






import warnings
from typing import Dict, List, Optional, Tuple

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


snake_case__      :	Union[str, Any]       	=      logging.get_logger(__name__)


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   ["""input_ids""", """attention_mask"""]




  def __init__(     self		,		_UpperCAmelCase="</s>"		,		_UpperCAmelCase="<unk>"		,		_UpperCAmelCase="<pad>"		,		_UpperCAmelCase=125		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> None:
     # Add extra_ids to the special token list
     if extra_ids > 0 and additional_special_tokens is None:
        UpperCamelCase_									=     [f"""<extra_id_{i}>""" for i in range(_UpperCAmelCase )]
     elif extra_ids > 0 and additional_special_tokens is not None:
        # Check that we have the right number of extra_id special tokens
        UpperCamelCase_									=     len(set(filter(lambda _UpperCAmelCase : bool('extra_id' in str(_UpperCAmelCase ) )		,		_UpperCAmelCase ) ) )
        if extra_tokens != extra_ids:
           raise ValueError(
               f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
               ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
               ' extra_ids tokens' )

     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 eos_token
     UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) else unk_token

     super().__init__(
         eos_token=_UpperCAmelCase		,		unk_token=_UpperCAmelCase		,		pad_token=_UpperCAmelCase		,		extra_ids=_UpperCAmelCase		,		additional_special_tokens=_UpperCAmelCase		,		**_UpperCAmelCase		,		)

     UpperCamelCase_									=     extra_ids

     UpperCamelCase_									=     2**8  # utf is 8 bits

     # define special tokens dict
     UpperCamelCase_									=     {
         self.pad_token: 0,
         self.eos_token: 1,
         self.unk_token: 2,
     }
     UpperCamelCase_									=     len(self.special_tokens_encoder )
     UpperCamelCase_									=     len(_UpperCAmelCase )
     for i, token in enumerate(_UpperCAmelCase ):
        UpperCamelCase_									=     self.vocab_size + i - n
     UpperCamelCase_									=     {v: k for k, v in self.special_tokens_encoder.items()}




  @property
  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




  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 )

     # normal case: some special tokens
     if token_ids_a is None:
        return ([0] * len(_UpperCAmelCase )) + [1]
     return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[int]:
     if len(_UpperCAmelCase ) > 0 and token_ids[-1] == self.eos_token_id:
        warnings.warn(
            f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
            ' eos tokens being added.' )
        return token_ids
     else:
        return token_ids + [self.eos_token_id]




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     UpperCamelCase_									=     [self.eos_token_id]

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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     UpperCamelCase_									=     self._add_eos_if_not_present(_UpperCAmelCase )
     if token_ids_a is None:
        return token_ids_a
     else:
        UpperCamelCase_									=     self._add_eos_if_not_present(_UpperCAmelCase )
        return token_ids_a + token_ids_a




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     [chr(_UpperCAmelCase ) for i in text.encode('utf-8' )]
     return tokens




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[str]:
     if token in self.special_tokens_encoder:
        UpperCamelCase_									=     self.special_tokens_encoder[token]
     elif token in self.added_tokens_encoder:
        UpperCamelCase_									=     self.added_tokens_encoder[token]
     elif len(_UpperCAmelCase ) != 1:
        UpperCamelCase_									=     self.unk_token_id
     else:
        UpperCamelCase_									=     ord(_UpperCAmelCase ) + self._num_special_tokens
     return token_id




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> str:
     if index in self.special_tokens_decoder:
        UpperCamelCase_									=     self.special_tokens_decoder[index]
     else:
        UpperCamelCase_									=     chr(index - self._num_special_tokens )
     return token




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Optional[Any]:
     UpperCamelCase_									=     B''
     for token in tokens:
        if token in self.special_tokens_decoder:
           UpperCamelCase_									=     self.special_tokens_decoder[token].encode('utf-8' )
        elif token in self.added_tokens_decoder:
           UpperCamelCase_									=     self.special_tokens_decoder[token].encode('utf-8' )
        elif token in self.special_tokens_encoder:
           UpperCamelCase_									=     token.encode('utf-8' )
        elif token in self.added_tokens_encoder:
           UpperCamelCase_									=     token.encode('utf-8' )
        else:
           UpperCamelCase_									=     bytes([ord(_UpperCAmelCase )] )
        bstring += tok_string
     UpperCamelCase_									=     bstring.decode('utf-8'		,		errors='ignore' )
     return string




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> Tuple[str]:
     return ()














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 )





