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

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCamelCase													=		{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

UpperCamelCase													=		{'''Helsinki-NLP/opus-mt-en-de''': 512}
UpperCamelCase													=		{}



class 						snake_case_ (	__A ):
   __A			:	List[str]        =       VOCAB_FILES_NAMES
   __A			:	List[str]        =       PRETRAINED_VOCAB_FILES_MAP
   __A			:	Tuple        =       PRETRAINED_INIT_CONFIGURATION
   __A			:	Optional[int]        =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A			:	List[str]        =       ["input_ids", "attention_mask"]
   __A			:	Optional[int]        =       re.compile(">>.+<<" )  # type: re.Pattern






   def __init__(   self       :  List[str]			,		lowercase_       :  int			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  int=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Union[str, Any]="<unk>"			,		lowercase_       :  Tuple="</s>"			,		lowercase_       :  List[Any]="<pad>"			,		lowercase_       :  Optional[int]=5_12			,		lowercase_       :  Optional[Dict[str, Any]] = None			,		lowercase_       :  Any=False			,		**lowercase_       :  Any			,		)      ->		None:
       lowercase__				:     str					      =       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
           source_lang=lowercase_			,		target_lang=lowercase_			,		unk_token=lowercase_			,		eos_token=lowercase_			,		pad_token=lowercase_			,		model_max_length=lowercase_			,		sp_model_kwargs=self.sp_model_kwargs			,		target_vocab_file=lowercase_			,		separate_vocabs=lowercase_			,		**lowercase_			,		)
       assert Path(lowercase_						).exists(), F'''cannot find spm source {source_spm}'''

       lowercase__				:     Dict					      =       separate_vocabs
       lowercase__				:     Dict					      =       load_json(lowercase_						)
       if self.unk_token not in self.encoder:
           raise KeyError("<unk> token must be in vocab"						)
       assert self.pad_token in self.encoder

       if separate_vocabs:
           lowercase__				:     Union[str, Any]					      =       load_json(lowercase_						)
           lowercase__				:     Optional[Any]					      =       {v: k for k, v in self.target_encoder.items()}
           lowercase__				:     str					      =       []
       else:
           lowercase__				:     Union[str, Any]					      =       {v: k for k, v in self.encoder.items()}
           lowercase__				:     list					      =       [k for k in self.encoder if k.startswith(">>"						) and k.endswith("<<"						)]

       lowercase__				:     str					      =       source_lang
       lowercase__				:     Any					      =       target_lang
       lowercase__				:     Any					      =       [source_spm, target_spm]

       # load SentencePiece model for pre-processing
       lowercase__				:     List[Any]					      =       load_spm(lowercase_			,		self.sp_model_kwargs						)
       lowercase__				:     Optional[Any]					      =       load_spm(lowercase_			,		self.sp_model_kwargs						)
       lowercase__				:     Tuple					      =       self.spm_source
       lowercase__				:     Optional[Any]					      =       self.encoder

       # Multilingual target side: default to using first supported language code.

       self._setup_normalizer()






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Optional[Any]:
       try:
           from sacremoses import MosesPunctNormalizer

           lowercase__				:     List[str]					      =       MosesPunctNormalizer(self.source_lang						).normalize
       except (ImportError, FileNotFoundError):
           warnings.warn("Recommended: pip install sacremoses."						)
           lowercase__				:     List[Any]					      =       lambda lowercase_						: x






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  str						)      ->		str:
       return self.punc_normalizer(lowercase_						) if x else ""






   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  str						)      ->		List[str]:
       return self.current_encoder.get(lowercase_			,		self.current_encoder[self.unk_token]						)






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  str						)      ->		Dict:
       lowercase__				:     Optional[int]					      =       self.language_code_re.match(lowercase_						)
       lowercase__				:     list					      =       [match.group(0						)] if match else []
       return code, self.language_code_re.sub(""			,		lowercase_						)






   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  str						)      ->		List[str]:
       lowercase__      ,     lowercase__				:     Optional[int]					      =       self.remove_language_code(lowercase_						)
       lowercase__				:     Any					      =       self.current_spm.encode(lowercase_			,		out_type=lowercase_						)
       return code + pieces






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  int						)      ->		str:
       return self.decoder.get(lowercase_			,		self.unk_token						)






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  List[Any]			,		**lowercase_       :  Any						)      ->		List[str]:
       return super().batch_decode(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  int			,		**lowercase_       :  str						)      ->		str:
       return super().decode(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  List[str]						)      ->		str:
       lowercase__				:     int					      =       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
       lowercase__				:     List[str]					      =       []
       lowercase__				:     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 += sp_model.decode_pieces(lowercase_						) + token + " "
               lowercase__				:     Optional[int]					      =       []
           else:
               current_sub_tokens.append(lowercase_						)
       out_string += sp_model.decode_pieces(lowercase_						)
       return out_string.strip()






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Union[str, Any]			,		lowercase_       :  Tuple=None						)      ->		List[int]:
       if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		int:
       lowercase__				:     List[str]					      =       self.spm_source
       lowercase__				:     Optional[int]					      =       self.encoder






   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		List[Any]:
       lowercase__				:     Optional[Any]					      =       self.spm_target
       if self.separate_vocabs:
           lowercase__				:     Optional[Any]					      =       self.target_encoder






   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		int:
       return len(self.encoder						)






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  str			,		lowercase_       :  Optional[str] = None						)      ->		Tuple[str]:
       if not os.path.isdir(lowercase_						):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
           return
       lowercase__				:     int					      =       []

       if self.separate_vocabs:
           lowercase__				:     int					      =       os.path.join(
               lowercase_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]			,		)
           lowercase__				:     Union[str, Any]					      =       os.path.join(
               lowercase_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]			,		)
           save_json(self.encoder			,		lowercase_						)
           save_json(self.target_encoder			,		lowercase_						)
           saved_files.append(lowercase_						)
           saved_files.append(lowercase_						)
       else:
           lowercase__				:     Any					      =       os.path.join(
               lowercase_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]						)
           save_json(self.encoder			,		lowercase_						)
           saved_files.append(lowercase_						)

       for spm_save_filename, spm_orig_path, spm_model in zip(
           [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]			,		self.spm_files			,		[self.spm_source, self.spm_target]			,		):
           lowercase__				:     Optional[Any]					      =       os.path.join(
               lowercase_			,		(filename_prefix + "-" if filename_prefix else "") + spm_save_filename						)
           if os.path.abspath(lowercase_						) != os.path.abspath(lowercase_						) and os.path.isfile(lowercase_						):
               copyfile(lowercase_			,		lowercase_						)
               saved_files.append(lowercase_						)
           elif not os.path.isfile(lowercase_						):
               with open(lowercase_			,		"wb"						) as fi:
                   lowercase__				:     Optional[Any]					      =       spm_model.serialized_model_proto()
                   fi.write(lowercase_						)
               saved_files.append(lowercase_						)

       return tuple(lowercase_						)






   def    __UpperCamelCase  (   self       :  int						)      ->		Dict:
       return self.get_src_vocab()






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		str:
       return dict(self.encoder			,		**self.added_tokens_encoder						)






   def    __UpperCamelCase  (   self       :  Any						)      ->		Tuple:
       return dict(self.target_encoder			,		**self.added_tokens_decoder						)






   def __getstate__(   self       :  Any						)      ->		Dict:
       lowercase__				:     Tuple					      =       self.__dict__.copy()
       state.update(
           {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}						)
       return state






   def __setstate__(   self       :  Tuple			,		lowercase_       :  Dict						)      ->		None:
       lowercase__				:     int					      =       d

       # for backward compatibility
       if not hasattr(self			,		"sp_model_kwargs"						):
           lowercase__				:     List[str]					      =       {}

       lowercase__      ,     lowercase__				:     Optional[Any]					      =       (load_spm(lowercase_			,		self.sp_model_kwargs						) for f in self.spm_files)
       lowercase__				:     Tuple					      =       self.spm_source
       self._setup_normalizer()






   def    __UpperCamelCase  (   self       :  List[str]			,		*lowercase_       :  str			,		**lowercase_       :  Optional[int]						)      ->		List[str]:
       return 1






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Any						)      ->		Dict:
       lowercase__				:     Dict					      =       set(self.all_special_ids						)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
       return [1 if x in all_special_ids else 0 for x in seq]







   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  Optional[List] = None			,		lowercase_       :  bool = False						)      ->		List[int]:
       if already_has_special_tokens:
           return self._special_token_mask(lowercase_						)
       elif token_ids_a is None:
           return self._special_token_mask(lowercase_						) + [1]
       else:
           return self._special_token_mask(token_ids_a + token_ids_a						) + [1]



def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: Dict[str, Any]):
    lowercase__				:     Union[str, Any]					      =       sentencepiece.SentencePieceProcessor(**_lowerCamelCase)
    spm.Load(_lowerCamelCase)
    return spm

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: str):
    with open(_lowerCamelCase	,			"w") as f:
        json.dump(_lowerCamelCase	,			_lowerCamelCase	,			indent=2)

def     lowercase_       (    _lowerCamelCase							: str):
    with open(_lowerCamelCase	,			"r") as f:
        return json.load(_lowerCamelCase)




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 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[Any]					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       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__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		BatchEncoding:
       # verify input
       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
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_			,		lowercase_						):
               lowercase__				:     List[Any]					      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__				:     Union[str, Any]					      =       features["words"]

       lowercase__				:     List[str]					      =       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=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__				:     str					      =       []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

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

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






   @property
   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



