



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				):
						lowerCamelCase__ :      Optional[Any]								=		tempfile.mkdtemp()

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

						lowerCamelCase__ :      Union[str, Any]								=		{
						    """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],
						}
						lowerCamelCase__ :      Union[str, Any]								=		os.path.join(self.tmpdirname							,    __magic_name__				)
						with open(self.image_processor_file							,    """w"""							,    encoding="""utf-8"""				) as fp:
									json.dump(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self							,    **__magic_name__				):
						return BertTokenizer.from_pretrained(self.tmpdirname							,    **__magic_name__				)





			def        _snake_case    (self							,    **__magic_name__				):
						return ViTImageProcessor.from_pretrained(self.tmpdirname							,    **__magic_name__				)





			def        _snake_case    (self				):
						shutil.rmtree(self.tmpdirname				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		[np.random.randint(255							,    size=(3, 30, 400)							,    dtype=np.uinta				)]

						lowerCamelCase__ :      Optional[Any]								=		[Image.fromarray(np.moveaxis(__magic_name__							,    0							,    -1				)				) for x in image_inputs]

						return image_inputs





			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		self.get_tokenizer()
						lowerCamelCase__ :      Optional[int]								=		self.get_image_processor()

						lowerCamelCase__ :      int								=		VisionTextDualEncoderProcessor(tokenizer=__magic_name__							,    image_processor=__magic_name__				)

						processor.save_pretrained(self.tmpdirname				)
						lowerCamelCase__ :      Tuple								=		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							,    __magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()							,    image_processor=self.get_image_processor()				)
						processor.save_pretrained(self.tmpdirname				)

						lowerCamelCase__ :      Optional[Any]								=		self.get_tokenizer(bos_token="""(BOS)"""							,    eos_token="""(EOS)"""				)
						lowerCamelCase__ :      Any								=		self.get_image_processor(do_normalize=__magic_name__							,    padding_value=1.0				)

						lowerCamelCase__ :      str								=		VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname							,    bos_token="""(BOS)"""							,    eos_token="""(EOS)"""							,    do_normalize=__magic_name__							,    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							,    __magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.get_image_processor()
						lowerCamelCase__ :      List[Any]								=		self.get_tokenizer()

						lowerCamelCase__ :      Tuple								=		VisionTextDualEncoderProcessor(tokenizer=__magic_name__							,    image_processor=__magic_name__				)

						lowerCamelCase__ :      int								=		self.prepare_image_inputs()

						lowerCamelCase__ :      Tuple								=		image_processor(__magic_name__							,    return_tensors="""np"""				)
						lowerCamelCase__ :      Tuple								=		processor(images=__magic_name__							,    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				):
						lowerCamelCase__ :      Tuple								=		self.get_image_processor()
						lowerCamelCase__ :      List[Any]								=		self.get_tokenizer()

						lowerCamelCase__ :      List[str]								=		VisionTextDualEncoderProcessor(tokenizer=__magic_name__							,    image_processor=__magic_name__				)

						lowerCamelCase__ :      List[str]								=		"""lower newer"""

						lowerCamelCase__ :      Tuple								=		processor(text=__magic_name__				)

						lowerCamelCase__ :      List[Any]								=		tokenizer(__magic_name__				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.get_image_processor()
						lowerCamelCase__ :      List[Any]								=		self.get_tokenizer()

						lowerCamelCase__ :      List[Any]								=		VisionTextDualEncoderProcessor(tokenizer=__magic_name__							,    image_processor=__magic_name__				)

						lowerCamelCase__ :      Dict								=		"""lower newer"""
						lowerCamelCase__ :      List[str]								=		self.prepare_image_inputs()

						lowerCamelCase__ :      Union[str, Any]								=		processor(text=__magic_name__							,    images=__magic_name__				)

						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(__magic_name__				):
									processor()





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.get_image_processor()
						lowerCamelCase__ :      Optional[int]								=		self.get_tokenizer()

						lowerCamelCase__ :      int								=		VisionTextDualEncoderProcessor(tokenizer=__magic_name__							,    image_processor=__magic_name__				)

						lowerCamelCase__ :      Tuple								=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCamelCase__ :      Optional[int]								=		processor.batch_decode(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		tokenizer.batch_decode(__magic_name__				)

						self.assertListEqual(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		self.get_image_processor()
						lowerCamelCase__ :      List[str]								=		self.get_tokenizer()

						lowerCamelCase__ :      Tuple								=		VisionTextDualEncoderProcessor(tokenizer=__magic_name__							,    image_processor=__magic_name__				)

						lowerCamelCase__ :      Union[str, Any]								=		"""lower newer"""
						lowerCamelCase__ :      List[str]								=		self.prepare_image_inputs()

						lowerCamelCase__ :      Tuple								=		processor(text=__magic_name__							,    images=__magic_name__				)

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








def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			while b:
						lowerCamelCase__							,lowerCamelCase__ :      int								=		b, a % b
			return a






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			return a if b == 0 else euclidean_gcd_recursive(UpperCamelCase      ,			a % b )






def      _A				()  ->str:



			'''simple docstring'''







			print(f"euclidean_gcd(3, 5) = {euclidean_gcd(3      ,			5 )}" )
			print(f"euclidean_gcd(5, 3) = {euclidean_gcd(5      ,			3 )}" )
			print(f"euclidean_gcd(1, 3) = {euclidean_gcd(1      ,			3 )}" )
			print(f"euclidean_gcd(3, 6) = {euclidean_gcd(3      ,			6 )}" )
			print(f"euclidean_gcd(6, 3) = {euclidean_gcd(6      ,			3 )}" )

			print(f"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3      ,			5 )}" )
			print(f"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5      ,			3 )}" )
			print(f"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1      ,			3 )}" )
			print(f"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3      ,			6 )}" )
			print(f"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6      ,			3 )}" )


if __name__ == "__main__":
				main()



