

'''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 					A__      (      unittest.TestCase			):



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      tempfile.mkdtemp()

           # fmt: off
           A_						=      ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
           # fmt: on
           A_						=      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]       )       )

           A_						=      {
               """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],
           }
           A_						=      os.path.join(self.tmpdirname		,				UpperCamelCase__       )
           with open(self.image_processor_file		,				"""w"""		,				encoding="""utf-8"""       ) as fp:
                  json.dump(UpperCamelCase__		,				UpperCamelCase__       )



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           return BertTokenizer.from_pretrained(self.tmpdirname		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           return ViTImageProcessor.from_pretrained(self.tmpdirname		,				**UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           shutil.rmtree(self.tmpdirname       )



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      [np.random.randint(255		,				size=(3, 30, 400)		,				dtype=np.uinta       )]

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

           return image_inputs



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      self.get_tokenizer()
           A_						=      self.get_image_processor()

           A_						=      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__		,				image_processor=UpperCamelCase__       )

           processor.save_pretrained(self.tmpdirname       )
           A_						=      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 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           A_						=      VisionTextDualEncoderProcessor(
               tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()       )
           processor.save_pretrained(self.tmpdirname       )

           A_						=      self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)"""       )
           A_						=      self.get_image_processor(do_normalize=UpperCamelCase__		,				padding_value=1.0       )

           A_						=      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 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.get_image_processor()
           A_						=      self.get_tokenizer()

           A_						=      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__		,				image_processor=UpperCamelCase__       )

           A_						=      self.prepare_image_inputs()

           A_						=      image_processor(UpperCamelCase__		,				return_tensors="""np"""       )
           A_						=      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 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      self.get_image_processor()
           A_						=      self.get_tokenizer()

           A_						=      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__		,				image_processor=UpperCamelCase__       )

           A_						=      """lower newer"""

           A_						=      processor(text=UpperCamelCase__       )

           A_						=      tokenizer(UpperCamelCase__       )

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



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.get_image_processor()
           A_						=      self.get_tokenizer()

           A_						=      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__		,				image_processor=UpperCamelCase__       )

           A_						=      """lower newer"""
           A_						=      self.prepare_image_inputs()

           A_						=      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 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           A_						=      self.get_image_processor()
           A_						=      self.get_tokenizer()

           A_						=      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__		,				image_processor=UpperCamelCase__       )

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

           A_						=      processor.batch_decode(UpperCamelCase__       )
           A_						=      tokenizer.batch_decode(UpperCamelCase__       )

           self.assertListEqual(UpperCamelCase__		,				UpperCamelCase__       )







    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.get_image_processor()
           A_						=      self.get_tokenizer()

           A_						=      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__		,				image_processor=UpperCamelCase__       )

           A_						=      """lower newer"""
           A_						=      self.prepare_image_inputs()

           A_						=      processor(text=UpperCamelCase__		,				images=UpperCamelCase__       )

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



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       assert (
           isinstance(UpperCAmelCase__,       UpperCAmelCase__				) and number_of_steps > 0
       ), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
       if number_of_steps == 1:
              return 1
       A_					,			A_						=      1, 1
       for _ in range(number_of_steps - 1				):
              A_					,			A_						=      current + previous, current
       return current


if __name__ == "__main__":
      import doctest

      doctest.testmod()
