
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__		:     List[str]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     List[Any]     			=      '▁'

UpperCAmelCase__		:     Dict     			=      {'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase__		:     Union[str, Any]     			=      {
    '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__		:     Dict     			=      {
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
UpperCAmelCase__		:     str     			=      ['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 UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    VOCAB_FILES_NAMES
   __UpperCamelCase		:    str 				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase		:    Optional[int] 				=    PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase		:    List[Any] 				=    ['''input_ids''', '''attention_mask''']

   __UpperCamelCase		:    List[int] 				=    []
   __UpperCamelCase		:    List[int] 				=    []


   def __init__(					self     :							Tuple			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Optional[int]=None			,			lowerCAmelCase_     :							Any="</s>"			,			lowerCAmelCase_     :							Optional[Any]="</s>"			,			lowerCAmelCase_     :							List[Any]="<s>"			,			lowerCAmelCase_     :							int="<unk>"			,			lowerCAmelCase_     :							List[Any]="<pad>"			,			lowerCAmelCase_     :							List[Any]="<mask>"			,			lowerCAmelCase_     :							Optional[Dict[str, Any]] = None			,			**lowerCAmelCase_     :							List[str]			,			):

      """simple docstring"""
      # Mask token behave like a normal word, i.e. include the space before it
      _A:  Dict					=       AddedToken(lowerCAmelCase_			,			lstrip=lowerCAmelCase_			,			rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ) else mask_token

      _A:  Optional[int]					=       {} 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:  Dict					=       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.Load(str(lowerCAmelCase_     )     )
      _A:  Optional[int]					=       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:  Optional[int]					=       1

      _A:  Dict					=       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:  Tuple					=       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:  Any					=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

      _A:  str					=       src_lang if src_lang is not None else '''en_XX'''
      _A:  Dict					=       self.lang_code_to_id[self._src_lang]
      _A:  List[str]					=       tgt_lang
      self.set_src_lang_special_tokens(self._src_lang     )


   @property
   def  __magic_name__				(					self     :							str     ):

      """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  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      return self._src_lang


   @src_lang.setter
   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      _A:  Optional[Any]					=       new_src_lang
      self.set_src_lang_special_tokens(self._src_lang     )


   def __getstate__(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Tuple					=       self.__dict__.copy()
      _A:  int					=       None
      return state


   def __setstate__(					self     :							str			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  Dict					=       d

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

      _A:  Tuple					=       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.Load(self.vocab_file     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  Optional[Any]					=       {self.convert_ids_to_tokens(lowerCAmelCase_     ): i for i in range(self.vocab_size     )}
      vocab.update(self.added_tokens_encoder     )
      return vocab


   def  __magic_name__				(					self     :							Dict			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      return self.sp_model.encode(lowerCAmelCase_			,			out_type=lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Dict			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _A:  Dict					=       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  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							int     ):

      """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  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      _A:  List[Any]					=       []
      _A:  Union[str, Any]					=       ''''''
      _A:  List[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[Any]					=       True
            _A:  Dict					=       []
         else:
            current_sub_tokens.append(lowerCAmelCase_     )
            _A:  List[Any]					=       False
      out_string += self.sp_model.decode(lowerCAmelCase_     )
      return out_string.strip()


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[str] = None     ):

      """simple docstring"""
      if not os.path.isdir(lowerCAmelCase_     ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
         return
      _A:  Union[str, Any]					=       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  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							List[int]			,			lowerCAmelCase_     :							Optional[List[int]] = None			,			lowerCAmelCase_     :							bool = False     ):

      """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:  int					=       [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  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							List[int]			,			lowerCAmelCase_     :							Optional[List[int]] = None     ):

      """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  __magic_name__				(					self     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[str]			,			lowerCAmelCase_     :							Optional[str]			,			**lowerCAmelCase_     :							Union[str, Any]     ):

      """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:  Union[str, Any]					=       src_lang
      _A:  List[Any]					=       self(lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_			,			return_tensors=lowerCAmelCase_			,			**lowerCAmelCase_     )
      _A:  int					=       self.convert_tokens_to_ids(lowerCAmelCase_     )
      _A:  Optional[int]					=       tgt_lang_id
      return inputs


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							str = "en_XX"			,			lowerCAmelCase_     :							Optional[List[str]] = None			,			lowerCAmelCase_     :							str = "ro_RO"			,			**lowerCAmelCase_     :							List[str]			,			):

      """simple docstring"""
      _A:  Any					=       src_lang
      _A:  Dict					=       tgt_lang
      return super().prepare_seqaseq_batch(lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      return self.set_src_lang_special_tokens(self.src_lang     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      return self.set_tgt_lang_special_tokens(self.tgt_lang     )


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       self.lang_code_to_id[src_lang]
      _A:  List[Any]					=       [self.cur_lang_code_id]
      _A:  Tuple					=       [self.eos_token_id]


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.lang_code_to_id[tgt_lang]
      _A:  List[Any]					=       [self.cur_lang_code_id]
      _A:  Dict					=       [self.eos_token_id]





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''maskformer-swin'''

   __UpperCamelCase		:    Any 				=    {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=2_2_4			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Dict=9_6			,			lowerCAmelCase_     :							Union[str, Any]=[2, 2, 6, 2]			,			lowerCAmelCase_     :							Optional[Any]=[3, 6, 1_2, 2_4]			,			lowerCAmelCase_     :							Optional[Any]=7			,			lowerCAmelCase_     :							Optional[Any]=4.0			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							Optional[Any]=0.0			,			lowerCAmelCase_     :							Union[str, Any]=0.0			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]="gelu"			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							str=1e-5			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Any=None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[Any]					=       image_size
      _A:  Optional[int]					=       patch_size
      _A:  Optional[Any]					=       num_channels
      _A:  str					=       embed_dim
      _A:  Any					=       depths
      _A:  str					=       len(lowerCAmelCase_     )
      _A:  Any					=       num_heads
      _A:  int					=       window_size
      _A:  Dict					=       mlp_ratio
      _A:  str					=       qkv_bias
      _A:  List[str]					=       hidden_dropout_prob
      _A:  List[Any]					=       attention_probs_dropout_prob
      _A:  Dict					=       drop_path_rate
      _A:  List[Any]					=       hidden_act
      _A:  Optional[int]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  Union[str, Any]					=       initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _A:  Any					=       int(embed_dim * 2 ** (len(lowerCAmelCase_     ) - 1)     )
      _A:  Tuple					=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,			len(lowerCAmelCase_     ) + 1     )]
      _A			,  _A:  str					=       get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase_			,			out_indices=lowerCAmelCase_			,			stage_names=self.stage_names     )



