


"""simple docstring"""






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


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       '''▁'''

__snake_case		=       {'''vocab_file''': '''sentencepiece.bpe.model'''}

__snake_case		=       {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__snake_case		=       {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
__snake_case		=       ['''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''']


class     __lowerCamelCase  (					_UpperCAmelCase				):

     '''simple docstring'''
     A_       :	Tuple    =		VOCAB_FILES_NAMES
     A_       :	Optional[int]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_       :	Union[str, Any]    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	Optional[Any]    =		['input_ids', 'attention_mask']

     A_       :	List[int]    =		[]
     A_       :	List[int]    =		[]


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase="<s>"    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="<s>"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase="<mask>"    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase    ,				)       ->							Any:
          _a    	=				AddedToken(__UpperCamelCase    ,				lstrip=__UpperCamelCase    ,				rstrip=__UpperCamelCase							) if isinstance(__UpperCamelCase    ,				__UpperCamelCase							) else mask_token

          _a    	=				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=__UpperCamelCase    ,				eos_token=__UpperCamelCase    ,				unk_token=__UpperCamelCase    ,				sep_token=__UpperCamelCase    ,				cls_token=__UpperCamelCase    ,				pad_token=__UpperCamelCase    ,				mask_token=__UpperCamelCase    ,				tokenizer_file=__UpperCamelCase    ,				src_lang=__UpperCamelCase    ,				tgt_lang=__UpperCamelCase    ,				additional_special_tokens=__UpperCamelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCamelCase    ,				)

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(str(__UpperCamelCase							)							)
          _a    	=				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    	=				{'''<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    	=				1

          _a    	=				len(self.sp_model							)
          _a    	=				{
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCamelCase							)
          }
          _a    	=				{v: k for k, v in self.lang_code_to_id.items()}
          _a    	=				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    	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
          _a    	=				list(self.lang_code_to_id.keys()							)

          if additional_special_tokens is not None:
               # Only add those special tokens if they are not already there.
               self._additional_special_tokens.extend(
                   [t for t in additional_special_tokens if t not in self._additional_special_tokens]							)

          _a    	=				src_lang if src_lang is not None else '''en_XX'''
          _a    	=				self.lang_code_to_id[self._src_lang]
          _a    	=				tgt_lang
          self.set_src_lang_special_tokens(self._src_lang							)


     def __getstate__(      self							)       ->							Dict:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          _a    	=				self.sp_model.serialized_model_proto()
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							str:
          _a    	=				d

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

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token


     @property
     def 							_UpperCAmelCase      (      self							)       ->							str:
          return self._src_lang


     @src_lang.setter
     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							None:
          _a    	=				new_src_lang
          self.set_src_lang_special_tokens(self._src_lang							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:






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

          _a    	=				[1] * len(self.prefix_tokens							)
          _a    	=				[1] * len(self.suffix_tokens							)
          if token_ids_a is None:
               return prefix_ones + ([0] * len(__UpperCamelCase							)) + suffix_ones
          return prefix_ones + ([0] * len(__UpperCamelCase							)) + ([0] * len(__UpperCamelCase							)) + suffix_ones


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:






          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							int:






          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    	=				src_lang
          _a    	=				self(__UpperCamelCase    ,				add_special_tokens=__UpperCamelCase    ,				return_tensors=__UpperCamelCase    ,				**__UpperCamelCase							)
          _a    	=				self.convert_tokens_to_ids(__UpperCamelCase							)
          _a    	=				tgt_lang_id
          return inputs


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				{self.convert_ids_to_tokens(__UpperCamelCase							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          return self.sp_model.encode(__UpperCamelCase    ,				out_type=__UpperCamelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Union[str, Any]:






          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          _a    	=				self.sp_model.PieceToId(__UpperCamelCase							)

          # 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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Tuple:






          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				''''''.join(__UpperCamelCase							).replace(__UpperCamelCase    ,				''' '''							).strip()
          return out_string


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:






          if not os.path.isdir(__UpperCamelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a    	=				os.path.join(
              __UpperCamelCase    ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

          if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCamelCase							) and os.path.isfile(self.vocab_file							):
               copyfile(self.vocab_file    ,				__UpperCamelCase							)
          elif not os.path.isfile(self.vocab_file							):
               with open(__UpperCamelCase    ,				'''wb'''							) as fi:
                    _a    	=				self.sp_model.serialized_model_proto()
                    fi.write(__UpperCamelCase							)

          return (out_vocab_file,)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = "en_XX"    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = "ro_RO"    ,				**__UpperCAmelCase    ,				)       ->							BatchEncoding:
          _a    	=				src_lang
          _a    	=				tgt_lang
          return super().prepare_seqaseq_batch(__UpperCamelCase    ,				__UpperCamelCase    ,				**__UpperCamelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          return self.set_src_lang_special_tokens(self.src_lang							)


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          return self.set_tgt_lang_special_tokens(self.tgt_lang							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							None:
          _a    	=				self.lang_code_to_id[src_lang]
          _a    	=				[]
          _a    	=				[self.eos_token_id, self.cur_lang_code]





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							None:
          _a    	=				self.lang_code_to_id[lang]
          _a    	=				[]
          _a    	=				[self.eos_token_id, self.cur_lang_code]



"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case		=       {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)