a__	:					List[Any]										=[self.cur_lang_code_id]
        a__	:					str										=[self.eos_token_id]











'''simple docstring'''





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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "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 __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



