



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 							lowerCAmelCase__  (	unittest.TestCase						):







					def  _lowercase			(       self      :     int):
											A__	:							Union[str, Any]						    = tempfile.mkdtemp()

											# fmt: off
											A__	:							Union[str, Any]						    = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
											# fmt: on
											A__	:							Tuple						    = 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__	:							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],
											}
											A__	:							Union[str, Any]						    = os.path.join(self.tmpdirname			,		_A)
											with open(self.image_processor_file			,		"w"			,		encoding="utf-8") as fp:
																	json.dump(_A			,		_A)







					def  _lowercase			(       self      :     Optional[Any]			,		**_A      :     Dict):
											return BertTokenizer.from_pretrained(self.tmpdirname			,		**_A)







					def  _lowercase			(       self      :     Optional[int]			,		**_A      :     Tuple):
											return ViTImageProcessor.from_pretrained(self.tmpdirname			,		**_A)







					def  _lowercase			(       self      :     Tuple):
											shutil.rmtree(self.tmpdirname)







					def  _lowercase			(       self      :     List[str]):
											A__	:							List[str]						    = [np.random.randint(255			,		size=(3, 30, 400)			,		dtype=np.uinta)]

											A__	:							int						    = [Image.fromarray(np.moveaxis(_A			,		0			,		-1)) for x in image_inputs]

											return image_inputs







					def  _lowercase			(       self      :     Tuple):
											A__	:							Optional[int]						    = self.get_tokenizer()
											A__	:							Optional[int]						    = self.get_image_processor()

											A__	:							Tuple						    = VisionTextDualEncoderProcessor(tokenizer=_A			,		image_processor=_A)

											processor.save_pretrained(self.tmpdirname)
											A__	:							Union[str, Any]						    = 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			,		_A)







					def  _lowercase			(       self      :     Tuple):
											A__	:							List[str]						    = VisionTextDualEncoderProcessor(
											    tokenizer=self.get_tokenizer()			,		image_processor=self.get_image_processor())
											processor.save_pretrained(self.tmpdirname)

											A__	:							Optional[int]						    = self.get_tokenizer(bos_token="(BOS)"			,		eos_token="(EOS)")
											A__	:							Union[str, Any]						    = self.get_image_processor(do_normalize=_A			,		padding_value=1.0)

											A__	:							List[Any]						    = VisionTextDualEncoderProcessor.from_pretrained(
											    self.tmpdirname			,		bos_token="(BOS)"			,		eos_token="(EOS)"			,		do_normalize=_A			,		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			,		_A)







					def  _lowercase			(       self      :     str):
											A__	:							Optional[int]						    = self.get_image_processor()
											A__	:							Any						    = self.get_tokenizer()

											A__	:							Union[str, Any]						    = VisionTextDualEncoderProcessor(tokenizer=_A			,		image_processor=_A)

											A__	:							Dict						    = self.prepare_image_inputs()

											A__	:							Union[str, Any]						    = image_processor(_A			,		return_tensors="np")
											A__	:							Tuple						    = processor(images=_A			,		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  _lowercase			(       self      :     Any):
											A__	:							Optional[int]						    = self.get_image_processor()
											A__	:							List[str]						    = self.get_tokenizer()

											A__	:							Union[str, Any]						    = VisionTextDualEncoderProcessor(tokenizer=_A			,		image_processor=_A)

											A__	:							Dict						    = "lower newer"

											A__	:							Union[str, Any]						    = processor(text=_A)

											A__	:							Union[str, Any]						    = tokenizer(_A)

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







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							Union[str, Any]						    = self.get_image_processor()
											A__	:							Tuple						    = self.get_tokenizer()

											A__	:							List[Any]						    = VisionTextDualEncoderProcessor(tokenizer=_A			,		image_processor=_A)

											A__	:							Optional[int]						    = "lower newer"
											A__	:							Optional[int]						    = self.prepare_image_inputs()

											A__	:							Union[str, Any]						    = processor(text=_A			,		images=_A)

											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(_A):
																	processor()







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

											A__	:							Dict						    = VisionTextDualEncoderProcessor(tokenizer=_A			,		image_processor=_A)

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

											A__	:							Union[str, Any]						    = processor.batch_decode(_A)
											A__	:							Any						    = tokenizer.batch_decode(_A)

											self.assertListEqual(_A			,		_A)







					def  _lowercase			(       self      :     Any):
											A__	:							Optional[int]						    = self.get_image_processor()
											A__	:							List[Any]						    = self.get_tokenizer()

											A__	:							Any						    = VisionTextDualEncoderProcessor(tokenizer=_A			,		image_processor=_A)

											A__	:							List[Any]						    = "lower newer"
											A__	:							Optional[Any]						    = self.prepare_image_inputs()

											A__	:							Union[str, Any]						    = processor(text=_A			,		images=_A)

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




def 	snake_case__       (					__lowercase       )		->		int:







						"""simple docstring"""

						if divisor % 5 == 0 or divisor % 2 == 0:
												return 0
						A__	:							Tuple						    = 1
						A__	:							Union[str, Any]						    = 1
						while repunit:
												A__	:							Union[str, Any]						    = (1_0 * repunit + 1) % divisor
												repunit_index += 1
						return repunit_index
def 	snake_case__       (					__lowercase = 1_0_0_0_0_0_0       )		->		int:







						"""simple docstring"""

						A__	:							Dict						    = limit - 1
						if divisor % 2 == 0:
												divisor += 1
						while least_divisible_repunit(__lowercase       ) <= limit:
												divisor += 2
						return divisor


if __name__ == "__main__":
		print(f"""{solution() = }""")