



'''simple docstring'''

import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import FlavaImageProcessor, FlavaProcessor
							from transformers.models.flava.image_processing_flava import (
							    FLAVA_CODEBOOK_MEAN,
							    FLAVA_CODEBOOK_STD,
							    FLAVA_IMAGE_MEAN,
							    FLAVA_IMAGE_STD,
							)

@require_vision
class 			__magic_name__		(					unittest.TestCase     ):







							def 							__lowercase    (							self      : Optional[int]	):
									_a  :   Tuple				 =							tempfile.mkdtemp()

									# fmt: off
									_a  :   Optional[Any]				 =							['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
									# fmt: on
									_a  :   int				 =							os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']	)

									with open(self.vocab_file			,'w'			,encoding='utf-8'	) as fp:
											fp.write(''.join([x + '\n' for x in vocab_tokens]	)	)

									_a  :   Union[str, Any]				 =							{
									    'image_mean': FLAVA_IMAGE_MEAN,
									    'image_std': FLAVA_IMAGE_STD,
									    'do_normalize': True,
									    'do_resize': True,
									    'size': 224,
									    'do_center_crop': True,
									    'crop_size': 224,
									    'input_size_patches': 14,
									    'total_mask_patches': 75,
									    'mask_group_max_patches': None,
									    'mask_group_min_patches': 16,
									    'mask_group_min_aspect_ratio': 0.3,
									    'mask_group_max_aspect_ratio': None,
									    'codebook_do_resize': True,
									    'codebook_size': 112,
									    'codebook_do_center_crop': True,
									    'codebook_crop_size': 112,
									    'codebook_do_map_pixels': True,
									    'codebook_do_normalize': True,
									    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
									    'codebook_image_std': FLAVA_CODEBOOK_STD,
									}

									_a  :   List[Any]				 =							os.path.join(self.tmpdirname			,_UpperCAmelCase	)
									with open(self.image_processor_file			,'w'			,encoding='utf-8'	) as fp:
											json.dump(_UpperCAmelCase			,_UpperCAmelCase	)







							def 							__lowercase    (							self      : Union[str, Any]			,**_UpperCAmelCase      : Union[str, Any]	):
									return BertTokenizer.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Optional[Any]	):
									return BertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : str			,**_UpperCAmelCase      : Optional[Any]	):
									return FlavaImageProcessor.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : Optional[Any]	):
									shutil.rmtree(self.tmpdirname	)







							def 							__lowercase    (							self      : str	):
									_a  :   Any				 =							[np.random.randint(255			,size=(3, 30, 400)			,dtype=np.uinta	)]

									_a  :   Union[str, Any]				 =							[Image.fromarray(np.moveaxis(_UpperCAmelCase			,0			,-1	)	) for x in image_inputs]

									return image_inputs







							def 							__lowercase    (							self      : Optional[int]	):
									_a  :   List[Any]				 =							self.get_tokenizer()
									_a  :   List[str]				 =							self.get_rust_tokenizer()
									_a  :   Tuple				 =							self.get_image_processor()

									_a  :   int				 =							FlavaProcessor(tokenizer=_UpperCAmelCase			,image_processor=_UpperCAmelCase	)
									processor_slow.save_pretrained(self.tmpdirname	)
									_a  :   List[Any]				 =							FlavaProcessor.from_pretrained(self.tmpdirname			,use_fast=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							FlavaProcessor(tokenizer=_UpperCAmelCase			,image_processor=_UpperCAmelCase	)
									processor_fast.save_pretrained(self.tmpdirname	)
									_a  :   List[str]				 =							FlavaProcessor.from_pretrained(self.tmpdirname	)

									self.assertEqual(processor_slow.tokenizer.get_vocab()			,tokenizer_slow.get_vocab()	)
									self.assertEqual(processor_fast.tokenizer.get_vocab()			,tokenizer_fast.get_vocab()	)
									self.assertEqual(tokenizer_slow.get_vocab()			,tokenizer_fast.get_vocab()	)
									self.assertIsInstance(processor_slow.tokenizer			,_UpperCAmelCase	)
									self.assertIsInstance(processor_fast.tokenizer			,_UpperCAmelCase	)

									self.assertEqual(processor_slow.image_processor.to_json_string()			,image_processor.to_json_string()	)
									self.assertEqual(processor_fast.image_processor.to_json_string()			,image_processor.to_json_string()	)
									self.assertIsInstance(processor_slow.image_processor			,_UpperCAmelCase	)
									self.assertIsInstance(processor_fast.image_processor			,_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[Any]	):
									_a  :   List[str]				 =							FlavaProcessor(tokenizer=self.get_tokenizer()			,image_processor=self.get_image_processor()	)
									processor.save_pretrained(self.tmpdirname	)

									_a  :   str				 =							self.get_tokenizer(bos_token='(BOS)'			,eos_token='(EOS)'	)
									_a  :   Optional[int]				 =							self.get_image_processor(do_normalize=_UpperCAmelCase			,padding_value=1.0	)

									_a  :   Any				 =							FlavaProcessor.from_pretrained(
									    self.tmpdirname			,bos_token='(BOS)'			,eos_token='(EOS)'			,do_normalize=_UpperCAmelCase			,padding_value=1.0	)

									self.assertEqual(processor.tokenizer.get_vocab()			,tokenizer_add_kwargs.get_vocab()	)
									self.assertIsInstance(processor.tokenizer			,_UpperCAmelCase	)

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







							def 							__lowercase    (							self      : Optional[Any]	):
									_a  :   Any				 =							self.get_image_processor()
									_a  :   List[Any]				 =							self.get_tokenizer()

									_a  :   Tuple				 =							FlavaProcessor(tokenizer=_UpperCAmelCase			,image_processor=_UpperCAmelCase	)

									_a  :   Optional[Any]				 =							self.prepare_image_inputs()

									_a  :   Tuple				 =							image_processor(_UpperCAmelCase			,return_tensors='np'	)
									_a  :   List[Any]				 =							processor(images=_UpperCAmelCase			,return_tensors='np'	)

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

									# With rest of the args
									random.seed(1234	)
									_a  :   Optional[int]				 =							image_processor(
									    _UpperCAmelCase			,return_image_mask=_UpperCAmelCase			,return_codebook_pixels=_UpperCAmelCase			,return_tensors='np'	)
									random.seed(1234	)
									_a  :   int				 =							processor(
									    images=_UpperCAmelCase			,return_image_mask=_UpperCAmelCase			,return_codebook_pixels=_UpperCAmelCase			,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      : Tuple	):
									_a  :   Optional[Any]				 =							self.get_image_processor()
									_a  :   List[str]				 =							self.get_tokenizer()

									_a  :   str				 =							FlavaProcessor(tokenizer=_UpperCAmelCase			,image_processor=_UpperCAmelCase	)

									_a  :   List[Any]				 =							'lower newer'

									_a  :   Union[str, Any]				 =							processor(text=_UpperCAmelCase	)

									_a  :   List[str]				 =							tokenizer(_UpperCAmelCase	)

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







							def 							__lowercase    (							self      : str	):
									_a  :   Union[str, Any]				 =							self.get_image_processor()
									_a  :   Union[str, Any]				 =							self.get_tokenizer()

									_a  :   Union[str, Any]				 =							FlavaProcessor(tokenizer=_UpperCAmelCase			,image_processor=_UpperCAmelCase	)

									_a  :   Any				 =							'lower newer'
									_a  :   str				 =							self.prepare_image_inputs()

									_a  :   str				 =							processor(text=_UpperCAmelCase			,images=_UpperCAmelCase	)

									self.assertListEqual(list(inputs.keys()	)			,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']	)

									# add extra args
									_a  :   str				 =							processor(text=_UpperCAmelCase			,images=_UpperCAmelCase			,return_codebook_pixels=_UpperCAmelCase			,return_image_mask=_UpperCAmelCase	)

									self.assertListEqual(
									    list(inputs.keys()	)			,[
									        'input_ids',
									        'token_type_ids',
									        'attention_mask',
									        'pixel_values',
									        'codebook_pixel_values',
									        'bool_masked_pos',
									    ]			,)

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







							def 							__lowercase    (							self      : int	):
									_a  :   Dict				 =							self.get_image_processor()
									_a  :   int				 =							self.get_tokenizer()

									_a  :   List[str]				 =							FlavaProcessor(tokenizer=_UpperCAmelCase			,image_processor=_UpperCAmelCase	)

									_a  :   Optional[int]				 =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									_a  :   str				 =							processor.batch_decode(_UpperCAmelCase	)
									_a  :   List[str]				 =							tokenizer.batch_decode(_UpperCAmelCase	)

									self.assertListEqual(_UpperCAmelCase			,_UpperCAmelCase	)







							def 							__lowercase    (							self      : Dict	):
									_a  :   Any				 =							self.get_image_processor()
									_a  :   str				 =							self.get_tokenizer()

									_a  :   Optional[Any]				 =							FlavaProcessor(tokenizer=_UpperCAmelCase			,image_processor=_UpperCAmelCase	)

									_a  :   Optional[Any]				 =							'lower newer'
									_a  :   Dict				 =							self.prepare_image_inputs()

									_a  :   Optional[Any]				 =							processor(text=_UpperCAmelCase			,images=_UpperCAmelCase	)

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









'''simple docstring'''

def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   tuple[int, int]:
		# base case
		if len(lowerCAmelCase_						) == 1:
				return a[0], a[0]
		else:
				# split A into half.
				_a  :   Union[str, Any]				 =							a[: len(lowerCAmelCase_						) // 2]
				_a  :   List[Any]				 =							a[len(lowerCAmelCase_						) // 2 :]

				# 2 sub problems, 1/2 of original size.
				_a ,      _a  :   Any				 =							max_difference(lowerCAmelCase_						)
				_a ,      _a  :   Any				 =							max_difference(lowerCAmelCase_						)

				# get min of first and max of second
				# linear time
				_a  :   Union[str, Any]				 =							min(lowerCAmelCase_						)
				_a  :   Optional[int]				 =							max(lowerCAmelCase_						)

				# 3 cases, either (small1, big1),
				# (min_first, max_second), (small2, big2)
				# constant comparisons
				if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
						return smalla, biga
				elif biga - smalla > max_second - min_first:
						return smalla, biga
				else:
						return min_first, max_second


if __name__ == "__main__":
							import doctest

							doctest.testmod()




