



'''simple docstring'''


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  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def  A       (  self      :					Any ):


       """simple docstring"""







       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': 1_8, 'width': 1_8},
           '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  A       (  self      :					str  ,						**UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       return BertTokenizer.from_pretrained(self.tmpdirname  ,						**UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any]  ,						**UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       return ViTImageProcessor.from_pretrained(self.tmpdirname  ,						**UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       shutil.rmtree(self.tmpdirname )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  [np.random.randint(2_5_5  ,						size=(3, 3_0, 4_0_0)  ,						dtype=np.uinta )]

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

       return image_inputs






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       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  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       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  A       (  self      :					str ):


       """simple docstring"""







       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  A       (  self      :					List[str] ):


       """simple docstring"""







       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  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       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  A       (  self      :					List[str] ):


       """simple docstring"""







       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  A       (  self      :					List[Any] ):


       """simple docstring"""







       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 )






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "IBertForMaskedLM",
       "IBertForMultipleChoice",
       "IBertForQuestionAnswering",
       "IBertForSequenceClassification",
       "IBertForTokenClassification",
       "IBertModel",
       "IBertPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase	:       Union[str, Any]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

