import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowerCAmelCase__					=				logging.get_logger(__name__)

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

lowerCAmelCase__					=				{
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

lowerCAmelCase__					=				{
    'openai-gpt': 512,
}






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      text.strip()
       if not text:
              return []
       lowerCAmelCase__  			=      text.split()
       return tokens
class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(    self      :						Optional[Any]    ,					lowercase__      :						str=True    ,					lowercase__      :						Tuple=None    ,					lowercase__      :						List[str]=True    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						Dict=True    ,					):







        '''simple docstring'''



        if never_split is None:
               lowerCAmelCase__  			=      []
        lowerCAmelCase__  			=      do_lower_case
        lowerCAmelCase__  			=      set(lowercase__)
        lowerCAmelCase__  			=      tokenize_chinese_chars
        lowerCAmelCase__  			=      strip_accents
        lowerCAmelCase__  			=      do_split_on_punc




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







        '''simple docstring'''



        lowerCAmelCase__  			=      self.never_split.union(set(lowercase__)) if never_split else self.never_split
        lowerCAmelCase__  			=      self._clean_text(lowercase__)

        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        if self.tokenize_chinese_chars:
               lowerCAmelCase__  			=      self._tokenize_chinese_chars(lowercase__)
        # prevents treating the same character with different unicode codepoints as different characters
        lowerCAmelCase__  			=      unicodedata.normalize('NFC'    ,					lowercase__)
        lowerCAmelCase__  			=      whitespace_tokenize(lowercase__)
        lowerCAmelCase__  			=      []
        for token in orig_tokens:
               if token not in never_split:
                      if self.do_lower_case:
                             lowerCAmelCase__  			=      token.lower()
                             if self.strip_accents is not False:
                                    lowerCAmelCase__  			=      self._run_strip_accents(lowercase__)
                      elif self.strip_accents:
                             lowerCAmelCase__  			=      self._run_strip_accents(lowercase__)
               split_tokens.extend(self._run_split_on_punc(lowercase__    ,					lowercase__))

        lowerCAmelCase__  			=      whitespace_tokenize(' '.join(lowercase__))
        return output_tokens




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      unicodedata.normalize('NFD'    ,					lowercase__)
        lowerCAmelCase__  			=      []
        for char in text:
               lowerCAmelCase__  			=      unicodedata.category(lowercase__)
               if cat == "Mn":
                      continue
               output.append(lowercase__)
        return "".join(lowercase__)




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







        '''simple docstring'''



        if not self.do_split_on_punc or (never_split is not None and text in never_split):
               return [text]
        lowerCAmelCase__  			=      list(lowercase__)
        lowerCAmelCase__  			=      0
        lowerCAmelCase__  			=      True
        lowerCAmelCase__  			=      []
        while i < len(lowercase__):
               lowerCAmelCase__  			=      chars[i]
               if _is_punctuation(lowercase__):
                      output.append([char])
                      lowerCAmelCase__  			=      True
               else:
                      if start_new_word:
                             output.append([])
                      lowerCAmelCase__  			=      False
                      output[-1].append(lowercase__)
               i += 1

        return ["".join(lowercase__) for x in output]




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        for char in text:
               lowerCAmelCase__  			=      ord(lowercase__)
               if self._is_chinese_char(lowercase__):
                      output.append(' ')
                      output.append(lowercase__)
                      output.append(' ')
               else:
                      output.append(lowercase__)
        return "".join(lowercase__)




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						List[Any]):







        '''simple docstring'''



        if (
            (cp >= 0x4E00 and cp <= 0x9FFF)
            or (cp >= 0x3400 and cp <= 0x4DBF)  #
            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
            or (cp >= 0xF900 and cp <= 0xFAFF)
            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
        ):  #
               return True

        return False




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        for char in text:
               lowerCAmelCase__  			=      ord(lowercase__)
               if cp == 0 or cp == 0xFFFD or _is_control(lowercase__):
                      continue
               if _is_whitespace(lowercase__):
                      output.append(' ')
               else:
                      output.append(lowercase__)
        return "".join(lowercase__)






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      set()
       lowerCAmelCase__  			=      word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)			)
              lowerCAmelCase__  			=      char
       return pairs






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      text.replace('—'			,						'-'			)
       lowerCAmelCase__  			=      text.replace('–'			,						'-'			)
       lowerCAmelCase__  			=      text.replace('―'			,						'-'			)
       lowerCAmelCase__  			=      text.replace('…'			,						'...'			)
       lowerCAmelCase__  			=      text.replace('´'			,						'\''			)
       lowerCAmelCase__  			=      re.sub(r'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'			,						r' \1 '			,						lowerCAmelCase__			)
       lowerCAmelCase__  			=      re.sub(r'\s*\n\s*'			,						' \n '			,						lowerCAmelCase__			)
       lowerCAmelCase__  			=      re.sub(r'[^\S\n]+'			,						' '			,						lowerCAmelCase__			)
       return text.strip()



class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      VOCAB_FILES_NAMES
 UpperCAmelCase_							=      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_							=      ['input_ids', 'attention_mask']




 def __init__(    self      :						int    ,					lowercase__      :						Tuple    ,					lowercase__      :						Tuple    ,					lowercase__      :						Tuple="<unk>"    ,					**lowercase__      :						List[str]):







        '''simple docstring'''



        super().__init__(unk_token=lowercase__    ,					**lowercase__)

        try:
               import ftfy
               from spacy.lang.en import English

               lowerCAmelCase__  			=      English()
               lowerCAmelCase__  			=      _nlp.tokenizer
               lowerCAmelCase__  			=      ftfy.fix_text
        except ImportError:
               logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.')
               lowerCAmelCase__  			=      BasicTokenizer(do_lower_case=lowercase__)
               lowerCAmelCase__  			=      None

        with open(lowercase__    ,					encoding='utf-8') as vocab_handle:
               lowerCAmelCase__  			=      json.load(lowercase__)
        lowerCAmelCase__  			=      {v: k for k, v in self.encoder.items()}
        with open(lowercase__    ,					encoding='utf-8') as merges_handle:
               lowerCAmelCase__  			=      merges_handle.read().split('\n')[1:-1]
        lowerCAmelCase__  			=      [tuple(merge.split()) for merge in merges]
        lowerCAmelCase__  			=      dict(zip(lowercase__    ,					range(len(lowercase__))))
        lowerCAmelCase__  			=      {}




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







        '''simple docstring'''



        return True




 @property
 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        return len(self.encoder)




 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        return dict(self.encoder    ,					**self.added_tokens_encoder)




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      tuple(token[:-1]) + (token[-1] + '</w>',)
        if token in self.cache:
               return self.cache[token]
        lowerCAmelCase__  			=      get_pairs(lowercase__)

        if not pairs:
               return token + "</w>"

        while True:
               lowerCAmelCase__  			=      min(lowercase__    ,					key=lambda lowercase__: self.bpe_ranks.get(lowercase__    ,					float('inf')))
               if bigram not in self.bpe_ranks:
                      break
               lowerCAmelCase__			,      lowerCAmelCase__  			=      bigram
               lowerCAmelCase__  			=      []
               lowerCAmelCase__  			=      0
               while i < len(lowercase__):
                      try:
                             lowerCAmelCase__  			=      word.index(lowercase__    ,					lowercase__)
                      except ValueError:
                             new_word.extend(word[i:])
                             break
                      else:
                             new_word.extend(word[i:j])
                             lowerCAmelCase__  			=      j

                      if word[i] == first and i < len(lowercase__) - 1 and word[i + 1] == second:
                             new_word.append(first + second)
                             i += 2
                      else:
                             new_word.append(word[i])
                             i += 1
               lowerCAmelCase__  			=      tuple(lowercase__)
               lowerCAmelCase__  			=      new_word
               if len(lowercase__) == 1:
                      break
               else:
                      lowerCAmelCase__  			=      get_pairs(lowercase__)
        lowerCAmelCase__  			=      ' '.join(lowercase__)
        if word == "\n  </w>":
               lowerCAmelCase__  			=      '\n</w>'
        lowerCAmelCase__  			=      word
        return word




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        if self.fix_text is None:
               # Using BERT's BasicTokenizer
               lowerCAmelCase__  			=      self.nlp.tokenize(lowercase__)
               for token in text:
                      split_tokens.extend(list(self.bpe(lowercase__).split(' ')))
        else:
               # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
               lowerCAmelCase__  			=      self.nlp(text_standardize(self.fix_text(lowercase__)))
               for token in text:
                      split_tokens.extend(list(self.bpe(token.text.lower()).split(' ')))
        return split_tokens




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







        '''simple docstring'''



        return self.encoder.get(lowercase__    ,					self.encoder.get(self.unk_token))




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







        '''simple docstring'''



        return self.decoder.get(lowercase__    ,					self.unk_token)




 def 	__snake_case		(    self      :						Tuple    ,					lowercase__      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      ''.join(lowercase__).replace('</w>'    ,					' ').strip()
        return out_string




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







        '''simple docstring'''



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

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

        lowerCAmelCase__  			=      0
        with open(lowercase__    ,					'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 lowercase__: 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!')
                             lowerCAmelCase__  			=      token_index
                      writer.write(' '.join(lowercase__) + '\n')
                      index += 1

        return vocab_file, merge_file

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_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




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







        '''simple docstring'''



        lowerCAmelCase__  			=      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__    ,					)
               lowerCAmelCase__  			=      kwargs.pop('feature_extractor')

        lowerCAmelCase__  			=      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      :						Any    ,					lowercase__      :						Union[str, 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]    ,					):







        '''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
        lowerCAmelCase__  			=      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__):
                      lowerCAmelCase__  			=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
               lowerCAmelCase__  			=      features['words']

        lowerCAmelCase__  			=      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
        lowerCAmelCase__  			=      features.pop('pixel_values')
        if return_overflowing_tokens is True:
               lowerCAmelCase__  			=      self.get_overflowing_images(lowercase__    ,					encoded_inputs['overflow_to_sample_mapping'])
        lowerCAmelCase__  			=      images

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        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 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



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




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







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



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




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







        '''simple docstring'''



        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 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



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