





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 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[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": 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		,	_lowerCAmelCase							)
         with open(self.image_processor_file		,	"w"		,	encoding="utf-8"							) as fp:
            json.dump(_lowerCAmelCase		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    str		,	**_lowerCAmelCase:    List[Any]							)    ->     Dict:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	**_lowerCAmelCase:    Tuple							)    ->     Optional[Any]:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         shutil.rmtree(self.tmpdirname							)



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =[np.random.randint(255		,	size=(3, 30, 400)		,	dtype=np.uinta							)]

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

         return image_inputs



      def 						lowerCAmelCase__     (						self:    int							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizer()
         UpperCAmelCase_           =self.get_image_processor()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

         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		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Tuple:
         '''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=_lowerCAmelCase		,	padding_value=1.0							)

         UpperCAmelCase_           =VisionTextDualEncoderProcessor.from_pretrained(
             self.tmpdirname		,	bos_token="(BOS)"		,	eos_token="(EOS)"		,	do_normalize=_lowerCAmelCase		,	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		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

         UpperCAmelCase_           =self.prepare_image_inputs()

         UpperCAmelCase_           =image_processor(_lowerCAmelCase		,	return_tensors="np"							)
         UpperCAmelCase_           =processor(images=_lowerCAmelCase		,	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 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

         UpperCAmelCase_           ="lower newer"

         UpperCAmelCase_           =processor(text=_lowerCAmelCase							)

         UpperCAmelCase_           =tokenizer(_lowerCAmelCase							)

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



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

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

         UpperCAmelCase_           =processor(text=_lowerCAmelCase		,	images=_lowerCAmelCase							)

         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(_lowerCAmelCase							):
            processor()



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

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

         UpperCAmelCase_           =processor.batch_decode(_lowerCAmelCase							)
         UpperCAmelCase_           =tokenizer.batch_decode(_lowerCAmelCase							)

         self.assertListEqual(_lowerCAmelCase		,	_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()

         UpperCAmelCase_           =VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase							)

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

         UpperCAmelCase_           =processor(text=_lowerCAmelCase		,	images=_lowerCAmelCase							)

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












"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return abs(lowerCamelCase       ) if a == 0 else greatest_common_divisor(b % a,						lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    y, x % y
				return abs(lowerCamelCase       )





def 				lowercase__		(					):
				try:
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    input('Enter two integers separated by comma (,): '       ).split(','       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    int(nums[0]       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    int(nums[1]       )
								print(
								    f"""greatest_common_divisor({num_a}, {num_a}) = """
								    f"""{greatest_common_divisor(lowerCamelCase,						lowerCamelCase       )}"""       )
								print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase,						lowerCamelCase       )}"""       )
				except (IndexError, UnboundLocalError, ValueError):
								print('Wrong input'       )


if __name__ == "__main__":
			main()

