
"""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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   tempfile.mkdtemp()

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

		def 							__lowerCamelCase							(   self ,	**_SCREAMING_SNAKE_CASE				):
			return BertTokenizer.from_pretrained(self.tmpdirname ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	**_SCREAMING_SNAKE_CASE				):
			return ViTImageProcessor.from_pretrained(self.tmpdirname ,	**_SCREAMING_SNAKE_CASE				)

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   [np.random.randint(2_55 ,	size=(3, 30, 4_00) ,	dtype=np.uinta				)]

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

			return image_inputs

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.get_tokenizer()
			__lowerCAmelCase    :		str		    =   self.get_image_processor()

			__lowerCAmelCase    :		List[Any]		    =   VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE				)

			processor.save_pretrained(self.tmpdirname				)
			__lowerCAmelCase    :		Optional[int]		    =   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 ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   VisionTextDualEncoderProcessor(
			    tokenizer=self.get_tokenizer() ,	image_processor=self.get_image_processor()				)
			processor.save_pretrained(self.tmpdirname				)

			__lowerCAmelCase    :		Tuple		    =   self.get_tokenizer(bos_token='(BOS)' ,	eos_token='(EOS)'				)
			__lowerCAmelCase    :		Union[str, Any]		    =   self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE ,	padding_value=1.0				)

			__lowerCAmelCase    :		int		    =   VisionTextDualEncoderProcessor.from_pretrained(
			    self.tmpdirname ,	bos_token='(BOS)' ,	eos_token='(EOS)' ,	do_normalize=_SCREAMING_SNAKE_CASE ,	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 ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   self.get_image_processor()
			__lowerCAmelCase    :		str		    =   self.get_tokenizer()

			__lowerCAmelCase    :		Dict		    =   VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   self.prepare_image_inputs()

			__lowerCAmelCase    :		Optional[int]		    =   image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors='np'				)
			__lowerCAmelCase    :		Union[str, Any]		    =   processor(images=_SCREAMING_SNAKE_CASE ,	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				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.get_image_processor()
			__lowerCAmelCase    :		Any		    =   self.get_tokenizer()

			__lowerCAmelCase    :		Any		    =   VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   'lower newer'

			__lowerCAmelCase    :		str		    =   processor(text=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   tokenizer(_SCREAMING_SNAKE_CASE				)

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.get_image_processor()
			__lowerCAmelCase    :		List[str]		    =   self.get_tokenizer()

			__lowerCAmelCase    :		Optional[int]		    =   VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   'lower newer'
			__lowerCAmelCase    :		str		    =   self.prepare_image_inputs()

			__lowerCAmelCase    :		str		    =   processor(text=_SCREAMING_SNAKE_CASE ,	images=_SCREAMING_SNAKE_CASE				)

			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(_SCREAMING_SNAKE_CASE				):
				processor()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.get_image_processor()
			__lowerCAmelCase    :		Optional[Any]		    =   self.get_tokenizer()

			__lowerCAmelCase    :		List[Any]		    =   VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Any		    =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowerCAmelCase    :		Optional[Any]		    =   processor.batch_decode(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		int		    =   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)

			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.get_image_processor()
			__lowerCAmelCase    :		Tuple		    =   self.get_tokenizer()

			__lowerCAmelCase    :		int		    =   VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   'lower newer'
			__lowerCAmelCase    :		List[str]		    =   self.prepare_image_inputs()

			__lowerCAmelCase    :		int		    =   processor(text=_SCREAMING_SNAKE_CASE ,	images=_SCREAMING_SNAKE_CASE				)

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase	):
	if n_term == "":
		return []
	__lowerCAmelCase    :		list		    =   []
	for temp in range(int(_UpperCamelCase	)	):
		series.append(F"1/{temp + 1}" if series else '1'	)
	return series


if __name__ == "__main__":
							lowerCamelCase__           =	input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))