






import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


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

snake_case__      :	str       	=      {"""vocab_file""": """spiece.model"""}

snake_case__      :	List[Any]       	=      {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

snake_case__      :	Optional[int]       	=      {
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   VOCAB_FILES_NAMES
  A_						=   PRETRAINED_VOCAB_FILES_MAP
  A_						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_						=   ["""input_ids""", """attention_mask"""]
  A_						=   []




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase="<unk>"		,		_UpperCAmelCase="<s>"		,		_UpperCAmelCase="</s>"		,		_UpperCAmelCase="<pad>"		,		_UpperCAmelCase="[SEP]"		,		_UpperCAmelCase="[MASK]"		,		_UpperCAmelCase="[CLS]"		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)			-> None:
     UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) else bos_token
     UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) else eos_token
     UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) else unk_token
     UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) else pad_token
     UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) else cls_token
     UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) else sep_token

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

     UpperCamelCase_									=     {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=_UpperCAmelCase		,		eos_token=_UpperCAmelCase		,		unk_token=_UpperCAmelCase		,		pad_token=_UpperCAmelCase		,		sep_token=_UpperCAmelCase		,		mask_token=_UpperCAmelCase		,		cls_token=_UpperCAmelCase		,		sp_model_kwargs=self.sp_model_kwargs		,		**_UpperCAmelCase		,		)

     UpperCamelCase_									=     vocab_file

     UpperCamelCase_									=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(_UpperCAmelCase )




  @property
  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.sp_model.get_piece_size()




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab




  def __getstate__(     self )			-> Any:
     UpperCamelCase_									=     self.__dict__.copy()
     UpperCamelCase_									=     None
     return state




  def __setstate__(     self		,		_UpperCAmelCase )			-> Optional[Any]:
     UpperCamelCase_									=     d

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

     UpperCamelCase_									=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(self.vocab_file )




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




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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     self.sp_model.IdToPiece(_UpperCAmelCase )
     return token




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     []
     UpperCamelCase_									=     ''
     UpperCamelCase_									=     False
     for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
           if not prev_is_special:
              out_string += " "
           out_string += self.sp_model.decode(_UpperCAmelCase ) + token
           UpperCamelCase_									=     True
           UpperCamelCase_									=     []
        else:
           current_sub_tokens.append(_UpperCAmelCase )
           UpperCamelCase_									=     False
     out_string += self.sp_model.decode(_UpperCAmelCase )
     return out_string.strip()




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		**_UpperCAmelCase		,		)			-> str:
     UpperCamelCase_									=     kwargs.pop('use_source_tokenizer'		,		_UpperCAmelCase )

     UpperCamelCase_									=     self.convert_ids_to_tokens(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase )

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     UpperCamelCase_									=     []
     UpperCamelCase_									=     []
     for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
           continue
        if token in self.added_tokens_encoder:
           if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase ) )
              UpperCamelCase_									=     []
           sub_texts.append(_UpperCAmelCase )
        else:
           current_sub_text.append(_UpperCAmelCase )
     if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase ) )

     # Mimic the behavior of the Rust tokenizer:
     # No space before [MASK] and [SEP]
     if spaces_between_special_tokens:
        UpperCamelCase_									=     re.sub(R' (\[(MASK|SEP)\])'		,		R'\1'		,		' '.join(_UpperCAmelCase ) )
     else:
        UpperCamelCase_									=     ''.join(_UpperCAmelCase )

     UpperCamelCase_									=     (
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
        UpperCamelCase_									=     self.clean_up_tokenization(_UpperCAmelCase )
        return clean_text
     else:
        return text




  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
     UpperCamelCase_									=     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:
           UpperCamelCase_									=     self.sp_model.serialized_model_proto()
           fi.write(_UpperCAmelCase )

     return (out_vocab_file,)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     UpperCamelCase_									=     [self.cls_token_id]
     UpperCamelCase_									=     [self.sep_token_id]
     return cls + token_ids_a + sep + token_ids_a + sep




  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 )

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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     UpperCamelCase_									=     [self.sep_token_id]
     UpperCamelCase_									=     [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 ) * [0] + len(token_ids_a + sep ) * [1]














import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     tempfile.mkdtemp()

     # fmt: off
     UpperCamelCase_									=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['vocab_file'] )
     with open(self.vocab_file		,		'w'		,		encoding='utf-8' ) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

     UpperCamelCase_									=     {
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		_UpperCAmelCase )
     with open(self.image_processor_file		,		'w'		,		encoding='utf-8' ) as fp:
        json.dump(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[int]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     [np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta )]

     UpperCamelCase_									=     [Image.fromarray(np.moveaxis(_UpperCAmelCase		,		0		,		-1 ) ) for x in image_inputs]

     return image_inputs




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_tokenizer()
     UpperCamelCase_									=     self.get_image_processor()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     processor.save_pretrained(self.tmpdirname )
     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     UpperCamelCase_									=     self.get_tokenizer(bos_token='(BOS)'		,		eos_token='(EOS)' )
     UpperCamelCase_									=     self.get_image_processor(do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname		,		bos_token='(BOS)'		,		eos_token='(EOS)'		,		do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     image_processor(_UpperCAmelCase		,		return_tensors='np' )
     UpperCamelCase_									=     processor(images=_UpperCAmelCase		,		return_tensors='np' )

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()		,		input_processor[key].sum()		,		delta=1e-2 )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'

     UpperCamelCase_									=     processor(text=_UpperCAmelCase )

     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,		encoded_processor[key] )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )

     # test if it raises when no input is passed
     with self.assertRaises(_UpperCAmelCase ):
        processor()




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCamelCase_									=     processor.batch_decode(_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.batch_decode(_UpperCAmelCase )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		processor.model_input_names )





