



'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Tuple										=  ['image_processor', 'tokenizer']
 __lowerCamelCase   :	Tuple										=  'LayoutLMv2ImageProcessor'
 __lowerCamelCase   :	Optional[int]										=  ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



 def __init__(self			,       A=None			,       A=None			,       **A	)   ->					int:


      """simple docstring"""



      _a     =  None
      if "feature_extractor" in kwargs:
           warnings.warn(
               '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
               ''' instead.'''			,       A			,       )
           _a     =  kwargs.pop('''feature_extractor'''	)

      _a     =  image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError('''You need to specify an `image_processor`.'''	)
      if tokenizer is None:
           raise ValueError('''You need to specify a `tokenizer`.'''	)

      super().__init__(A			,       A	)



 def __call__(self			,       A			,       A = None			,       A = None			,       A = None			,       A = None			,       A = True			,       A = False			,       A = False			,       A = None			,       A = 0			,       A = None			,       A = None			,       A = None			,       A = False			,       A = False			,       A = False			,       A = False			,       A = True			,       A = None			,       **A			,       )   ->					BatchEncoding:


      """simple docstring"""



      if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''	)

      if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''	)

      if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''	)

      # first, apply the image processor
      _a     =  self.image_processor(images=A			,       return_tensors=A	)

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(A			,       A	):
                _a     =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
           _a     =  features['''words''']

      _a     =  self.tokenizer(
          text=text if text is not None else features['''words''']			,       text_pair=text_pair if text_pair is not None else None			,       boxes=boxes if boxes is not None else features['''boxes''']			,       word_labels=A			,       add_special_tokens=A			,       padding=A			,       truncation=A			,       max_length=A			,       stride=A			,       pad_to_multiple_of=A			,       return_token_type_ids=A			,       return_attention_mask=A			,       return_overflowing_tokens=A			,       return_special_tokens_mask=A			,       return_offsets_mapping=A			,       return_length=A			,       verbose=A			,       return_tensors=A			,       **A			,       )

      # add pixel values
      _a     =  features.pop('''pixel_values'''	)
      if return_overflowing_tokens is True:
           _a     =  self.get_overflowing_images(A			,       encoded_inputs['''overflow_to_sample_mapping''']	)
      _a     =  images

      return encoded_inputs



 def  a__  (self			,       A			,       A	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  []
      for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]	)

      if len(A	) != len(A	):
           raise ValueError(
               '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
               f''' {len(A	)} and {len(A	)}'''	)

      return images_with_overflow



 def  a__  (self			,       *A			,       **A	)   ->					int:


      """simple docstring"""



      return self.tokenizer.batch_decode(*A			,       **A	)



 def  a__  (self			,       *A			,       **A	)   ->					Optional[int]:


      """simple docstring"""



      return self.tokenizer.decode(*A			,       **A	)



 @property
 def  a__  (self	)   ->					Any:


      """simple docstring"""



      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



 @property
 def  a__  (self	)   ->					Any:


      """simple docstring"""



      warnings.warn(
          '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       A			,       )
      return self.image_processor_class



 @property
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       A			,       )
      return self.image_processor








'''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_mbartaa import MBartaaTokenizer
else:
  lowercase_			=  None


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase_			=  {
    "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"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_			=  {
    "facebook/mbart-large-50-one-to-many-mmt": 1_024,
}

# fmt: off
lowercase_			=  ["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 __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	List[str]										=	VOCAB_FILES_NAMES
 __lowerCamelCase   :	int										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase   :	Optional[int]										=	PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase   :	Any										=	['input_ids', 'attention_mask']
 __lowerCamelCase   :	Optional[Any]										=	MBartaaTokenizer

 __lowerCamelCase   :	List[int]										=	[]
 __lowerCamelCase   :	List[int]										=	[]



 def __init__(self			,       A=None			,       A=None			,       A=None			,       A=None			,       A="</s>"			,       A="</s>"			,       A="<s>"			,       A="<unk>"			,       A="<pad>"			,       A="<mask>"			,       **A			,       )   ->					List[str]:


      """simple docstring"""



      _a     =  AddedToken(A			,       lstrip=A			,       rstrip=A	) if isinstance(A			,       A	) else mask_token

      _a     =  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__(
          A			,       src_lang=A			,       tgt_lang=A			,       tokenizer_file=A			,       eos_token=A			,       sep_token=A			,       cls_token=A			,       unk_token=A			,       pad_token=A			,       mask_token=A			,       **A			,       )

      _a     =  vocab_file
      _a     =  False if not self.vocab_file else True

      _a     =  {
          lang_code: self.convert_tokens_to_ids(A	) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

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



 @property
 def  a__  (self	)   ->					str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang	)



 def  a__  (self			,       A			,       A = 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  a__  (self			,       A			,       A = "en_XX"			,       A = None			,       A = "ro_RO"			,       **A			,       )   ->					BatchEncoding:


      """simple docstring"""



      _a     =  src_lang
      _a     =  tgt_lang
      return super().prepare_seqaseq_batch(A			,       A			,       **A	)



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


      """simple docstring"""



      return self.set_src_lang_special_tokens(self.src_lang	)



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


      """simple docstring"""



      return self.set_tgt_lang_special_tokens(self.tgt_lang	)



 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  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  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  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  a__  (self			,       A			,       A			,       A			,       A			,       **A	)   ->					Optional[int]:


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



 def  a__  (self			,       A			,       A = None	)   ->					Tuple[str]:


      """simple docstring"""



      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
      _a     =  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,)



