





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor


@require_torch
@require_vision
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[Any]								=	ViTImageProcessor if is_vision_available() else None







						@property
						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										return self.image_processor_tester.prepare_image_processor_dict()







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			str  						=   (3, 32, 1_28)
										_snake_case :			Dict  						=   tempfile.mkdtemp()

										# fmt: off
										_snake_case :			Dict  						=   ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
										# fmt: on
										_snake_case :			List[str]  						=   dict(zip(lowerCamelCase_ ,				range(len(lowerCamelCase_					)					)					)					)

										_snake_case :			str  						=   os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['vocab_file']					)
										with open(self.vocab_file ,				'w' ,				encoding='utf-8'					) as fp:
														fp.write(json.dumps(lowerCamelCase_					) + '\n'					)

										_snake_case :			Union[str, Any]  						=   {
										    'do_normalize': False,
										    'do_resize': True,
										    'image_processor_type': 'ViTImageProcessor',
										    'resample': 3,
										    'size': {'height': 32, 'width': 1_28},
										}
										_snake_case :			Optional[int]  						=   os.path.join(self.tmpdirname ,				lowerCamelCase_					)
										with open(self.image_processor_file ,				'w' ,				encoding='utf-8'					) as fp:
														json.dump(lowerCamelCase_ ,				lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				**lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										return MgpstrTokenizer.from_pretrained(self.tmpdirname ,				**lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				**lowerCamelCase_			:		Any					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										shutil.rmtree(self.tmpdirname					)







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			str  						=   np.random.randint(2_55 ,				size=(3, 30, 4_00) ,				dtype=np.uinta					)

										_snake_case :			Union[str, Any]  						=   Image.fromarray(np.moveaxis(lowerCamelCase_ ,				0 ,				-1					)					)

										return image_input







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







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

										_snake_case :			str  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)
										processor.save_pretrained(self.tmpdirname					)
										_snake_case :			Optional[Any]  						=   MgpstrProcessor.from_pretrained(self.tmpdirname ,				use_fast=lowerCamelCase_					)

										self.assertEqual(processor.char_tokenizer.get_vocab() ,				tokenizer.get_vocab()					)
										self.assertIsInstance(processor.char_tokenizer ,				lowerCamelCase_					)

										self.assertEqual(processor.image_processor.to_json_string() ,				image_processor.to_json_string()					)
										self.assertIsInstance(processor.image_processor ,				lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







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

										_snake_case :			Any  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)
										processor.save_pretrained(self.tmpdirname					)

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

										_snake_case :			Optional[Any]  						=   MgpstrProcessor.from_pretrained(
										    self.tmpdirname ,				bos_token='(BOS)' ,				eos_token='(EOS)' ,				do_normalize=lowerCamelCase_ ,				padding_value=1.0					)

										self.assertEqual(processor.char_tokenizer.get_vocab() ,				tokenizer_add_kwargs.get_vocab()					)
										self.assertIsInstance(processor.char_tokenizer ,				lowerCamelCase_					)

										self.assertEqual(processor.image_processor.to_json_string() ,				image_processor_add_kwargs.to_json_string()					)
										self.assertIsInstance(processor.image_processor ,				lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.get_image_processor()
										_snake_case :			int  						=   self.get_tokenizer()

										_snake_case :			Any  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   self.prepare_image_inputs()

										_snake_case :			Tuple  						=   image_processor(lowerCamelCase_ ,				return_tensors='np'					)
										_snake_case :			List[str]  						=   processor(images=lowerCamelCase_ ,				return_tensors='np'					)

										for key in input_image_proc.keys():
														self.assertAlmostEqual(input_image_proc[key].sum() ,				input_processor[key].sum() ,				delta=1e-2					)







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







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

										_snake_case :			int  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)

										_snake_case :			str  						=   'test'

										_snake_case :			Any  						=   processor(text=lowerCamelCase_					)

										_snake_case :			Any  						=   tokenizer(lowerCamelCase_					)
										for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key] ,				encoded_processor[key]					)







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Dict  						=   self.get_image_processor()
										_snake_case :			str  						=   self.get_tokenizer()

										_snake_case :			Tuple  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)

										_snake_case :			Optional[Any]  						=   'test'
										_snake_case :			Union[str, Any]  						=   self.prepare_image_inputs()

										_snake_case :			List[Any]  						=   processor(text=lowerCamelCase_ ,				images=lowerCamelCase_					)

										self.assertListEqual(list(inputs.keys()					) ,				['pixel_values', 'labels']					)

										# test if it raises when no input is passed
										with pytest.raises(lowerCamelCase_					):
														processor()







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			str  						=   self.get_image_processor()
										_snake_case :			str  						=   self.get_tokenizer()

										_snake_case :			Tuple  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)

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

										_snake_case :			int  						=   processor.char_decode(lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   tokenizer.batch_decode(lowerCamelCase_					)
										_snake_case :			List[Any]  						=   [seq.replace(' ' ,				''					) for seq in decoded_tok]

										self.assertListEqual(lowerCamelCase_ ,				lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







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

										_snake_case :			Dict  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)

										_snake_case :			List[str]  						=   None
										_snake_case :			Tuple  						=   self.prepare_image_inputs()

										_snake_case :			Dict  						=   processor(text=lowerCamelCase_ ,				images=lowerCamelCase_					)

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







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







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

										_snake_case :			Dict  						=   MgpstrProcessor(tokenizer=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)

										_snake_case :			Dict  						=   torch.randn(1 ,				27 ,				38					)
										_snake_case :			Any  						=   torch.randn(1 ,				27 ,				5_02_57					)
										_snake_case :			Any  						=   torch.randn(1 ,				27 ,				3_05_22					)

										_snake_case :			List[str]  						=   processor.batch_decode([char_input, bpe_input, wp_input]					)

										self.assertListEqual(list(results.keys()					) ,				['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds']					)













import itertools
import math

def       A__(		__lowerCAmelCase ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5	,			int(math.sqrt(__lowerCAmelCase ) + 1 )	,			6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def       A__(		):
				_snake_case :			Optional[Any]  						=   2
				while True:
								if is_prime(__lowerCAmelCase ):
												yield num
								num += 1

def       A__(		__lowerCAmelCase = 1_00_01 ):
				return next(itertools.islice(prime_generator()	,			nth - 1	,			__lowerCAmelCase ) )


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






