


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase				:  Optional[Any]      					=							logging.get_logger(__name__)


__UpperCamelCase				:  Tuple      					=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
__UpperCamelCase				:  str      					=							{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

__UpperCamelCase				:  Optional[int]      					=							{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}
@lru_cache()
def      snake_case_						( ):
   UpperCAmelCase_							:      List[Any]						=   (
       list(range(ord('''!'''       )			,       ord('''~'''       ) + 1       )       ) + list(range(ord('''¡'''       )			,       ord('''¬'''       ) + 1       )       ) + list(range(ord('''®'''       )			,       ord('''ÿ'''       ) + 1       )       )
   )
   UpperCAmelCase_							:      Tuple						=   bs[:]
   UpperCAmelCase_							:      Tuple						=   0
   for b in range(2**8       ):
      if b not in bs:
         bs.append(__lowercase       )
         cs.append(2**8 + n       )
         n += 1
   UpperCAmelCase_							:      List[Any]						=   [chr(__lowercase       ) for n in cs]
   return dict(zip(__lowercase			,       __lowercase       )       )
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      Optional[Any]						=   set()
   UpperCAmelCase_							:      str						=   word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)       )
      UpperCAmelCase_							:      Dict						=   char
   return pairs





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     List[Any]			= VOCAB_FILES_NAMES
    A_  :     Dict			= PRETRAINED_VOCAB_FILES_MAP
    A_  :     Optional[Any]			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    A_  :     int			= ['input_ids', 'attention_mask']





    def __init__(	self				:   List[str]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Any       ,   __snake_case				:   Optional[Any]="replace"       ,   __snake_case				:   Tuple="<s>"       ,   __snake_case				:   Union[str, Any]="</s>"       ,   __snake_case				:   Optional[Any]="</s>"       ,   __snake_case				:   Any="<s>"       ,   __snake_case				:   str="<unk>"       ,   __snake_case				:   Tuple="<pad>"       ,   __snake_case				:   Any="<mask>"       ,   __snake_case				:   List[str]=False       ,   **__snake_case				:   List[str]       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case	) if isinstance(__snake_case       ,   __snake_case	) else bos_token
       UpperCAmelCase_							:      List[Any]						=   AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case	) if isinstance(__snake_case       ,   __snake_case	) else eos_token
       UpperCAmelCase_							:      str						=   AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case	) if isinstance(__snake_case       ,   __snake_case	) else sep_token
       UpperCAmelCase_							:      str						=   AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case	) if isinstance(__snake_case       ,   __snake_case	) else cls_token
       UpperCAmelCase_							:      str						=   AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case	) if isinstance(__snake_case       ,   __snake_case	) else unk_token
       UpperCAmelCase_							:      Optional[int]						=   AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case	) if isinstance(__snake_case       ,   __snake_case	) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCAmelCase_							:      Any						=   AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case	) if isinstance(__snake_case       ,   __snake_case	) else mask_token

       super().__init__(
           errors=__snake_case       ,   bos_token=__snake_case       ,   eos_token=__snake_case       ,   unk_token=__snake_case       ,   sep_token=__snake_case       ,   cls_token=__snake_case       ,   pad_token=__snake_case       ,   mask_token=__snake_case       ,   add_prefix_space=__snake_case       ,   **__snake_case       ,   )

       with open(__snake_case       ,   encoding='''utf-8'''	) as vocab_handle:
          UpperCAmelCase_							:      Optional[Any]						=   json.load(__snake_case	)
       UpperCAmelCase_							:      str						=   {v: k for k, v in self.encoder.items()}
       UpperCAmelCase_							:      Any						=   errors  # how to handle errors in decoding
       UpperCAmelCase_							:      Optional[int]						=   bytes_to_unicode()
       UpperCAmelCase_							:      Dict						=   {v: k for k, v in self.byte_encoder.items()}
       with open(__snake_case       ,   encoding='''utf-8'''	) as merges_handle:
          UpperCAmelCase_							:      Tuple						=   merges_handle.read().split('''\n'''	)[1:-1]
       UpperCAmelCase_							:      int						=   [tuple(merge.split()	) for merge in bpe_merges]
       UpperCAmelCase_							:      Optional[int]						=   dict(zip(__snake_case       ,   range(len(__snake_case	)	)	)	)
       UpperCAmelCase_							:      Optional[int]						=   {}
       UpperCAmelCase_							:      Tuple						=   add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCAmelCase_							:      str						=   re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''	)





    @property
    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       return len(self.encoder	)





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       return dict(self.encoder       ,   **self.added_tokens_encoder	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Optional[Any]	):





       '''simple docstring'''
       if token in self.cache:
          return self.cache[token]
       UpperCAmelCase_							:      Any						=   tuple(__snake_case	)
       UpperCAmelCase_							:      Union[str, Any]						=   get_pairs(__snake_case	)

       if not pairs:
          return token

       while True:
          UpperCAmelCase_							:      List[Any]						=   min(__snake_case       ,   key=lambda __snake_case	: self.bpe_ranks.get(__snake_case       ,   float('''inf'''	)	)	)
          if bigram not in self.bpe_ranks:
             break
          UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   bigram
          UpperCAmelCase_							:      str						=   []
          UpperCAmelCase_							:      Optional[int]						=   0
          while i < len(__snake_case	):
             try:
                UpperCAmelCase_							:      Optional[int]						=   word.index(__snake_case       ,   __snake_case	)
             except ValueError:
                new_word.extend(word[i:]	)
                break
             else:
                new_word.extend(word[i:j]	)
                UpperCAmelCase_							:      Union[str, Any]						=   j

             if word[i] == first and i < len(__snake_case	) - 1 and word[i + 1] == second:
                new_word.append(first + second	)
                i += 2
             else:
                new_word.append(word[i]	)
                i += 1
          UpperCAmelCase_							:      Optional[int]						=   tuple(__snake_case	)
          UpperCAmelCase_							:      List[str]						=   new_word
          if len(__snake_case	) == 1:
             break
          else:
             UpperCAmelCase_							:      Tuple						=   get_pairs(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   ''' '''.join(__snake_case	)
       UpperCAmelCase_							:      Optional[Any]						=   word
       return word





    def 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   []
       for token in re.findall(self.pat       ,   __snake_case	):
          UpperCAmelCase_							:      Any						=   ''''''.join(
              self.byte_encoder[b] for b in token.encode('''utf-8'''	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case	).split(''' '''	)	)
       return bpe_tokens





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   List[Any]	):





       '''simple docstring'''
       return self.encoder.get(__snake_case       ,   self.encoder.get(self.unk_token	)	)





    def 					_lowerCamelCase (	self				:   int       ,   __snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       return self.decoder.get(__snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   ''''''.join(__snake_case	)
       UpperCAmelCase_							:      str						=   bytearray([self.byte_decoder[c] for c in text]	).decode('''utf-8'''       ,   errors=self.errors	)
       return text





    def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   str       ,   __snake_case				:   Optional[str] = None	):





       '''simple docstring'''
       if not os.path.isdir(__snake_case	):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
          return
       UpperCAmelCase_							:      Union[str, Any]						=   os.path.join(
           __snake_case       ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
       UpperCAmelCase_							:      Dict						=   os.path.join(
           __snake_case       ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']	)

       with open(__snake_case       ,   '''w'''       ,   encoding='''utf-8'''	) as f:
          f.write(json.dumps(self.encoder       ,   indent=2       ,   sort_keys=__snake_case       ,   ensure_ascii=__snake_case	) + '''\n'''	)

       UpperCAmelCase_							:      Dict						=   0
       with open(__snake_case       ,   '''w'''       ,   encoding='''utf-8'''	) as writer:
          writer.write('''#version: 0.2\n'''	)
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,   key=lambda __snake_case	: kv[1]	):
             if index != token_index:
                logger.warning(
                    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                    ''' Please check that the tokenizer is not corrupted!'''	)
                UpperCAmelCase_							:      Any						=   token_index
             writer.write(''' '''.join(__snake_case	) + '''\n'''	)
             index += 1

       return vocab_file, merge_file





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   List[int]       ,   __snake_case				:   Optional[List[int]] = None	):





       '''simple docstring'''
       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCAmelCase_							:      Any						=   [self.cls_token_id]
       UpperCAmelCase_							:      Tuple						=   [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   List[int]       ,   __snake_case				:   Optional[List[int]] = None       ,   __snake_case				:   bool = False	):





       '''simple docstring'''
       if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__snake_case       ,   token_ids_a=__snake_case       ,   already_has_special_tokens=__snake_case	)

       if token_ids_a is None:
          return [1] + ([0] * len(__snake_case	)) + [1]
       return [1] + ([0] * len(__snake_case	)) + [1, 1] + ([0] * len(__snake_case	)) + [1]





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   List[int]       ,   __snake_case				:   Optional[List[int]] = None	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   [self.sep_token_id]
       UpperCAmelCase_							:      Tuple						=   [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 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Tuple       ,   __snake_case				:   List[Any]=False       ,   **__snake_case				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   kwargs.pop('''add_prefix_space'''       ,   self.add_prefix_space	)
       if (is_split_into_words or add_prefix_space) and (len(__snake_case	) > 0 and not text[0].isspace()):
          UpperCAmelCase_							:      Dict						=   ''' ''' + text
       return (text, kwargs)



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  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     str			= ['image_processor', 'tokenizer']
    A_  :     int			= 'LayoutLMv2ImageProcessor'
    A_  :     str			= ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')





    def __init__(	self				:   Union[str, Any]       ,   __snake_case				:   List[Any]=None       ,   __snake_case				:   List[str]=None       ,   **__snake_case				:   Optional[int]	):





       '''simple docstring'''
       if "feature_extractor" in kwargs:
          warnings.warn(
              '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
              ''' instead.'''       ,   __snake_case       ,   )
          UpperCAmelCase_							:      List[Any]						=   kwargs.pop('''feature_extractor'''	)

       UpperCAmelCase_							:      Optional[int]						=   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__(__snake_case       ,   __snake_case	)





    def __call__(	self				:   List[str]       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,   __snake_case				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,   __snake_case				:   Union[List[List[int]], List[List[List[int]]]] = None       ,   __snake_case				:   Optional[Union[List[int], List[List[int]]]] = None       ,   __snake_case				:   bool = True       ,   __snake_case				:   Union[bool, str, PaddingStrategy] = False       ,   __snake_case				:   Union[bool, str, TruncationStrategy] = None       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   int = 0       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = True       ,   __snake_case				:   Optional[Union[str, TensorType]] = None       ,   **__snake_case				:   Optional[int]       ,   ):





       '''simple docstring'''
       # 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
       UpperCAmelCase_							:      Tuple						=   self.image_processor(images=__snake_case       ,   return_tensors=__snake_case	)

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

       UpperCAmelCase_							:      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=__snake_case       ,   add_special_tokens=__snake_case       ,   padding=__snake_case       ,   truncation=__snake_case       ,   max_length=__snake_case       ,   stride=__snake_case       ,   pad_to_multiple_of=__snake_case       ,   return_token_type_ids=__snake_case       ,   return_attention_mask=__snake_case       ,   return_overflowing_tokens=__snake_case       ,   return_special_tokens_mask=__snake_case       ,   return_offsets_mapping=__snake_case       ,   return_length=__snake_case       ,   verbose=__snake_case       ,   return_tensors=__snake_case       ,   **__snake_case       ,   )

       # add pixel values
       UpperCAmelCase_							:      List[str]						=   features.pop('''pixel_values'''	)
       if return_overflowing_tokens is True:
          UpperCAmelCase_							:      Optional[int]						=   self.get_overflowing_images(__snake_case       ,   encoded_inputs['''overflow_to_sample_mapping''']	)
       UpperCAmelCase_							:      List[Any]						=   images

       return encoded_inputs





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[Any]	):





       '''simple docstring'''
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCAmelCase_							:      List[str]						=   []
       for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]	)

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

       return images_with_overflow





    def 					_lowerCamelCase (	self				:   List[Any]       ,   *__snake_case				:   Optional[int]       ,   **__snake_case				:   List[Any]	):





       '''simple docstring'''
       return self.tokenizer.batch_decode(*__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   *__snake_case				:   Optional[Any]       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       return self.tokenizer.decode(*__snake_case       ,   **__snake_case	)





    @property
    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       return ["input_ids", "bbox", "attention_mask", "image"]





    @property
    def 					_lowerCamelCase (	self				:   List[str]	):





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





    @property
    def 					_lowerCamelCase (	self				:   Dict	):





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